Implementing a DataDriven Computer Security
Defense
Roger A. Grimes
Microsoft IT Information Security and Risk Management
March 2017
For the latest update of this paper check http://aka.ms/datadrivendefense
Foreword
In today’s environment, information security executives face a challenge of protecting
company assets by optimally aligning defenses with an ever increasing number of threats
and risks. Often, organizations have considerable investments in protection without
using a risk-based approach to prioritizing investments. This approach leads to
ineffective security controls and an inefficient use of resources. Information security
organizations collect a tremendous amount of data about IT environments. For some
organizations, activities occurring on those IT infrastructures exceed more than ten
billion events on a daily basis. In other words, considerable information is available about
the environments we manage and it’s that data that can help us make informed
decisions.
In support of these challenges, considerable improvement in rigor and process is
necessary to inform and make better business decisions.
This whitepaper draws upon hundreds of engagements with Microsoft clients, as well as
internal security operations, culminating in a framework for dramatically improving
operational security posture. The methods discussed are based largely on Microsoft’s
Information Security and Risk Management (ISRM) organization’s experience, which is
accountable for protecting the assets of Microsoft IT, other Microsoft Business Divisions,
and advising a selected number of Microsoft’s Global 500 customers.
The framework described utilizes a data-driven approach to optimize investment
allocation for security defenses and significantly improve the management of risk for an
organization.
Joseph Lindstrom – former Sr. Director, Microsoft Information Security & Risk
Management
Implementing a Data-Driven Computer Security Defense
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Acknowledgements
Author
Contributors
Roger A. Grimes
Kurt Tonti
Reviewers
Mark Simos
Shahbaz Yusuf
Adam Shostack
Ashish Popli
Joe Faulhaber
Joseph Lindstrom
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Implementing a Data-Driven Computer Security Defense
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Revision Sheet
Change Record
Date
Author
Version
Change Reference
9-27-15
Roger A. Grimes
1.0
First public release
10-8-15
Roger A. Grimes
1.1
Moderate content additions to paper since 1.0
release
10-27-15
Roger A. Grimes
1.2
Added contributions by Joe Faulhaber
11-19-15
Roger A. Grimes
1.3
Minor updates
2-1-16
Roger A. Grimes
1.4
Minor updates and figure updates
2-8-16
Roger A. Grimes
1.45
Minor updates
2-11-16
Roger A. Grimes
1.46
Minor updates
7-25-16
Roger A. Grimes
1.47
Minor updates; updated SIR chart
11-12-16
Roger A. Grimes
2.0
Moderate updates – mostly clarifications and
some additional information
3-16-17
Roger A. Grimes
2.02
Minor updates - clarified impact of threat is more
important than pure numbers of threats
Implementing a Data-Driven Computer Security Defense
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Contents
Foreword ................................................................................................................ 2
Acknowledgements............................................................................................... 3
Executive summary ............................................................................................... 6
The problem with most computer security defenses: Inefficient alignment of risk
and defense............................................................................................................ 7
A common example of misalignment .................................................................................. 8
The top exploit method changes over time ..................................................................... 10
How did it get this way? .......................................................................................................... 12
Sheer number of threats...................................................................................................... 12
Poor ranking of threats ........................................................................................................ 14
Poor detection capabilities and metrics ........................................................................ 17
Poor communication of top threats................................................................................ 19
Focus on Compliance Requirements ....................Error! Bookmark not defined.
What a data-driven computer security defense looks like ......................................... 20
Part of a Comprehensive Defense ................................................................................... 20
Implementing a data-driven computer security defense ............................... 22
Collect better and localized threat intelligence .............................................................. 23
Microsoft Threat Intelligence Resources ....................................................................... 25
Rank risk appropriately ............................................................................................................ 26
Developing attack scenarios .............................................................................................. 28
Calculating Causation Risk.................................................................................................. 30
Using inventory to calculate causation risk .................................................................. 31
Risk Assessment Is Risky ...................................................................................................... 32
Create a communications and mitigation plan ............................................................... 33
Define and collect metrics....................................................................................................... 34
Define and select defenses ranked by risk ....................................................................... 35
Review and improve the defense plan as needed ......................................................... 37
Putting It All Together .............................................................................................................. 38
FAQ ....................................................................................................................... 40
Related reading ................................................................................................... 43
Implementing a Data-Driven Computer Security Defense
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Executive summary
Many companies do not appropriately align computer security defenses with the threats
that pose the greatest risk to their environment. The growing number of ever-evolving
threats has made it more difficult for organizations to identify and appropriately rank the
risk of the most critical threats. This leads to inefficient and often ineffective application
of security controls.
The implementation weaknesses described in this white paper are common to most
organizations, and point to limitations in traditional modeling of and response to threats
to computer security. Most of the problems occur due to inaccurate risk ranking, poor
communications, and uncoordinated, slow, ineffectual responses.
This paper proposes a framework that can help organizations more efficiently allocate
defensive resources against the most likely threats to reduce risk. This new data-driven
computer security defense plan approach results in:
•
Collecting better and more localized threat intelligence
•
More accurate threat risk ranking
•
Better understanding of computer defenses as compared to biggest threats
•
Defining and collecting new, more relevant metrics
•
Selecting and implementing defenses ranked by risk reduction and making them
accountable for the solutions they purport to provide
•
More timely responses to newly emerging threats
•
A communications plan which efficiently conveys the greatest threats to everyone in
the organization
The key goal of an implemented data-driven computer security defense is to more
directly align and funnel mitigations against the root-causes of the most successful
threats. The outcome is a more efficient appropriation of defensive resources with
measurably lower risk. The measure of success of a data- and relevancy-driven computer
security defense is fewer high-risk compromises and faster responses to successful
compromises.
If such a defense is implemented correctly, defenders will focus on the most critical
initial-compromise exploits that harm their company the most in a given time period. It
will efficiently reduce risk the fastest of any defense strategy, and appropriately align
resources. And when the next attack vector cycle begins, the company can recognize it
earlier, respond more quickly, and reduce damage faster.
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The problem with most computer
security defenses: Inefficient
alignment of risk and defense
Imagine two armies, good and bad, engaged in a long-term fight on a field of battle. The
bad army has successfully managed to compromise the good army’s defenses, again and
again, by focusing most of its troops on the good army’s left flank. Surprisingly, instead
of rushing to put reinforcements on its left flank, the good army keeps its troops evenly
spread, or perhaps decides to put more of its defenders
on the right flank. Or worse: it decides to pull troops
Most companies from the left flank to man anti-aircraft weapons in the
center because of rumors that the enemy might onedo not correctly
day attack from the sky. Despite continued reports of
align defensive
successful attacks on the left flank, the defending army
resources against continues to amass troops nearly everywhere else, and
wonders why it is losing the battle.
the threats that
are most
successful or
most likely to
compromise their
environments.
No army would survive long ignoring successful
attacks. Yet this scenario describes how most
companies defend the security of their computer
systems. Today, most enterprises do not correctly align
their resources—money, labor, and time—against the
threats that pose the greatest risk to their computer
systems and have been most successful at attacking
them.
Computer security defense has always been about
identifying threats, determining risk, and then applying mitigations to minimize those
risks. Unfortunately, the complexity of numerous threats and their constantly evolving
nature has led many defenders to respond too slowly or to focus on the wrong threats.
This misalignment is due to several factors, including that enterprise defenders often fail
to:
•
Identify in a clear and timely way all the localized threat scenarios they face
•
Focus on how initial compromises happen (i.e. root cause) versus what happens
afterward
•
Understand the comparative relative risks of different threats
Implementing a Data-Driven Computer Security Defense
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•
Broadly communicate threats ranked by risk to all stakeholders, including senior
management
•
Efficiently coordinate agreed-upon responses to risk
•
Measure the success of deployed defensive resources against the threats they were
defined to mitigate
All these implementation weaknesses lead to a misalignment of computer security
defenses against the highest risk threats.
A common example of misalignment
Currently, the primary way computers are initially exploited is through unpatched
software, with just a few programs responsible for the majority of those exploits. In the
recent past, vulnerabilities in unpatched operating systems (OS) were the most likely
targets of malicious hackers, but as OS vendors gained success in helping customers
patch their OS software, malware writers have turned to targeting applications—in
particular, popular Internet browser–related software running on multiple platforms.
In some years, according to the Cisco 2014 Annual Security Report, a single unpatched
program was identified as causing the vast majority—ranging as high as 91 percent—of
successful web attacks. Although which software is most exploited changes over time—
and the exact percentages reported vary depending on the survey used to assess it—it
seems fairly reasonable to conclude that unpatched software, in general, and a few
programs in a particular time period, are responsible for most successful web exploits.
This seems unlikely to change in the near term.
Another very common successful attack vector is social engineering, either through fake
phish emails, rogue web links, or other forms of social engineering. Many of the world’s
most damaging enterprise attacks have begun with a social engineering attack, which led
from elevated credential compromise to malicious access of critical resources.
In today’s threat environment, it is clear that unpatched software and social engineering
threats are among the two biggest threats to most organizations. Unless defenders can
demonstrate that their environments are less susceptible to these risks than those of
their peers, it seems reasonable to conclude that most companies should significantly
improve patching, particularly of the most exploited programs, and work hard to
decrease the potential success of social engineering attempts as their primary defense
strategies.
Unfortunately, however, most companies don’t do this. Instead of patching the most
problematic programs, companies don't differentiate between patching those
applications and every other program. Often the highest risk programs are not patched
Implementing a Data-Driven Computer Security Defense
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at all, or at significantly lower rates due to a variety of factors (this fact is why those very
programs are so highly targeted by attackers). So although a company may report a
fairly high level of overall patching compliance, it often includes very low levels of
patching compliance on the programs most likely to be exploited. It's possible, then, that
a company reporting 95 percent overall patch compliance is likely not showing (or even
aware of) the real risk caused by a few missing patches.
In most enterprises, patch management is left up to one or two employees who are
rewarded based on their overall patching rate (or perhaps their patching rate for lower
risk operating system patches), rather than how well they patch the highest-risk
programs.
Indeed, patch management employees are often prevented from patching the very
programs which would provide the most protection. Despite the continued presence of
high risk attacks, most companies still tolerate large percentages of unpatched Internetrelated software for various, and often substantial, reasons. These include that critical
applications may break if the software is patched, and a lack of real authority given to
the employees charged with patching software. It is well accepted in the computer
security industry that it's easier to get fired by causing a substantial operational
interruption than it is by deciding to accept residual risk by leaving high-risk programs
unpatched.
But many other necessary defenses do not have a significant downside and are unlikely
to cause significant operational interruption. For instance, effective end-user education
designed to lower the risk of social engineering attacks is woefully under-utilized in most
organizations. Even though social engineering is one of the most common types of
attack most employees are lucky to get any training to defeat these attacks, or may get
15-30 minutes on an annual basis. This is not enough in light of the risk the education
could mitigate. Often times this training is many years old and does not focus on the
most likely attacks which employees could face.
Companies which conduct initial social engineering tests against their own employees
are often surprised to find that these tests are successful against a large percentage of
them. Even when the companies know that a significant portion of their employee base
can be fooled by social engineering attacks, rarely does the company then commit the
necessary resources to significantly reduce the risk.
The lack of substantial, focused, end-user training tends to be a factor of the perceived
difficultly in delivering the correct education in enough quantities to substantially impact
the organization. Some defenders will even state that there are some individuals in their
organization which can never be trained well enough, and that those few individuals will
undermine the overall value of the entire educational campaign. In this case, the goal of
Implementing a Data-Driven Computer Security Defense
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perceived perfection undermines improved education for the majority, which could
significantly reduce risk.
So while most companies could most efficiently decrease the highest computer security
risks by better patching a few high-risk programs and providing appropriate amounts of
anti-social engineering training, few do so. Instead, most companies tend to focus on
other defense activities—such as two-factor authentication, intrusion detection systems,
and firewalls. While good to implement, they do not directly address the biggest initial
compromise root cause vectors. If the biggest problems are not corrected, then other
defenses will most likely be inadequate at stopping the highest risk attacks.
In data-driven computer defense (like this paper is proposing), the fact that unpatched
software and social engineering are the biggest threats would be confirmed against the
enterprise’s actual experience. If confirmed, this would be communicated to all
stakeholders, including senior management. Senior management would assign the
necessary resources—and give them the authority—to combat the top threats. A special
task force project team might be created to look at the overall problems, discuss
mitigations, do testing, and reduce the risk posed by unpatched programs and social
engineering. If every stakeholder understood the large threat posed by a few unpatched
programs and social engineering, it's doubtful that they would stand by and do nothing
(or give software-patching and education minor focus).
The top exploit method changes over time
In general, the most popular programs are the ones that are attacked the most. The most
successful and popular attack methods also change over time, as illustrated below.
General timeline of the most popular malware threats
In the late 1980s, it was boot viruses. In the 1990s and the first decade of this century, it
was malicious email attachments, which eventually morphed into email with embedded
links carrying toxic payloads. After 2010, most malicious compromises have been due to
exploited websites and social engineering. The most popular and successful exploits
always change over time.
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This is due to a myriad of reasons. Sometimes the underlying technology that they
exploit requires is removed (e.g. floppy disk boot viruses), although it is often because
defensive mitigation finally appropriately address the underlying root cause to the point
that the threat becomes minimize. An example of the latter is macro viruses. As macro
viruses became more of a nuisance, vendors began preventing untrusted macros from
executing by default.
Attackers will move onto more successful exploits as the ones they are using become
less successful. A data-driven defense takes this into account and does not overly fixate
on a particular threat past its risk window and strives to detect new, rising, exploit trends
sooner.
The most successful exploits change over time.
Root Causes
Within those exploit methods are the actual attack vectors that allowed those methods
to be successfully executed in the first place—that is, the initial exploit vector or root
cause. It's at least as important to recognize how a particular threat got through existing
defenses as it is to simply recognize the threat. IT security breach root causes include:
•
Zero days
•
Unpatched software
•
Social engineering
•
Password Issues
•
Data Leaks
•
Eavesdropping
•
Misconfiguration
•
Denial of Service
•
Insider/Partner/Consultant/Vendor/3rd Party
•
User Error
If you are trying to minimize initial breaches you need to understand the root causes of
those breaches. For example, if a malware program gets executed on a user's
workstation, how did it get there? Did it exploit unpatched software or use social
engineering? Was it embedded in a website the user visited, or did it crawl across
network file shares? Defenders would realize bigger dividends if they concentrated more
on how something was accomplished than on what it did after it was executed.
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Unfortunately, most computer security defenses don’t focus much on the root causes of
initial exploits and then wonder why breaches keep happening.
How did it get this way?
It isn't natural for an army or a company to ignore responding appropriately to the
biggest risks. So how did it get this way?
There are a number of reasons (many beyond the scope of this paper), but they include
the sheer number of threats, inadequate ranking of their severity, poor detection metrics,
and poor communications. (Some of the best discussion on this subject has been written
by the world-renowned computer security expert Bruce Schneier, particularly in his book
Beyond Fear: Thinking Sensibly About Security in an Uncertain World.)
Sheer number of threats
In the computer world, new threats of astonishing variety arrive like water from a fire
hose. The military analogy that started off this white paper would probably better reflect
the digital environment if it showed one army being attacked over and over by every
other army in the world, without a break.
As the next figure below, from Microsoft Security Intelligence Report
(www.microsoft.com/sir) collected data shows, there are between 5000 – 6000 new
vulnerabilities reported each year (or about 15 per day, day-after-day). One-fourth to
one-third of them are ranked by the highest criticality by the vendor or reporting agency.
That’s a lot of top threats for defenders to understand and evaluate.
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And that’s just brand new, unique vulnerabilities. It doesn’t include the fact that
malicious hackers have generated tens of millions of pieces of unique malware, and there
are literally hundreds of different ways to exploit a computer system—malware,
password cracking, memory corruption, misconfiguration, user error, and eavesdropping,
to name a few. Not only do defenders have to worry about nearly every threat ever
found (because old threats rarely go away), but they must address every new
exploitation vector that the bad guys create.
To further complicate matters, defenders are often in charge of protecting multiple OS
platforms (Microsoft, Apple, Linux, Android, for example) and form factors (PCs, slates, a
variety of mobile devices, and cloud threats). Defending against a particular type of
attack, or even the same malicious threat, requires different defensive techniques. The
end result is an incredible number of new and old priorities competing for limited
resources, without the time to give each the consideration it deserves.
Lack of Focus – Competition for Attention
The sheer number of threats plus an astonishing array of other factors compete for the
attention of computer security defenders and management. Here are some common
factors which cause a lack of focus on the greatest threats:
•
Avalanche of Threats
•
Compliance Concerns
•
Too Many Projects
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•
Higher Priority Pet Projects/Politics
•
Slower Budgeting Cycles
•
Inefficient IT Organization
•
Corporate Culture Risk Tolerance
Most experienced IT security practitioners will likely agree with those reasons listed
above and be able to list more causes for lack of focus within their environment. IT
environments are complex with multiple stakeholders, each with their own concerns and
recommendations.
Poor ranking of threats
The sheer number of threats and competition for attention makes it harder to efficiently
rank which threats to focus on. In general, human beings are particular poor at ranking
critical risks, even when presented with the appropriate facts. For example, more people
are concerned about the airline flight they are on crashing (odds are 1 in 11 million) than
they were with the more higher risk car ride (1 in 5000) to the airport.
Because security defenders are besieged by so many threats and have neither the time
nor the resources to analyze comparative risk, they see them as illustrated below (“like
bubbles in a glass of champagne”), not as relative to the actual risk of each threat to the
organization.
Espionage
Ransomware
Spyware
Phishing
Adware
BYOD
Physical
Theft
Viruses
&
Worms
APT
SQL
Injection
Heartbleed
vulnerability
USB Attacks
Unpatched
Software
Insider
Threats
Sandworm
Vulnerability
Weak
Passwords
Anonymous
Hacker
Group
Buffer
Overflows
SSL Flaw
Pass-theHash
Attacks
Social
Engineering
Stolen
Data
Lost
Backdoor
Laptops Trojans
IoT
Leaked
Data
Fraud
Mobile
Threats are not ranked.
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Without a focused set of threats ranked by the localized risk to an organization, it’s easy
to see why the defenses mounted would mirror the un- or mis-ranked threats.
Don t Know
Outsource
AV
Warnings
Better Auth
Just Give Up
Vendors
Try
Anything
Patch
Everything
Policy
Policy
Police
Some
Monitoring
Vuln
Detection
Vuln
Detection
DLP
Complex
Passwords/
2FA
Don t Know
Disk
Encryption
Config
Mgmt
Segment
Patch
Windows
A little
end-user
training
Guessing
Not Sure/
Ignore
Looking Into
DLP
Legal
Every defense is treated equally.
Or worse, and far more common in most environments, is that mitigations are not
correctly proportionally applied to the right threats so that the mitigations requiring the
greatest resources do not align to the risks with the greatest potential impact/damage.
This is not only an inefficient allocation of resources, but also results in larger threats
continuing to remain more impactful.
Implementing a Data-Driven Computer Security Defense
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It would greatly benefit a company to focus on root causes and rank each threat based
on how much impact there is within their own environment.
#1
Vendors
Most Impactful
Exploit
Root Cause
Threat
#2
Most Impactful
Exploit
Root Cause
Threat
Medium
Threat
Medium
Threat
#3
Most Impactful
Exploit
Root Cause
Threat
Medium
Threat
Small Small Small Small Small Small Small
Threat Threat Threat Threat Threat Threat Threat
Small Small Small Small Small Small Small
Threat Threat Threat Threat Threat Threat Threat
Small Small Small Small Small Small Small
Threat Threat Threat Threat Threat Threat Threat
Small Small Small Small Small Small Small
Threat Threat Threat Threat Threat Threat Threat
Threats ranked by actual risk to the organization—the larger the circle, the greater the
risk/impact of the threat.
If IT defenders had a clearer picture of the relative organizational risk of each threat or
exploit, they could better align their defenses to that risk.
Implementing a Data-Driven Computer Security Defense
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Defenses
Against
Vendors
#1
Most Impactful
Exploit
Root Cause
Threat
Defenses
Against
#2 Most Impactful
Exploited Root
Cause
Threat
Medium
Mitigation
Medium
Mitigation
Defenses
Against
#3 Most Impactful
Exploited
Root Cause
Threat
Medium
Mitigation
Small Small Small Small Small Small Small
Threat Threat Threat Threat Threat Threat Threat
Small Small Small Small Small Small Small
Threat Threat Threat Threat Threat Threat Threat
Small Small Small Small Small Small Small
Threat Threat Threat Threat Threat Threat Threat
Small Small Small Small Small Small Small
Threat Threat Threat Threat Threat Threat Threat
May decide that the cost of defending against small threats is not a good business decision
Example of defenses ranked according to how much risk/impact the threat poses to the
organization—the larger the circle, the more important the defense.
Poor detection capabilities and metrics
In general, most companies do not do a good job of detecting localized and emerging
threats. It’s not that most IT environments don’t have the ability to detect these threats;
it’s that they often focus on the wrong things.
Understanding how a company was compromised (i.e. root cause) and how long the
compromise went undetected is far more important to a defense plan than names and
numbers alone. For example, companies can generate a list of all the malware families
that their antimalware software detected and removed. They can present actual numbers
and identify the malware programs that were removed. But if they cannot tell you how
that same malware was introduced into the environment in the first place—such as
through social engineering, unpatched software, or a compromised website or USB
drive—or say how long the malware program was in the environment before it was
detected, then all those names and numbers are of little use.
Implementing a Data-Driven Computer Security Defense
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Microsoft Showcase
Gathering data to identify attack vectors
The Conficker worm in 2008 could propagate using at least three different methods:
memory corruption, guessing file share passwords, and running automatically from a
USB drive. Initially it was believed that Conficker was spreading due to unpatched
software—after all, unpatched software is often the number one problem. The Microsoft
Security Intelligence Report Volume 12, however, explained that the key to efficiently
eradicating Conficker was understanding how often it was successful using each exploit
vector. Microsoft detection methodologies determined that most Conficker infections
were the result not of unpatched software, but of poor password policies and autoruns
from USB drives.
Microsoft communicated this information in its Security Intelligence Report, and created
and distributed a patch that disabled autorun functionality. By focusing on the most
common attack vectors, Microsoft was able to help customers significantly decrease the
number of Conficker infections.
Computer Security Defense Cycle
In general, most companies have inadequate metrics and data about successful
malicious hacking against their company, in their industry, and even worldwide. Because
of this, it often takes months—even years—for a defender to adequately respond to the
biggest threats. And once the correct security defenses have been put in place and are
working, the attackers simply change their tactics and start the whole cycle again. The
next figure below shows the common phases a particular attack vector and the
associated mitigations go through over time.
All computer threats and security defenses undergo a similar cycle of response and
remediation with each exploit.
Even though the most popular exploits always change over time, the computer defense
cycle does not. Early on, the company may not be prepared at all to deal with an
Implementing a Data-Driven Computer Security Defense
18 | Page
emerging threat. If the company is not experiencing exploits from the new attack vector,
it may even feel that it is somehow immune to the attack. Often by the time the
company realizes that the new attack has made a significant negative impact, the exploit
is widespread and out of control.
If the exploit continues to spread, the company must eventually respond to it if it wants
to survive, and change or increase remediation methods to handle the exploit. Then, the
company will begin to get a handle on the exploitation method, decreasing the number
of successful exploitations. Eventually, either due to the company’s remediation
responses or technology changes, the exploitation method will cease to be a top threat.
Historically, it has often taken longer than it should between the early and mature stages
of computer defense.
A data-driven computer security defense recognizes the defense cycle stages and
attempts to better recognize growing, emerging threats in order to more quickly
implement appropriate mitigations to lessen risk faster.
Poor communication of top threats
To determine if your company is a candidate for a data-driven computer security
defense, ask if the majority of IT security employees would be able to accurately describe
the number one threat to your organization? If you can get anything resembling
consensus, and answers that are supported by actual data, your company is rare indeed.
IT security employees in most companies cannot cohesively name the number one
computer security threat that is most successfully exploiting their company, so
unfortunately they cannot correctly or efficiently align defensive resources against them.
Can your employees accurately describe the number one
threat to your organization?
If IT Security employees cannot accurately and cohesively describe the top threats
among themselves, how can the top threats be accurately described to mid- and toplevel management? If management doesn’t know what the top threats are and how to
mitigate them, how can they approve getting resources in the right places? This leads to
inefficient alignment of resources, focus, and accountability on the wrong threats and
defenses.
Implementing a Data-Driven Computer Security Defense
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What a data-driven computer security defense
looks like
A data-driven computer security defense focuses on:
•
Minimizing Initial Breaches
•
Root-Causes of Initial Exploits
•
Historic and Current Attacks First
•
Data-Driven Mitigations Right-Aligned To Most Critical Threats
In a company using a computer security defense model driven by data, every employee
would know the top root cause threats that are most successfully exploiting the
organization's security. There would be no guessing; everyone could point to the top
threats to the company's critical computer systems.
With a data-driven computer security defense, the IT team would actively collect threat
intelligence and appropriately rank the risk to the company of the most likely critical
threats. It would then focus resources on the biggest threats with senior management
involvement and approval, and use metrics to track success. All defenses would be
measurable and held accountable for the threats they purport to lessen. The ultimate
outcome is a lower number of exploitations and lower computer security risk to the
organization.
When a new threat emerges, the organization is on top of it from the start, measuring
the company’s own rate of exploitation from the threat. It is a continuous, faster cycle of
alertness, mitigation, and reduction.
The key outcome of a data-driven computer security defense
is an operational framework that results in faster, more
responsive cycles of alertness and mitigation, due to a more
precise focus on reducing the top root-cause threats.
Part of a Comprehensive Defense
A data-driven defense doesn’t replace your existing strategy. It re-aligns and augments
your already existing comprehensive strategy. In today’s world, you have to “assume
breach”, that one or more attackers have already penetrated your hardened defenses
and are inside your perimeter. As part of a data-driven defense, you should identify the
Implementing a Data-Driven Computer Security Defense
20 | Page
root causes of how they got in, as well as, detecting, tracking, and slowing down what
they do once they are inside of the environment. Your assume breach strategies will
likely be as big as your root cause defenses.
Additionally, there are many defense you do as a normal part of doing business or as a
part of general security hardening (for example, strengthening authentication or
hardening access controls, etc.). These are things that everyone should do as part of their
defense-in-depth.
Lastly, there will be things that either you aren’t aware of (i.e. unknown threats and risks
or zero days), or threats and risks you accept as a part of doing business. No business
can afford to minimize every potential threat and risk to zero. You probably couldn’t do
so even if you tried. Risk management has always been about identifying the biggest and
most likely threats while realizing that some risks may go accepted or unaddressed.
The conflux of these different computer security defenses strategies can be thought of as
your comprehensive strategy (see figure below). All parts of your strategy have a place.
This paper is attempting to get defenders to recognize the often missing components of
root-cause analysis and risk relevance ranking.
A Comprehensive Computer Security Strategy Contains These Components Topped by a
Root-Cause, Data-Driven Defense Which Attempts To Minimize Breaches
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Implementing a data-driven computer
security defense
The ultimate objective for a computer security defense built on data is to create and
implement a framework that assists defenders in creating more timely mitigations that
focus on the biggest, most likely threats first.
A key goal of an implemented data-driven computer security defense is to more directly
align and funnel mitigations against the root-causes of the most successful threats.
Data-Driven Defense Alignment Focus Areas
Goal
Streamline Mitigations
Against Root-Causes of
Successful Exploitation
Data-Driven Analysis
Data-Driven Responses
More Intelligent
Threat Intelligence
Inclusive
Threat Detection
Root Cause Analysis
Better Risk
Assessment
Localized
Detecting
Root Causes
Renewed
Focus
Tied To Root
Causes
Implement
Risk-Aligned
Mitigations
Measurable
Accountable
Outcomes
Aligned to
Biggest
Threats
Are Defenses
Successful?
Central to this defense is focusing on threats that are ranked by risk to the specific
organization’s computers.
A data-driven computer security defense plan includes the following steps:
•
Collect better and localized threat intelligence
•
Rank risk appropriately
•
Create a communications plan that efficiently conveys the greatest risk threats to
everyone in the organization
•
Define and collect metrics
•
Define and select defenses ranked by risk
•
Review and improve the defense plan as needed
Here is a diagram summarizing the key components of a data-driven defense plan:
Implementing a Data-Driven Computer Security Defense
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Collect Better and Localized
Threat Intelligence
Rank Risk Appropriately
Review and Improve
Plan As Needed
A Data-Driven Computer
Security Defense
Cycle
Create Effective
Communication s Plan
Select and Deploy
Root Cause
Defenses
Defined and Collect Metrics
Collect better and localized threat intelligence
It is impossible to prepare for all threats, and that shouldn’t be the goal. Instead,
defenders should focus on the most likely and impactful threats to their specific
environment. To do that, each defender needs to create and gather threat intelligence
from many sources, both inside and outside the company.
Start with your company’s own localized experience, which in general is the most
relevant and reliable. Your company will always be susceptible to other threats, but the
history of successful exploitations against your company is one of the best data sources;
hackers and malware often use attack methods that have worked successfully against
your company in the past. The most successful attack methods are in direct response to
the organization’s biggest weaknesses.
After gathering internal data, get additional data from partners and industry news, and
then move out to external vendors and worldwide news feeds. As you move away from
the company’s own experience, the data will usually become less relevant. The figure
below summarizes the relationship as threat intelligence moves further away.
Implementing a Data-Driven Computer Security Defense
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The Relevance of Threat Intelligence
This is not to say that vendor or general threat information cannot have acute relevance
during particular time periods. Commonsense must prevail. For example, the first news of
a fast moving Internet threat often comes from external sources far away from the
organization. But even in those instances, the threat may or may not be relevant to the
organization depending on its existing defenses and platform makeup.
A data-driven computer security defense focuses on historic and current attacks first,
followed by the in-the-wild attacks most likely to occur to the enterprise, or within their
industry, followed by everything else. Although an enterprise can be compromised by
something new or rare (i.e. a zero-day), generally most of an enterprise’s computer
security risk occurs from previous and existing attacks. A data-driven computer security
defense accepts the risk that some unknown exploit may occur against it, but chooses to
focus on the known and most expected risks first.
It’s also important to note that simply because a critical vulnerability is found, or found
thousands of times, does not necessarily make it a critical risk. A typical vulnerability scan
in an average organization will reveal thousands of vulnerabilities, with a large
percentage of them being ranked with the highest criticality. It’s very normal.
It’s important to ask the likelihood and impact of those vulnerabilities being used
successfully against the organization to access valuable resources. And conversely, a
lessor ranked vulnerability should be given higher criticality if it has been used in the
past or is currently actively being used against the organization. Criticality and risk is not
something you should readily accept from someone else’s data and pronouncements.
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External parties do not understand what defenses and mitigations are already deployed
in your environment that will offset particular vulnerabilities, even if they exist.
The concept of threat intelligence localization is brought in this paper because most
organizations neglect their own and best data. By beginning with their own experience,
organizations are most likely to be able to respond to the most likely threat scenarios in
most time periods.
Note: It’s important to understand that risk is measured by impact and not purely by the
number of occurrences. Less occurrences of a particular threat could result in more
damage and impact than more occurrences of another threat. Risk is calculated by
overall impact and damage to the environment. With that said, if potential impact is
difficult to calculate for risk purposes, the number of occurrences can be used as the
primary driver of risk if the number of occurrences of a particular threat is a good
indicator of overall impact.
Microsoft Threat Intelligence Resources
Many software and antimalware vendors have data feeds specifically for sharing threat
intelligence. Some are free, but most are paid services. Microsoft offers many free
resources that provide good, timely threat information. In particular, the Microsoft
Security Intelligence Reports, published about once a quarter, provide specific threat
information, including regional information, along with specific defense
recommendations.
•
Microsoft Internet Safety & Security Center
•
Microsoft Malware Protection Center
•
Microsoft Security Intelligence Report
•
Microsoft Malware Protection Center blog
•
Microsoft Threat Reports
•
Microsoft Cyber Trust Blog
•
Microsoft Security Response Center blog
•
Microsoft Security Research and Defense Blog
•
Microsoft Security Newsletter
Microsoft offers many free data sources for threat
intelligence.
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Large organizations should assign intelligence data aggregation to an employee or team
who can subscribe to and follow multiple security newsletters and RSS feeds, and create
news pages that aggregate the search results.
Rank risk appropriately
Ranking risk is often described as evaluating the likelihood of a particular event
happening, multiplied by the potential damage. Said more simply, risk is the possibility
of loss or injury from a specific event. Every model of risk management (including ISO
31000:2009) contains a section on determining the likelihood of a particular threat, which
then leads directly to developing defenses that are ranked by risk.
The National Institute of Standards and Technology (NIST), has several excellent
publications on risk and risk management, especially as they apply toward IT security,
including NIST Special Publication 800-37
(http://csrc.nist.gov/publications/nistpubs/800-37-rev1/sp800-37-rev1-final.pdf) and
NIST Special Publication (http://csrc.nist.gov/publications/nistpubs/800-30rev1/sp800_30_r1.pdf).
Unfortunately, humans, if left to their own “gut instincts” or beliefs, will often
inappropriately rank risks, not only in IT, but in their daily lives. For example, most people
fear plane crashes and shark attacks more than they do the car ride to the locations of
those activities, even though the car ride is thousands of times more likely to cause injury
or death.
It’s often the same with computer security. IT professionals will often rank the most
publicized threats above the attacks that are consistently and most successfully used
against them. Oftentimes managers will ask front-line employees what they are doing to
prevent a threat they have heard or read about versus asking what successful attacks the
front-line employees are seeing the most. Or a manager will ask employees to drop what
they are doing to defend against a (rarer) attack vector successfully exploited by a
penetration tester against the organization before they are finished defending against
the most likely attacks perpetrated by the more prevalent attackers in the wild.
Because of the chance of inappropriate risk assessment, risk must be assigned by using
collected data and metrics to determine relevance.
Microsoft Showcase:
How Microsoft Cybersecurity ranks threats to the company
1. Already or actively used against Microsoft
2. Already or actively used against industry competitors or peers
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3. Broadly in use or publicly and easily available
4. Everything else that's possible
Microsoft recommends assigning higher percentages of likelihood to malicious events
that have occurred, are occurring, or are likely to reoccur to your company, unless you
have taken sufficient steps to ensure that they don’t happen again. As previously
discussed, companies are routinely re-exploited using methods that have been
successful in the past; this is often a factor of what attackers are using against the
company (each attacker has their own go-to, often-used set of attack tools) as well as an
expected outcome when customers have gaps or weakness in a particular defense.
In general, Microsoft gives greater weight to past, current, and industry threats than to
broad attacks on many industries or theoretical attacks which have not yet happened.
Industry-specific attack campaigns are common. For example, if attackers targeting one
energy company are successful, they are more likely to capitalize on energy industry–
specific weaknesses against other energy companies.
This analysis must, of course, be compared with the success of those particular threats
and attacks and how much damage they caused. For example, if your defense system
cleans up every malware program before it has been able to execute on critical
computers or devices, it’s less likely that future malware attacks of the same nature will
have a significantly different impact. If, however, the opposite is true—that your
company has suffered great damage due to undetected malware—then your company
should give greater importance to such malware attacks in the future.
Appropriate risk assessment also means using common sense to recognize when your
consideration of external risks forces a change of approach—for example, if vendor and
national public resources announce a new critical threat that is likely to spread soon and
fast in any environment. This has happened a few times in recent history, including
ahead of the MS-Blaster worm. Microsoft and other agencies warned of a recently
discovered critical vulnerability, and advised everyone to patch their systems. A few days
later, a computer worm exploiting that vulnerability quickly made its way around the
world, infecting millions of unpatched computers.
If you have a hard time getting started on calculating
risk and risk events, start with the actual, recent loss(es)
your organization has incurred due to a cyber event(s),
then track back the causative origination events which
led to the loss(es)
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Additionally, there is always a chance that a relatively unknown exploit could spread
rapidly without any warning. This was the case with the 2003 SQL Slammer worm, which
was released in the early hours of a weekend morning in North America, and infected
most of the unpatched SQL servers on the Internet in
the first 10 minutes. By the time most defenders woke
Use your
up, it was too late to prevent the damage.
company’s unique
history as the
starting point of
predicting future
malicious attacks,
and then modify
that prediction
based on other
external threats.
The idea is to use your company’s unique history as the
starting point of predicting malicious attacks, and then
modify that prediction based on other external threats.
Although an externality may override your company’s
own exploitation history, in the absence of competing
priorities it’s best to use your company’s own history as
the starting point for ranking risk.
If you have a hard time developing your own threat
model, consider using the Microsoft Threat Modeling
Tool, or the Microsoft STRIDE Threat Model, a
classification scheme for characterizing known threats.
The Open Web Application Security Project also offers
an overview of the approaches to threat risk modeling.
Developing attack scenarios
Creating attack scenarios of the different ways a remote or inside attacker could
compromise your environment can be helpful in determining threats and risks. For
example, a scenario where:
•
An attacker launches a massive distributed denial of service (DDoS) attack against the
biggest web sites.
•
An attacker defaces your company's website.
•
An attacker uses phishing to obtain company credentials.
•
Hackers exploit unpatched software to give them remote access to end-user
workstations.
•
An attacker gives or sells a customer database to your competitors.
•
An attacker uses password hash dumping tools to obtain all Active Directory
credentials.
•
A virus deletes random files on the main corporate network.
•
An attacker obtains access to protected source code.
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It’s important to select risk scenarios that are most likely to occur and result in critical
damage to your company. This is the point where most computer defense plans misalign
defenses. Remember, there is a big digital gulf between discovered critical threats and
the ones that are most likely to occur in your environment.
Start by documenting and using the scenarios that have most recently occurred, and
then move out, much like the intelligence data feed idea, from scenarios that are more
relevant to less relevant. Use attack scenarios to help calculate threat likelihoods and
cost from damage.
Microsoft encourages vulnerability and threat modeling both inside and outside the
company. For example, Microsoft has held its famous BlueHat hacking competition
going on 14 years now. Plus, Microsoft Bounty Programs were recently expanded to
include external competitors. In these programs, Microsoft offers direct payments for
reporting certain types of vulnerabilities and exploitation techniques. Microsoft also
offers cash prizes for employees who come up with the most inventive scenarios for
modeling threats.
Defend against the initial compromise and additional tactics
Most malicious attacks have two phases:
•
The initial phase is how hackers (or their malware) gained initial access—for example,
through unpatched software, social engineering, misconfiguration, or weak
passwords. For a defender, this phase is more important than the second phase,
because it should directly correlate with contemplated defenses.
•
The second phase is what the hackers (or malware) did after the initial compromise.
Did they stay on the initial compromised computer or start to move laterally to other
computers of the same type, or vertically to computers with different roles? What
other exploits and tools did they use?
Not all hacker tactics work in both instances. There are different defenses for minimizing
an initial compromise and preventing additional movement or minimizing the
subsequent damage.
The concept of the attacker beginning with an initial exploit followed by one or more
exploits designed to move laterally or vertically is called exploit chaining. Defenders must
try their best to prevent both. If they can prevent the initial compromise, they don’t have
to prevent subsequent exploits. Unfortunately, most defenders have a hard time
preventing initial compromises, in which case they have to be aware of and defend
against both types of exploits.
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Targeted spear phishing: An initial compromise followed by additional
movement
A targeted spear phishing email campaign entices a user to open an email message, and
click on the enclosed web link. The web link usually contains malicious JavaScript, which
installs malware that exploits unpatched Java. The malware connects to its “mothership”
computer and is instructed to connect to other servers. This process is repeated
(sometimes up to 20 times), until the malware downloader is instructed to download
additional remote control software.
The remote control software gets activated and alerts a waiting human attacker, who
now has local access on several computers. The user on one of the compromised
computers is running as local Administrator. The hackers use the Administrator’s
permissions to launch additional programs in the Local System, where they are able to
obtain multiple user names, passwords, and password hashes.
One of the passwords belongs to a tape backup service account that belongs to the
Domain Admins group and is installed on every computer in the environment. The
attacker uses this newly found credential to log on to the nearest domain controller, and
downloads every Active Directory credential and password hash. Using the new
credentials and Pass-the-Hash (PtH) tools, the attacker is able to log on to all the other
servers in the environment, after which he can view and copy data at his leisure.
Calculating Causation Risk
As covered above, most data-driven computer security defense plans could be improved
by focusing more initial compromise (i.e. causation) threats and risks. Causation events
can be many things, including social engineering, unpatched software, misconfiguration,
zero day exploits, password guessing/cracking, etc. Unfortunately, detecting, identifying,
and measuring causation events is fairly difficult to do in most environments. Usually it’s
due to a variety of factors, which include tools, people, and processes. But in many
instances, it’s simply because the organization wasn’t looking for causation events
and/or trying to measure them.
This paper recommends the following improvements be made to collect causation
events to help better calculate attack origination events:
•
Communicate to all computer security defense resources that origination causes
of all successful attacks are important to the success of computer security
•
Implement tooling that could help with identifying and measuring causation
events
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•
Recognize that many successful exploits have one or a limited set of possible
ways they could have been caused. For example, malware that only spreads by
password guessing on NETBIOS shares.
•
If no tooling can be enabled or improved, try to draw conclusions using personal
interviews with compromised employees and/or forensic reviews. These sorts of
investigations can occur with every successful compromise or just be sampling. If
using sampling, try to conduct a sample size that is fairly representative of the
overall population
Use every reasonably available tool and process to start collecting causation statistics.
The ultimate goal is to come up with a chart that compares metrics of different causation
events, especially as compared to each other. Here’s a simple example:
Unpatched software – 78%
Social Engineering – 18%
Password guessing- 1%
Insecure drive shares – 2%
Misconfiguration – 1%
In most environments accounting for 100% of causation events will be difficult to
impossible to ascertain. However, each organization should try to collect these types of
metrics and statistics, and improve on their collection over time.
Using inventory to calculate causation risk
Many organizations have a hard time calculating risk, especially around origination
causative attack events. This is often because most organizations do not have good
instrumentation or tools around capturing or determining what initial attacks happened.
This paper recommends that all organizations work harder to develop better
instrumentation (composed of people, tools, and processes) to better determine and
track initial attack events.
However, another way to determine risk is to simply look at the software and hardware
inventory associated with compromised assets. For each device in your environment,
take an inventory of all software and hardware. This is something that you should be
doing already on a regular basis, but can be particularly helpful when determining
computer security risk. In order for this method to be most accurate, it’s important that
software and hardware inventory be taken on a fairly regular basis (say one week to one
month at longest).
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Then when a compromise is detected, compare what software and hardware was running
when the compromised happened versus machines that were not compromised during
the same event time window. You will be able to point out software and hardware
attributes that were more or less present on computers that were compromised versus
not compromised, and from that determine risk and mitigations.
For example, Microsoft frequently sees substantially more exploitations on computers
with the following attributes:
•
Unpatched software, especially unpatched Internet browser add-on software
•
Older operating system versions
•
Older browser versions
•
32-bit systems (versus 64-bit systems)
•
Systems running non-current versions of anti-malware software or none at all
•
Particular geographic regions throughout the world often tend to have higher
exploitation percentages
•
Non-domain joined computers
•
User Account Control (UAC) disabled
Microsoft frequently reports these statistics in their quarterly Security Intelligence
Reports (http://www.microsoft.com/sir).
An organization can use their own software and hardware inventory and compare it to
their own rates of exploitation to determine what device traits seem to lead to higher
risk. For example, one browser version of another may lead to more exploitations or
perhaps risk can be lowered by moving from 32-bit to 64-bit systems. With a good
inventory and detection of successful exploitations, any organization should be able to
determine relative risks for different software and hardware configurations.
Risk Assessment Is Risky
It’s important to note that risk assessment is always a risk itself. Risk assessment tries to
predict what threats an organization is most likely to be exposed to in the future. Any
risk assessment assumes the risk that predicted threats and risks may not align to actual
risks and threats when experienced in a future time period. In fact, it’s almost guaranteed
that any risk assessment will not be 100% accurate. Threats and their risks rise and fall
over time. New threats arrive and older threats disappear. And even if you mitigate 100%
of your predicted risks, an unexpected threat vector or determined adversary may
penetrate your deployed defenses. Risk assessors understand their predictions may not
always adequately protect their organization. But all other factors equal, a risk-assessed
Implementing a Data-Driven Computer Security Defense
32 | Page
defense based on real data should be more accurate than a computer defense plan
based on “gut” feelings or plans not based on collected data.
Create a communications and mitigation plan
Once you have identified the biggest threats it’s time to communicate that knowledge
throughout the organization. This needs to be done to ensure that all stakeholders have
a cohesive understanding of the biggest threats. This will help with the allocation of
resources and the creation of defenses across the organization.
Communication can be done using daily educational screen pushes, email messages,
newsletters, and the like. The end goal of a communications plan is that every
employee—from senior management and IT teams to end users and business partners—
can correctly identify the biggest security threats to the company and how each person
would work to participate in its remediation.
There is “no one size fits all.” A communications plan needs to include the right level of
data for each stakeholder about the most likely threats (and implemented defenses), and
also include ways for everyone to provide updates and feedback to the communications
plan and defensive mitigations. Each stakeholder needs to be talked to in the language
and metrics they are accustomed to receiving; at the same time, it’s crucial that key
critical threats be communicated so everyone is on the same page.
In particular, senior management must be briefed on the most likely critical threats in the
environment, as they ultimately decide how much risk the company can afford and what
resources to allocate to defend the system. End users, too, need to know the most likely
critical threats, as well as how to recognize and prevent them.
Microsoft Security Intelligence Reports are one of the most comprehensive looks at the
biggest, most likely threats and how to prevent those threats. Download each report
when it is released, and share those lessons with coworkers and employees when
relevant to the company’s own experience.
For example, if a company recognized unpatched software as its biggest threat, this
finding would be communicated to senior management and to every employee and
team. IT security would create a project task force team to address the problem of why
so much unpatched software exists in the enterprise, and develop a mitigation plan that
would start with removing software where it is not needed and set up a plan for timely
patching any software that remains. The patch management team would be given the
needed authority and rewarded for patching the most exploited programs first. The plan
would also provide for reworking applications that depend on it so that security updates
do not break them, and put in place defense remediation specific to the software such as
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blocking exploits coming from the Internet. The IT security team would disseminate the
remediation plan, as well as progress made, throughout the company.
Define and collect metrics
An often repeated commonsense say is, “If you can’t measure it, you can’t manage it.”
Once you have ranked your biggest risk threats, you need to define metrics to measure
the success or failure of the current and future proposed defenses against specific
threats.
•
Many companies measure overall software patch status, or even just the status of
Microsoft software, but each metric should align to a particular threat or set of
threats. Be as specific as you can. For example, the biggest threats usually aren’t a
general “unpatched software” or “social engineering”, but specific unpatched
programs and email phishing with malicious attachments or links.
•
Of course this specificity means your metrics will most certainly change over time.
What programs and methods attackers focus on over time changes. So, too, should
your metrics, as previous threats disappear and new ones appear.
•
Companies work hard to move their logon authentication mechanisms to two-factor
authentication, or at least to longer and more complex passwords—a laudable goal.
But it’s important to ask if the most recent critical exploits would have been prevented
by longer and more complex passwords or two-factor authentication. In most
companies, the answer would be no. So, for example, if unpatched Java is your
biggest problem, you would need to measure the percentage of patched Java in the
enterprise.
•
Similarly, while simple malware infections might not initially seem to be high risk, if
persistent attackers use malware to gain their initial foothold, allowing them to quickly
compromise the entire domain, then antimalware defenses might need to be moved
up.
•
It's also helpful to consider which metrics provide the most value to your company.
For example, it’s not as important to know how many malware programs your
antimalware program blocked in a particular time period as it is to know how many
false negatives it had—that is, where the program did not deliver an alert after
scanning malware that it should have detected. Blocked malware is like dropped
packets on a firewall—each block measures a successful remediation of a problem.
•
Metrics should indicate the success or failure of defenses against a specific threat. Use
the metrics and information coming back from implementers to help guide future
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data-driven computer security defense plans. Malware and hackers never stop
evolving, so neither will the defense plan.
•
No defense system can immediately detect and remove 100 percent of malware. For
example, there will always be some malware that is not initially detected, and because
of the false-negative identification, is allowed to execute for a certain amount of time.
Ultimately the biggest risk from malware is the time from initial execution to
detection. But how do you measure that?
Within Microsoft, every Microsoft Windows asset runs Microsoft AppLocker in audit-only
mode. This means we record every previously unexpected executable to the local event
log. When we find and remove malware, we can compare the malware detection and
removal date and time in the antimalware log to the first execution time recorded in the
AppLocker events.
Currently in test mode, the eventual idea is to create a metric called Mean Time to
Malware Detection, which ultimately correlates to our risk from undetected malware. The
smaller that number, the better our detection and the lower our risk. If the metric trends
up, we can look to our antimalware detection team for answers.
Learning from current and prior incidents is critical to understanding what threats have
been seen within an environment and in helping to create better monitoring and metrics
to detect those exploits faster. Getting help desk and incident response teams to better
document and capture this kind of data will dramatically improve your ability to prioritize
the best metrics about the most important threats.
Define and select defenses ranked by risk
After you make a list of risk-ranked threats, you can create and select appropriate
defenses.
Make sure you implement defenses that will directly and immediately reduce the
most critical and most likely threats. For example, in addressing the attack scenario of
the spear phishing email that ultimately leads to PtH attacks, customers may conclude
that they needed stronger authentication (often smart cards) and expensive intrusion
detection systems.
Smart cards and multifactor authentication solutions are good for strengthening
authentication, but rarely stop PtH attacks. Once a PtH attacker has your password hash,
there is little they can’t do except log on without your smart card. But they can still log
on remotely to other computers that accept the stolen credentials, map the drives, and
copy and steal data. And most of the time, intrusion detection systems have a hard time
differentiating between malicious behavior caused by a PtH attacker and what the
original holder of the credential might do.
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Proposed
defenses should
be proven to
lower the risk
they are being
proposed to
reduce.
When a defense is proposed, ask the proposer to walk
you through how their device or solution would
actually stop the attacker in the proposed scenario.
Don't take their word for it. Ask the proposer to show
details to prove that the defense will work.
The typical company Microsoft advises has dozens and
dozens of IT security projects and initiatives planned
each year. A significant percentage of those projects
never get done, and many of those that are done are
done sub optimally, or do not directly reduce the
threat they were intended to remediate. For most
companies, it would be far more useful for IT security
to focus on a few projects that will directly reduce the biggest threats the fastest.
Give precedence to defenses that stop initial compromises. This is also where you
should make sure to consider how the exploit was initially successful against your
environment. Stopping the initial compromise is more important than trying to stop a
single malware family or malicious hacker, or trying to stop what hackers do once they
have compromised your environment.
For example, for attackers to dump password hashes in
Give precedence to a Windows environment, they must have either local
Administrator or Domain Admins security contexts.
defenses that stop Once they have that level of elevated privilege, they
can do anything allowed by the OS, or even modify the
initial
OS to do things it would have never allowed. They can
compromises.
disable all your defenses, create a backdoor user
account, or even modify the OS (in which case the OS
is no longer the vendor’s or the user’s). Said another way, you can put down every single
PtH attack and still not stop your attacker from successfully owning your environment.
But if you stop your attacker from getting domain or enterprise admin, you’ve stopped
many attacks.
Microsoft Showcase:
Mapping threats against mitigation capabilities
Internally, Microsoft has created a Threat Mitigation Matrix that maps possible threats
against current mitigation capabilities. It not only helps identify the gaps, but is also a
good process for understanding when new capabilities are needed, and whether a new
tool under consideration would cover a gap or is simply overlapping.
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Microsoft has also developed an internal Threat Monitoring Matrix, which maps threats
against the monitoring tools most likely to alert or document a related incident. In this
case, every log and tool that can generate an event is mapped against the different types
of threats that may impact the environment. Like the mitigation matrix, the monitoring
matrix is crucial for identifying gaps and weaknesses.
Tie Defenses To Threats
For maximum efficiency, each mitigation would be directly compared against the threats
they are desired to defend against, along with the percentages of threats they resolve.
For example:
% of Threats
Defensive Mitigation
Mitigated By
Defense
Better Patch Management
62%
Better Social Engineering Training
20%
Two-factor authentication
15%
Longer and more complex passwords
2%
Note: % of Threats Mitigated by Defense will often add up to more than 100%, as several defenses will often
mitigate the same threats.
The goal is to directly identify how much of the current threats would be removed by
applying particular and specific mitigations. The example above is a simplistic
representation of a table in a real production environment, but the concept is the same.
Review and improve the defense plan as needed
It is just as important at the end of the year to measure how well the deployed defenses
did against the threats they were supposed to mitigate. If one or more defined threats
persisted despite the defensive mitigations, defenders need to know why, and redefine
the plan to account for the needed changes.
Every person that sponsored a particular defense should be held accountable to support
how well their defensive recommendation did or didn’t do against the threats they
purported it would reduce. Accountability is a key component of a data-driven computer
security defense plan.
It is expected that attackers will change tactics over time, and malicious techniques will
change to fight the deployed defenses. Successful attack methods must always be
measured, and noted when changing in percentage of occurrence.
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A particular attack lessening could be due to several factors, including:
•
Effective deployed defenses
•
Changes in threat landscape
•
Changes in technology
•
Other attack methods becoming more successful
New attack methods should be aggressively looked for, noted, and watched for increases
in occurrence. A data-driven computer security defense should only be considered
lifecycle successful if it is able reduce current risks and to detect new attack methods so
that defenders can most effectively and timely respond to better remediate attackers and
their methodology.
Putting It All Together
Most organizations would significantly benefit by utilizing the concepts and framework
recommended in this whitepaper. A Data-Drive Computer Security Defense framework
will help organizations more efficiently allocate defensive resources against the most
likely threats to reduce risk the fastest.
A Data-Driven Computer Security Defense strategy focuses on:
•
Minimizing Initial Breaches
•
Root-Causes of Initial Exploits
•
Historic and Current Attacks First
•
Data is King
•
Relevance Drives Risk Assessment
•
Data-Driven Mitigations Right-Aligned To Most Critical Threats
A new data-driven plan for defending computer security includes these steps:
•
Collect better and localized threat intelligence
•
Rank risk appropriately
•
Create a communications plan that efficiently conveys the greatest risk threats to
everyone in the organization
•
Define and collect metrics
•
Define and select defenses ranked by risk
•
Review and improve the defense plan as needed
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A quick way for most readers charged with defending their organization’s computer
environment is to use this whitepaper to educate needed sponsors. Then focus on
creating the threat intelligence and metrics need to detect and communicate the top
threats across the organization. Create and implement defenses which directly mitigate
found threats.
A good tool for starting a new Data-Driven Computer Security Defense is a short slide
presentation communicating the basic concepts, followed by a single slide listing the
biggest threats to the organization, followed recommended defenses. Perhaps 5 to 10
slides are all that is needed to begin your organization’s transition to a better, datadriven defense.
The outcome is a more efficient appropriation of defensive resources with measurably
lower risk. The measure of success of a data- and relevancy-driven computer security
defense is fewer high-risk compromises and faster responses to successful compromises.
-
End of main content -
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FAQ
Here is a list of commonly asked questions regarding the statements and proposals of
this whitepaper. Contact the author, rogrim@microsoft.com, to add additional questions
or to argue against particular answers.
I know how often we have unpatched, high risk, software in our environment. How
does that differ from what you’re saying?
There is a huge information gulf between how much unpatched software you have and
how often it is used to successful exploit your organization, and the latter is far more
important about determining real risk in your organization. For example, even though
you have unpatched software the risk of it could be completely be eliminated in your
environment due to other mitigations. If unpatched software is leading to zero
exploitations in your environment, then it might argue that it doesn’t need to be
resolved.
What real benefit is there to a defense if everyone knows what the top threats are?
How does a common understanding actually make a defense more efficient versus
the IT security department alone knowing and implementing?
I can’t believe I get this question, but it’s a relatively frequent one from computer
security defenders who don’t fully realize the value of everyone rowing in the same
direction. First, most IT security departments can’t answer the question of what the most
popular successful attack types are in their environment. Without understanding that
fundamental fact, how can any resources be most efficiently directed. Second, it’s almost
always assured that senior management doesn’t know the correct answers, and if they
don’t, how can you get senior management support and budget to implement the
needed changes in the defense plan. Lastly, when everyone in the company knows the
top threats, they can be more focused and aware of them, and hopefully fight them
better. Without that understanding you will almost certainly fail in that task.
Can’t attackers just attack you using any attack at any time and bypass the top
threats, and associated defenses, you implemented?
Yes. There are thousands of possible vulnerabilities and attacks an attacker can use. The
answer is where are you going to focus your scarce resources? A traditional risk
management model says that what you defend against should be among the highest
risks with the most costs. This paper is not changing that concept, it’s only saying that
defenders aren’t assessing risks correctly and use the included framework to better align
defenses. It doesn’t guarantee that a malicious hacker won’t find a way around the
deployed defenses. It only tells you where most attackers will attack, based upon the
best available current, local, data. Defenders worried about other, less popular attacks,
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should deploy defenses as they see fit. This paper only sets the expectation that the most
popular attack types should also be defended against using the most resources.
You aren’t mentioning Pass-the-Hash attacks (or whatever XYZ popular attack
type), why not, since it is the biggest reason why enterprises are compromised?
Great question. But oftentimes defenses concentrate on the wrong things. A data-driven
defense focuses on initial compromise vectors, because if you don’t close those holes,
the rest of your defenses will ultimate fail in a game of “whack-a-mole” in which the
hackers rule. For example, suppose we successfully stop all pass-the-hash (PtH) attacks,
so that there is never another one in the world ever again. Would that stop attackers
from taking over your network? Probably not. In order to accomplish pass-the-hash
attacks, the attacker needs membership in local Administrators or in Domain Admins,
and if they have that level of access there is nothing they cannot do. You may end PtH
attacks, but the same hackers will just start insert key loggers or malicious programs to
capture credentials and/or maintain administrative control. When you start thinking
about the importance of defending against initial compromises, you begin to see a
computer compromised by “harmless” adware is just as vulnerable to a far more
malicious program, because the effort need to place the malicious program is identical. A
computer infected with adware is a warning that your defenses aren’t working, and is just
as dangerous as a computer infected by something else.
Don’t defenders have to do both “Assume Breach” defenses as well as “Minimize
Breach”, which the Data-Driven Defense advocates?
Absolutely. Today attackers have the upper hand. Most organizations are either currently
compromised or could easily be compromised. It’s our reality today. The majority of your
defenses probably need to concentrate on slowing down attackers once they have
compromised your environment. What a Data-Driven Computer Security Defense
indicates is that some portion of your defense should be dedicated to Minimize Breach.
Let your history and current record of exploitations drive what types of defenses are
deployed where and in what proportions.
Am I wasting money and resources on XYZ defense?
I don’t know. I’m not an expert in your organization’s threat and attack experiences.
There’s only one question and answer that matters, Does the XYZ defense mitigate
vulnerabilities that would otherwise be actively and successfully exploited today, or in the
near future, in your environment? And because any threat or risk could possibly be
actively exploited in your environment it’s important to recognize which threats and risks
are most likely, and defend against those first. For example, suppose you buy and run
software that looks for and closes software bugs in your organizations’ custom made
software that only runs internally. Has your company ever been successfully exploited
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with an attacker using a bug found in your custom, internal-only software? If not, why
focus on securing it? A data-driven defense is all about you looking at your
organization’s actual experience and then using the data to determine what needs to be
focused on.
You said unpatched software, and specifically Java, is to blame for most successful
exploits, but Java or unpatched software is no longer the biggest reason. You were
wrong.
First, please recognize that I’m writing this response while unpatched software and Java
is still the number one problem. One of the biggest lessons to learn in a data-driven
defense is that what is the number one threat absolutely changes of time. It will change.
Expect it! And what is the world’s most common number one threat may not be your
organization’s biggest threat. The idea is to use your own local data to determine what is
your biggest threats and defense against those. Plus a good data-driven defense plan
expects change and is deploy in such a way that when something new starts to become
a bigger threat, it is noticed quicker, and responded to quicker.
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Related reading
Schneier, Bruce. Beyond Fear: Thinking Sensibly About Security in an Uncertain World,
Copernicus Books, 2003
Boose, Shelly. Key Metrics for Risk-Based Security Management, The State of Security,
July 2013
Grimes, Roger A. 5 reasons why hackers own your organization, InfoWorld, September
2014
Jacobs, Jay and Rudis, Bob, Data-Driven Security: Analysis, Visualization and Dashboards,
Wiley, 2014
Microsoft Security Intelligence Reports
Pereira, Marcelo. Human and tech flaws caused data hemorrhage from Dept of Energy.
Let’s learn from their mistakes in 2014, January 2014
Platt, Mosi K. Making Your Security Metrics Work for You, Pivot Point Security, August
2012
Symantec. Why Take a Metrics and Data-Driven Approach to Security?, Confident Insights
Newsletter, December 2012
Young, Lisa. Tips for Using Metrics to Build a Business-driven Threat Intelligence
Capability, ISACA, August 2014
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