Incident Response in the Age of AI: Updating Your Playbook

Your IR Playbook Has a Blind Spot

Most enterprise incident response plans were written for a world of web applications, databases, and network infrastructure. They cover data breaches, ransomware, DDoS attacks, and unauthorized access. They are battle-tested and well-understood.

But AI systems introduce fundamentally different failure modes. Model poisoning doesn't look like a traditional breach. Adversarial inputs don't trigger conventional alerts. And when an LLM leaks sensitive data through its outputs, your existing detection rules won't catch it because they were never designed to.

MITRE recognized this gap by creating ATLAS (Adversarial Threat Landscape for AI Systems), a knowledge base specifically for AI-related adversarial techniques. If your IR team hasn't reviewed it yet, now is the time.

New Incident Categories Your Playbook Needs

Model Poisoning and Tampering

An attacker corrupts your training data or modifies model weights to change how the model behaves. The dangerous thing about poisoning attacks is that they can be subtle. The model might work perfectly on 99% of inputs while producing targeted misclassifications on specific inputs the attacker cares about.

Detection: Monitor model performance metrics continuously. Sudden accuracy drops or unexpected shifts in prediction distributions can indicate tampering. Maintain cryptographic hashes of model artifacts and training datasets to detect unauthorized modifications.

Containment: Immediately take the affected model offline. Roll back to a known-good model version. Quarantine the suspected training data for forensic analysis.

Adversarial Input Attacks

Carefully crafted inputs that cause models to produce incorrect outputs. In computer vision, this might be an image with imperceptible modifications that fools a classifier. In NLP, it might be specially formatted text that bypasses content filters or safety guardrails.

Detection: Log all model inputs and outputs. Use anomaly detection on input patterns to flag unusual requests. Monitor for repeated probe-like queries that might indicate someone testing your model's boundaries.

Containment: Implement rate limiting on inference endpoints. Add input validation layers that reject known adversarial patterns. Have a process for quickly deploying updated input filters when new attack vectors emerge.

LLM Data Leakage

Large language models can memorize and reproduce fragments of their training data. If that training data included sensitive information (customer records, internal documents, proprietary code), an attacker might be able to extract it through carefully constructed prompts.

Detection: Monitor LLM outputs for patterns that match sensitive data formats like credit card numbers, social security numbers, internal project names, or email addresses. Implement output filtering that catches known sensitive patterns before they reach the user.

Containment: Immediately restrict access to the affected model. Audit what data was included in training and assess the scope of potential exposure. Notify affected parties as required by your data breach notification obligations.

Supply Chain Compromise

A pre-trained model, open-source library, or third-party API that your AI system depends on gets compromised. This is analogous to traditional software supply chain attacks but with AI-specific vectors like backdoored model weights or poisoned training data in public datasets.

Detection: Maintain a complete inventory of all AI components including models, libraries, and external APIs. Monitor for security advisories affecting your dependencies. Validate model behavior against known benchmarks after any update.

Containment: Isolate the compromised component. Roll back to a previously validated version. Conduct a thorough review of any data processed by the compromised component during the exposure window.

Extending Your Existing Playbook

You don't need to start from scratch. The NIST SP 800-61 framework (Preparation, Detection, Containment, Eradication, Recovery, Lessons Learned) still applies. You just need to expand each phase for AI-specific considerations.

Preparation. Add AI systems to your asset inventory. Include model versioning, training data lineage, and inference endpoint documentation. Train your IR team on AI-specific threats. Conduct tabletop exercises that simulate AI incidents.

Detection. Extend your monitoring to cover model performance metrics, inference patterns, and output anomalies. Most SIEM tools don't natively support these signals, so you'll likely need custom integrations.

Containment. Develop playbooks for quickly rolling back to previous model versions and restricting access to inference endpoints. Model rollback should be as well-rehearsed as database failover.

Recovery. Plan for model retraining from clean data. This can take significantly longer than restoring a backup, so factor training time into your recovery time objectives.

AI as an IR Accelerator

The relationship between AI and incident response goes both ways. While AI introduces new threats, it also offers powerful tools for improving your response capabilities.

Automated triage. ML models can classify and prioritize incoming alerts, reducing the noise that SOC analysts deal with daily and surfacing the incidents that need immediate human attention.

Threat intelligence correlation. AI can process and correlate threat intelligence feeds at a scale that human analysts cannot, identifying patterns and connections across seemingly unrelated indicators.

Anomaly detection. Behavioral baselines established through machine learning can detect subtle deviations in network traffic, user behavior, and system activity that rule-based systems miss.

The Bottom Line

AI security incidents are not hypothetical. They're happening now, and the organizations that have updated their IR playbooks to account for them will respond faster and more effectively when an incident occurs.

Start by reviewing your current playbook against the MITRE ATLAS framework. Identify the gaps. Add AI-specific detection capabilities, containment procedures, and recovery plans. And make sure your IR team has the training to recognize and respond to these new threat categories.