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ENHANCING INCIDENT RESPONSE PLAYBOOKS WITH MACHINE LEARNINGENHANCING INCIDENT
RESPONSE PLAYBOOKS WITH MACHINE LEARNING

Cybersecurity analysts use playbooks as a guide to quickly investigate and
respond to incidents, but regularly neglect to keep the process documents up to
date.

Robert Lemos, Contributing Writer

December 5, 2023

4 Min Read
Source: Ivelin Radkov via Alamy Stock Photo


Every company should have a general incident response plan that establishes an
incident response team, designates the members, and outlines their strategy for
reacting to any cybersecurity incident.

To consistently act on that strategy, however, companies need playbooks —
tactical guides that walk responders through investigation, analysis,
containment, eradication, and recovery for attacks such as ransomware, a malware
outbreak, or business email compromise. Organizations that do not follow a
playbook for security will frequently suffer more serious incidents, says John
Hollenberger, senior security consultant with Fortinet's Proactive Services
group. In nearly 40% of the global incidents Fortinet handles, the lack of
adequate playbooks was a contributing factor that led to the intrusion in the
first place.



"Quite often we have found that while the company may have the right tools to
detect and respond, there was no, or inadequate, processes around said tools,"
Hollenberger says. Even with playbooks, he says, analysts still have complex
decisions to make based on the details of the compromise. He adds, "Without
knowledge and forethought by an analyst, the wrong approach may be taken or
ultimately hinder response efforts."



Unsurprisingly, companies and researchers are increasingly trying to apply
machine learning and artificial intelligence to playbooks — such as getting
recommendations on what steps to take while investigating and responding to an
incident. A deep neural network can be trained to outperform current
heuristic-based schemes, recommending next steps automatically based on the
features of an incident and playbooks represented as a series of steps in a
graph, according to a paper published in early November by a group of
researchers from Ben-Gurion University of the Negev and technology giant NEC.



The BGU and NEC researchers argue that manually managing playbooks can be
untenable in the long run.

"Once defined, playbooks are hard-coded for a fixed set of alerts and are fairly
static and rigid," the researchers stated in their paper. "This may be
acceptable in the case of investigative playbooks, which may not need to be
changed frequently, but it is less desirable in the case of response playbooks,
which may need to be changed in order to adapt to emerging threats and novel,
previously unseen alerts."




PROPER REACTIONS REQUIRE PLAYBOOKS

Automating the detection, investigation, and response to events are the domains
of security orchestration, automation, and response (SOAR) systems, which —
among other roles — have become the repositories of playbooks to use in the
variety of circumstances firms face during a cybersecurity event.

"The world of security is dealing with probabilities and uncertainties —
playbooks are a way to reduce further uncertainty by applying a rigorous process
to gain predictable final outcomes," says Josh Blackwelder, deputy chief
information security officer at SentinelOne, adding that repeatable outcomes
requires the automated application of playbooks through SOAR. "There's no
magical way to go from uncertain security alerts to predictable outcomes without
a consistent and logical process flow."

SOAR systems are becoming increasingly automated, as their name suggests, and
adopting AI/ML models to add intelligence to the systems is a natural next step,
according to experts.

Managed detection and response firm Red Canary, for example, currently uses AI
to identify patterns and trends that are useful in detecting and responding to
threats and reducing the cognitive load on analysts to make them more efficient
and effective. In addition, generative AI systems can make it easier to
communication both a summary and the technical details of incidents to
customers, says Keith McCammon, chief security officer and co-founder of Red
Canary.



"We don't use AI to do things like make more playbooks, but we are using it
extensively to make execution of playbooks and other security operations
processes faster and more effective," he says.

Eventually, playbooks may be fully automated through deep learning (DL) neural
networks, the BGU and NEC researchers wrote. "[W]e aim at extending our method
to support complete end-to-end pipeline where, once an alert is received by the
SOAR system, a DL-based model handles the alert and deploys appropriate
responses automatically — dynamically and autonomously creating on-the-fly
playbooks — and thus reducing the burden on security analysts," they wrote.

Yet giving AI/ML models the ability to manage and update playbooks should be
done with care, especially in sensitive or regulated industries, says Andrea
Fumagalli, senior director of orchestration and automation for Sumo Logic. The
cloud-based security management company uses AI/ML-driven models in its platform
and for finding and highlighting threat signals in the data.

"Based on multiple surveys that we've conducted with our customers over the
years, they are not comfortable yet having AI adapting, amending, and creating
playbooks autonomously, either for security reasons or for compliance," he says.
"Enterprise customers want to have full control over what is implemented as
incident management and response procedures."

Automation needs to be fully transparent, and one way to do that is by showing
all the queries and data to the security analysts. "This allows the user to
sanity-check the logic and data that is returned and validate the results before
moving to the next step," says SentinelOne's Blackwelder. "We feel this
AI-assisted approach is the appropriate balance between the risks of AI and the
need to accelerate efficiencies to match the rapidly changing threat landscape."





ABOUT THE AUTHOR(S)

Robert Lemos, Contributing Writer



Veteran technology journalist of more than 20 years. Former research engineer.
Written for more than two dozen publications, including CNET News.com, Dark
Reading, MIT's Technology Review, Popular Science, and Wired News. Five awards
for journalism, including Best Deadline Journalism (Online) in 2003 for coverage
of the Blaster worm. Crunches numbers on various trends using Python and R.
Recent reports include analyses of the shortage in cybersecurity workers and
annual vulnerability trends.

See more from Robert Lemos, Contributing Writer
Keep up with the latest cybersecurity threats, newly discovered vulnerabilities,
data breach information, and emerging trends. Delivered daily or weekly right to
your email inbox.

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