AI Incident Response Planning

Pre-defined technical runbooks for specific failure modes (model drift, adversarial attacks) enable engineering teams to diagnose and remediate incidents in hours, not days. This minimizes system downtime and business impact.

Automated, immutable logging of all incident response actions creates a defensible audit trail for regulators (EU AI Act, NIST AI RMF). Demonstrate due diligence and structured governance during audits.

Rapid containment protocols for bias incidents or data breaches limit exposure. Quantifiable reduction in potential fines, litigation costs, and brand damage associated with unmanaged AI failures.

Clear role definition (SRE, Legal, Compliance) and communication protocols eliminate confusion during crises. Technical runbooks integrate seamlessly with your existing ITIL or DevSecOps workflows.

Structured root cause analysis feeds directly into model retraining pipelines and architecture improvements. Each incident strengthens system defenses, turning failures into long-term robustness gains.

Incident response playbooks become a living component of your broader AI Governance Dashboard, providing real-time visibility into system health and compliance status.

Real-time response for algorithmic trading failures, fraud detection model drift, and regulatory breaches (e.g., Reg BI, AML). We develop playbooks for immediate model rollback, transaction freezing, and mandated reporting to agencies like the SEC and FINRA.

Key Differentiator: Integration with existing SOX and SOC 2 controls.

Specialized runbooks for clinical decision support errors, diagnostic imaging model bias incidents, and HIPAA/GDPR data breaches from AI processing. Ensures patient safety, maintains care continuity, and manages communications with regulatory bodies (FDA, EMA).

Key Differentiator: Experience with FDA SaMD (Software as a Medical Device) incident protocols.

Air-gapped, sovereign incident response for autonomous systems, intelligence analysis models, and secure communications AI. Playbooks address adversarial attacks (data poisoning, model evasion), integrity failures, and controlled degradation in contested environments.

Key Differentiator: Designs compliant with NIST SP 800-171, CMMC, and ITAR requirements.

Safety-critical response for perception model failures, planning algorithm errors, and V2X communication breaches in autonomous vehicles (AVs) and ADAS. Procedures align with ISO 21448 (SOTIF) and ISO/SAE 21434 cybersecurity standards for immediate operational design domain (ODD) limitation.

Key Differentiator: Coordination with NHTSA recall and reporting processes.

Containment and remediation for AI failures in contract analysis, e-discovery, and predictive litigation. Protects attorney-client privilege, manages disclosure obligations, and contains erroneous legal advice generation from RAG systems or DSLMs.

Key Differentiator: Playbooks integrate with legal hold processes and state bar ethical guidelines.

Incident management for AI used in benefit allocation, public safety forecasting, and citizen services. Addresses algorithmic fairness incidents, transparency failures under open government laws, and voter/citizen data breaches with public communication protocols.

Key Differentiator: Compliance with OMB AI Memos, State-level AI Acts, and public records request handling.