Artificial intelligence is rapidly changing how organizations access, analyze, and use information. Tools such as Microsoft Copilot, AI-powered assistants, automated workflows, and intelligent search capabilities are helping businesses improve productivity and decision-making. However, these same technologies are introducing new categories of security risks that traditional cybersecurity frameworks were never designed to address.
For decades, incident response programs focused on malware infections, unauthorized access, phishing campaigns, ransomware attacks, and other conventional cyberattacks. Security teams developed mature processes based on frameworks such as NIST SP 800-61, the widely adopted Computer Security Incident Handling Guide, which provides a structured methodology for preparing for, detecting, containing, and recovering from security incidents.
Today, however, organizations face a different challenge. AI systems can expose sensitive information without malware ever being deployed. Employees can unintentionally access confidential data through AI-powered tools. Automated workflows can move information across systems without proper oversight. These risks require organizations to rethink Incident Response for AI and adapt existing frameworks to address modern data-centric threats.
The good news is that NIST remains highly relevant. Rather than replacing existing methodologies, organizations can extend and modernize them to address AI-driven environments. This article explores how Incident Response for AI can be built by adapting NIST SP 800-61 to manage emerging risks associated with AI adoption.
Traditional incident response was designed around events that generated clear technical indicators.
Examples include:
These incidents typically trigger alerts through technologies such as:
Security teams then follow established incident response procedures to investigate, contain, remediate, and recover.
AI changes this model.
Many AI-related incidents do not begin with malicious code or external attackers.
Instead, risks may involve:
In these scenarios, traditional incident detection methods may not immediately identify a problem because no classic attack signature exists.
This is why organizations must rethink Incident Response for AI through a data-centric lens.
The foundation of modern incident response within many organizations is NIST SP 800-61, developed by the National Institute of Standards and Technology.
The framework defines four key phases:
These phases form the basis of the incident response lifecycle used by government agencies, regulated industries, and enterprises worldwide.
The publication is closely aligned with:
The latest guidance found in NIST SP 800-61r3 expands the framework's applicability and reflects evolving cybersecurity challenges.
For AI environments, the framework remains valuable because its structure is adaptable. The key is redefining each phase to account for AI-related risks.
The first phase of Incident Response for AI begins with preparation.
Historically, preparation focused on:
In AI environments, preparation must expand significantly.
Organizations should establish:
An effective incident response plan should explicitly include AI-related scenarios.
Examples include:
Preparation should also include ongoing risk assessments that evaluate how AI systems interact with sensitive business data.
Organizations operating under frameworks such as HIPAA, FISMA, and other regulatory compliance requirements should ensure AI governance is integrated into existing compliance programs.
Ne Digital often begins AI security engagements with readiness assessments that evaluate data exposure risks, governance maturity, and AI adoption readiness before deployment occurs.
The second phase of the incident response lifecycle is Detection and Analysis.
Traditionally, this stage focused on identifying:
In AI environments, organizations must broaden the definition of suspicious activity.
The detection and analysis process should include monitoring:
For example, an employee may use an AI assistant to summarize documents that contain confidential financial information.
No malware exists.
No traditional indicators of compromise appear.
Yet the organization may still face a serious security event.
This requires new monitoring approaches.
Organizations should leverage:
A modern SIEM environment should aggregate telemetry not only from infrastructure but also from AI-related activities.
This expanded visibility improves AI-related incident detection and supports faster triage.
Effective triage remains one of the most critical components of incident response.
Security teams must rapidly determine:
AI incidents often present unique challenges because they may not involve traditional attack vectors.
For example:
The ability to perform effective triage requires visibility into both user behavior and AI system activity.
Organizations should update their incident response policies to include AI-specific investigation procedures that help analysts classify and prioritize these incidents appropriately.
The third phase of Incident Response for AI involves containment.
In traditional security incidents, containment may include:
AI incidents require a different approach.
Containment may involve:
The objective is to prevent additional exposure while preserving evidence for investigation.
Organizations should develop AI-specific incident response procedures that clearly define how access restrictions are applied during an active event.
Strong governance frameworks significantly improve containment effectiveness because policies already exist before incidents occur.
The next step in containment, eradication, and recovery is eradication.
Traditional eradication often focuses on removing malware, closing vulnerabilities, or eliminating unauthorized access.
In AI-related incidents, eradication frequently targets governance weaknesses rather than technical infections.
Common eradication activities include:
The root cause may not be malicious activity.
Instead, it may stem from:
Organizations should conduct detailed root cause analysis to understand why exposure occurred and how similar events can be prevented in the future.
The recovery portion of containment, eradication, and recovery focuses on restoring normal operations safely.
In AI environments, recovery should include:
Recovery is not simply about restoring system functionality.
It is about ensuring AI systems operate within approved security boundaries.
Organizations should confirm that:
Many organizations also use tabletop exercises to validate recovery plans and test readiness for future AI incidents.
The final phase of the incident response lifecycle is post-incident activity.
This phase remains just as important in AI environments as it is in traditional cybersecurity.
Organizations should document:
The goal is to generate meaningful lessons learned that strengthen future defenses.
Effective post-incident analysis helps organizations identify recurring patterns and systemic weaknesses.
For AI incidents, organizations should specifically evaluate:
These findings should feed directly into future preparation activities, creating a continuous improvement cycle.
Modern Incident Response for AI requires integration between governance processes and security operations.
Organizations should align AI incident response with:
Security teams should also coordinate with:
This collaboration helps ensure AI-related incidents are managed consistently across the organization.
Modern security platforms remain essential for supporting Incident Response for AI.
SIEM solutions provide centralized visibility and correlation across Microsoft environments and cloud services.
EDR technologies help identify endpoint-related threats that may intersect with AI workflows.
XDR and Extended Detection and Response capabilities expand visibility across:
These technologies enable organizations to investigate AI-related attack vectors while maintaining a broader understanding of organizational risk.
They also support digital forensics investigations when AI-related incidents require deeper analysis.
Perhaps the most important lesson for organizations adapting NIST is that governance must become part of incident response.
Many AI incidents originate not from external attackers but from:
Traditional security tools alone cannot solve these issues.
Organizations must establish governance frameworks that define:
Without governance, even the strongest technical controls may prove insufficient.
The strength of NIST SP 800-61r3 is its flexibility.
The framework does not prescribe specific technologies. Instead, it provides a structured methodology that organizations can adapt as threats evolve.
By extending preparation, monitoring, containment, eradication, recovery, and post-incident activities to include AI-specific risks, organizations can continue using the framework effectively in modern environments.
Rather than replacing NIST, organizations should modernize it.
This approach allows them to maintain consistency with existing incident management practices while addressing emerging AI-related challenges.
Artificial intelligence is transforming how organizations work, but it is also introducing new security risks that traditional cybersecurity models were not designed to address.
Data leakage through AI outputs, unauthorized access via AI tools, workflow misuse, and governance failures require organizations to rethink Incident Response for AI while preserving the proven structure of established frameworks.
Fortunately, NIST SP 800-61 remains highly relevant. By adapting its phases—preparation, detection and analysis, containment, eradication, and recovery, and post-incident activity—to address AI-specific risks, organizations can build a modern and effective response capability.
At ne Digital, we help organizations operationalize these adaptations through AI readiness assessments, governance frameworks, continuous monitoring strategies, and Microsoft security solutions. Our approach enables organizations to extend proven NIST methodologies into AI environments while maintaining visibility, control, compliance, and resilience as AI adoption continues to grow.