Incident response centers on one core remit: Minimizing the impact of a cyberattack.
When security incidents occur, every second counts. Security Operations Center (SOC) teams must quickly identify the threat and contain the spread of malicious activity to restore normal operations before significant damage is done.
Unfortunately, AI-boosted cyberattacks are fundamentally changing the pace of modern incidents.
Threat actors can now use AI to automate and launch multiple attack campaigns simultaneously. At the same time, organizations are introducing agentic AI and automation platforms into everyday workflows, expanding the potential attack surface defenders must protect.
This means the incident response plans that worked five years ago are no longer enough.
Response still relies on fast detection and effective containment, but SOC teams must now prepare for attacks that move faster, adapt more quickly, and generate far more activity than traditional security operations were designed to handle.
What is incident response?
Incident response is the structured process organizations use to identify, investigate, contain, eradicate, and recover from cybersecurity incidents.
A mature incident response plan typically includes several key stages:
- Preparation
- Detection and analysis
- Containment
- Eradication
- Recovery
- Lessons learned
Having a documented incident response plan allows organizations to respond consistently during high-pressure situations while minimizing operational disruption and reducing the overall impact of an attack.
But as attacks become increasingly automated, each of these stages is coming under greater pressure.
How AI is changing incident response
AI hasn't created new attacker objectives, but it has changed how quickly they can achieve them. Tasks that once required hours to days can now be completed in minutes.
Attackers are using AI to:
- Generate convincing phishing campaigns at scale
- Automate vulnerability discovery
- Create unique malware variants designed to evade traditional detection
- Modify scripts during an attack
- Coordinate attacks across multiple endpoints simultaneously
- Identify opportunities for privilege escalation and lateral movement more quickly
Meanwhile, organizations are deploying AI-powered tools of their own. From coding assistants and productivity tools to autonomous AI agents capable of interacting with enterprise systems, these technologies introduce new connections, permissions, and workflows that SOC teams must account for during an investigation.
Simply put, there are more systems communicating, more applications making decisions, and more activity occurring than ever before. That increases both the complexity of incident response and the importance of having clear visibility into what "normal" looks like.
Why identity is no longer enough
Historically, incident responders often began an investigation by asking a straightforward question:
Who logged in?
That's only part of the picture now. Modern attackers frequently gain access using legitimate credentials obtained through phishing, session hijacking, stolen authentication tokens, or compromised API keys.
To security tools, these sessions often appear completely legitimate with successful user authentication and complete multi-factor authentication. Often, there has not necessarily been any malware detected, yet malicious activity may already be underway.
Rather than focusing solely on who authenticated, SOC teams increasingly need to understand what is happening after authentication.
These questions become far more valuable:
- Is this application behaving as expected?
- Has this process attempted actions it has never performed before?
- Should this endpoint be communicating with this server?
- Is this account accessing resources outside its normal scope?
- Has an approved application suddenly begun interacting with sensitive data?
Behavior provides context that identity alone cannot.
Focus on behavior, not just indicators
Traditional threat detection and response has relied heavily on indicators of compromise (IOCs), including:
- Known malicious hashes
- IP reputation
- Malware signatures
- Known command-and-control infrastructure
These remain valuable, but AI allows attackers to change those indicators faster than defenders can update detection rules.
Malware can be rewritten, infrastructure can rotate, and Scripts can change automatically. The behavior, however, often remains consistent.
For example, it is rarely normal for:
- Microsoft Word to launch PowerShell.
- A PDF reader to execute command-line tools.
- A browser extension to access sensitive local files.
- A productivity application to begin scanning network shares.
- An AI assistant to suddenly interact with resources outside its intended scope.
By monitoring application behavior alongside traditional indicators, SOC teams gain additional context that helps distinguish legitimate business activity from malicious actions—even when the attacker is using valid credentials or previously unseen malware.
Prevention must be part of your incident response
One of the biggest changes AI introduces is time, with automated attacks greatly compressing the window between initial access and widespread compromise. An attacker no longer needs hours to move laterally or identify valuable assets.
Many of these activities can now be automated.
That means incident response cannot begin only after malicious activity is detected. Preparation increasingly means reducing what attackers can do before an incident occurs.
This includes controls such as:
- Preventing unauthorized applications from executing
- Restricting what approved applications can do
- Removing unnecessary administrator privileges
- Limiting lateral movement between systems
- Restricting access to cloud resources
- Continuously validating users, devices, and applications
The goal isn't to eliminate incident response. It's to ensure that when an incident occurs, there is far less damage to contain.
Best practices for incident response in the AI era
As organizations continue adopting AI technologies, incident response plans should evolve alongside them. Several best practices can help SOC teams prepare.
Continuously review your incident response plan
Incident response procedures should be reviewed regularly to ensure they reflect current attack methods, cloud services, AI integrations, and business processes.
Monitor application behavior
Understanding how trusted applications normally behave makes abnormal activity easier to identify during an investigation.
Behavioral visibility provides valuable context when attackers are using legitimate credentials or trusted software.
Reduce the attack surface
Every unnecessary application, unused administrator account, exposed service, or excessive permission creates another opportunity for attackers.
Reducing these opportunities makes successful attacks more difficult from the outset.
Practice realistic incident response scenarios
Think beyond traditional ransomware scenarios. Tabletop exercises should now include AI-assisted phishing campaigns, compromised AI tools, cloud account abuse, and automated lateral movement.
The closer simulations reflect today's threats, the better prepared response teams will be.
How Zero Trust strengthens incident response
A Zero Trust approach complements incident response, because even after gaining access to an environment, attackers will be very limited in what they can do.
Rather than assuming authenticated users or trusted applications are inherently safe, Zero Trust continuously verifies access while enforcing least privilege and explicit policy controls. This limits opportunities for attackers to:
- Execute unauthorized software
- Abuse trusted applications
- Escalate privileges
- Move laterally across the network
- Access sensitive systems unnecessarily
For SOC teams, this simplifies investigations while also decreasing alert fatigue and burnout.
Instead of chasing every possible indicator, analysts can focus on activity that violates clearly defined policies and expected behavior.
The fewer actions attackers are allowed to perform, the fewer incidents your security team will ultimately need to investigate.
How ThreatLocker helps strengthen incident response
While incident response remains an essential part of every cybersecurity strategy, prevention plays an increasingly important role in limiting the impact of AI-powered attacks. ThreatLocker helps organizations strengthen their incident response strategy by reducing attacker freedom at every stage of an attack.
Application Allowlisting prevents unauthorized applications from executing, helping stop malicious software before it can establish a foothold.
Ringfencing™ restricts what trusted applications are allowed to do, limiting behaviors such as launching child processes, accessing sensitive files, or communicating with unauthorized applications.
Privileged Access Management removes standing administrator privileges, helping reduce opportunities for privilege escalation while allowing users to elevate only when appropriate.
Zero Trust Network Access and Zero Trust Cloud Access ensure users, devices, and applications can only access the resources they are explicitly authorized to use, helping limit lateral movement throughout the environment.
Together, these controls help reduce the scope of an incident, making investigations faster and containment more effective.
Incident response starts before the incident occurs
AI is reshaping both cyberattacks and cybersecurity operations.
Threat actors can automate attacks at unprecedented speed, while organizations continue expanding their environments with AI-powered applications and cloud services.
For SOC teams, effective incident response is no longer just about detecting known threats after compromise. It requires understanding application behavior, reducing the attack surface, and implementing security controls that prevent attackers from achieving their objectives in the first place.
Organizations that combine strong threat detection and response capabilities with a prevention-first, Zero Trust approach will be better positioned to contain AI-powered attacks before they become business-impacting incidents.
Because in the age of AI, the most effective incident response plan is one that leaves attackers with as little room to operate as possible.
