Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Proactive technical playbooks for AI-specific failures, from model drift to regulatory breaches.
When an AI system fails—due to model drift, an adversarial attack, or a bias incident—your response must be immediate, precise, and defensible. Ad-hoc reactions create regulatory exposure and erode stakeholder trust. We build your technical first-responder capability.
We engineer specialized incident response playbooks that turn crisis into a controlled, documented procedure, ensuring compliance and preserving system integrity.
SOC and MLOps pipelines.Move from reactive panic to governed response. Ensure your teams are equipped not just to fix the AI, but to document the why and how for auditors. This discipline is core to mature AI Governance and Compliance Frameworks.
Our AI Incident Response Planning service delivers concrete, technical outcomes that reduce risk, ensure compliance, and maintain operational continuity. We move beyond theoretical frameworks to implement actionable playbooks that produce verifiable results.
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.
Our AI Incident Response Planning service delivers a complete technical and procedural framework, moving from assessment to operational readiness. This table outlines the key deliverables and typical timeline for each engagement tier.
| Deliverable / Phase | Rapid Assessment | Comprehensive Planning | Managed IR Program |
|---|---|---|---|
Initial Risk & Maturity Assessment | |||
AI-Specific Incident Classification Taxonomy | |||
Technical Runbooks for Top 5 AI Failure Modes | 3 runbooks | 8-10 runbooks | 15+ runbooks |
Regulatory Breach Playbook (EU AI Act, etc.) | |||
Integrated Drift & Bias Detection Alerting | |||
Tabletop Exercise & Team Training | 1 session | 2 sessions | Quarterly sessions |
Integration with AI Governance Dashboard | |||
Continuous Playbook Updates (12 months) | |||
Dedicated On-Call Technical Support | 24/7 Priority | 24/7 Dedicated SME | |
Typical Engagement Timeline | < 2 weeks | 4-6 weeks | Ongoing Program |
AI failures carry unique, sector-specific consequences. Our incident response planning is tailored to the distinct technical, regulatory, and operational risks faced by industries where AI is mission-critical.
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.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers on how we build technical runbooks and playbooks for AI-specific failures, from adversarial attacks to regulatory breaches.
Our playbooks are technical runbooks, not policy documents. Each includes:
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
Incident response playbooks become a living component of your broader AI Governance Dashboard, providing real-time visibility into system health and compliance status.
