15.3.1 AI-specific incident handling

When an AI system is the source of an incident, the entire response framework shifts. The asset under attack isn’t just a server or a database; it’s the model’s integrity, the data it learns from, and the logic it executes. Traditional incident response (IR) focuses on infrastructure compromise. AI incident response (AI-IR) must address logical compromise. Let’s walk through this case to see how.

Phase 1: Triage and Initial Analysis

Your first instinct might be to check the application’s front-end or the data visualization pipeline. This is a critical first step—rule out the simple explanations. In this scenario, your team quickly confirms the data pipeline is flowing and the dashboard is rendering correctly. The problem lies with the model’s outputs themselves.

The AI Incident Response Team (AI-IRT), a specialized subset of your main IR team, takes over. Their first questions are fundamentally different:

• Model Versioning: When did the anomalous behavior start? Correlate this with the deployment timeline of the current model version.
• Input Drift: Has the nature of the input data changed dramatically? Did the client’s product launch coincide with a new slang term or meme that the model misinterprets?
• Output Monitoring: What do the runtime monitors (Chapter 15.2.4) show? Are we seeing a sudden spike in confidence scores for “Positive” classifications on this specific topic? Is the distribution of outputs skewed?

The team finds that the anomalous behavior began three days ago, shortly after the weekly model retrain-and-deploy cycle. Input data drift analysis shows nothing unusual. The problem is isolated to the model’s behavior concerning one client’s product keywords.

Phase 2: Containment – Isolating the Logic, Not the Host

In traditional IR, containment means isolating a compromised host from the network. For an AI incident, you must isolate the compromised *logic* from the production environment. You can’t just unplug the server; that causes a total outage. The goal is service continuity with mitigated risk.

Your immediate containment options include:

1. Model Rollback: The fastest and most effective first step. Revert the production endpoint to the previously known-good model version. This immediately stops the bleeding and restores service integrity for the client. (We’ll cover this strategy in detail in 15.3.2).
2. Rule-Based Override: If a rollback isn’t possible, implement a temporary, hard-coded rule. For example: “IF text contains ‘ProductXYZ’ AND source is ‘SocialMediaPlatformA’, THEN flag for manual review.” This is a crude but effective stopgap.
3. Canary Environment Analysis: Keep the faulty model running in an isolated canary environment, feeding it a stream of production data. This allows your team to perform live forensics without impacting customers.

The team executes a model rollback. The client’s sentiment reports immediately return to expected accuracy. The crisis is contained, but the investigation has just begun.

Phase 3: Eradication – Sanitizing the Data, Not the Disk

With the faulty model in the analysis environment, the team begins to hunt for the root cause. They suspect data poisoning: an attacker intentionally introduced mislabeled data into the training set to manipulate the model’s behavior.

Eradication isn’t about running an antivirus scan. It’s about finding and removing the toxic data that taught the model its flawed logic. The team’s process involves:

• Data Provenance Audit: Tracing the source of all new data added to the training set in the last cycle.
• Labeling Anomaly Detection: Searching for patterns in the mislabeled data. Were they all from a specific user group? A specific time window? Did they use similar phrasing?
• Influence Functions: Using techniques to identify which specific training examples had the most influence on the model’s incorrect predictions about ‘ProductXYZ’.

They discover a small cluster of accounts that posted subtly negative comments about ‘ProductXYZ’, which were then mislabeled as “Positive” by a compromised third-party data labeling service. The poisoning was low-and-slow, designed to be missed by basic statistical checks.

The eradication step is to revoke access for the compromised labeling service, flag all data from that source for removal, and implement stricter cross-validation checks on all incoming training data.

# Pseudocode for a new data validation check
function validate_new_data(data_point):
    # Check 1: Labeler reputation score
    labeler_score = get_labeler_reputation(data_point.source)
    if labeler_score < MIN_TRUST_SCORE:
        quarantine_data(data_point, reason="Low reputation source")
        return False

    # Check 2: Heuristic keyword mismatch
    contains_negative_words = any(word in data_point.text for word in NEGATIVE_KEYWORDS)
    if data_point.label == "Positive" and contains_negative_words:
        quarantine_data(data_point, reason="Label/keyword mismatch")
        return False
    
    return True
            

Phase 4: Recovery and Post-Mortem

Recovery involves more than just restoring from a backup. You must rebuild the compromised asset—the model itself.

1. Sanitize the Dataset: Purge all identified poisoned data from the master training set.
2. Retrain the Model: Train a new model version from scratch using the cleaned dataset.
3. Rigorous Validation: Test the new model extensively against a “golden”, manually curated test set, paying special attention to the keywords targeted in the attack.
4. Phased Redeployment: Deploy the new, validated model to production, closely monitoring its performance.

The post-incident analysis (Chapter 15.3.4) yields critical updates to the AI-IR playbook. The incident exposed gaps in data supply chain security and highlighted the need for continuous, adversarial-aware monitoring. Your defense strategy is now stronger because you’ve learned to treat the model and its data as a primary incident response battleground, not just a secondary application running on your servers.