As AI integration increases, so does the need for specialized incident response protocols. While the core process mirrors general cyber security, it must address vulnerabilities unique to artificial intelligence.
"AI incident response forms a continuous cycle. Post incident output always feeds back into the preparation phase, improving future responses."
The 5-Phase Model
1. Prepare
The foundation of a strong response. This involves establishing clear protocols for AI-specific attacks that standard measures might miss.
Specific Risks: Plan for prompt injection, data poisoning, and adversarial interference.
Strategy: Set up detection and mitigation strategies in advance.
2. Identify and Report
Quick and accurate identification is crucial to minimize damage.
Signs of Compromise: Look for unexpected model behavior or performance degradation.
Reporting: Employees must know exactly how and to whom to report suspected incidents for rapid escalation.
3. Assess
Determine the depth and impact of the incident.
Scope Analysis: Determine which systems and datasets were affected.
Root Cause: Identify if the issue stems from data corruption, model manipulation, or inherent algorithm flaws.
4. Response
Action must be swift and targeted.
Mitigation: Isolate affected models or roll back to a known secure version.
Recovery: Contain the damage and systematically restore full operational capacity.
5. Post Incident
Learning from the incident is vital to complete the cycle.
Documentation: Analyze what went wrong and capture all lessons learned.
Feedback Loop: Feed insights back into the "Prepare" phase to strengthen defenses for the future.
