Introducing AI-IRF: A New Framework for AI Incident Response
- barksdale2004
- Nov 26
- 5 min read
Traditional incident response wasn't built for AI. It's time for a new approach.
As organizations race to deploy artificial intelligence systems, a critical gap has emerged in our security posture: we're protecting AI with frameworks designed for conventional IT infrastructure. Model manipulation, training data poisoning, adversarial inputs, prompt injection—these aren't edge cases anymore. They're the new attack surface, and they require a fundamentally different response methodology.
That's why I developed AI-IRF (Artificial Intelligence Incident Response Framework)—a structured, repeatable approach to preparing for, detecting, and responding to security incidents targeting AI systems.
Why Traditional IR Falls Short
When a threat actor poisons your training data, your standard incident response playbook doesn't have an answer. When an adversarial input causes your production model to behave unexpectedly, the NIST Incident Response lifecycle—while excellent for traditional IT—leaves critical questions unanswered:
How do you "contain" a compromised model that's already been serving predictions?
What does "eradication" look like when the threat lives in your model weights?
How do you validate that a retrained model is actually clean?
AI-IRF addresses these gaps by extending proven incident response principles with AI-specific procedures, threat taxonomies, and maturity benchmarks.
The AI-IRF Lifecycle: Seven Phases
AI-IRF organizes incident response into seven interconnected phases that form a continuous improvement cycle:
1. PREPARE
Establish AI-specific incident response capabilities before incidents occur. This means developing playbooks tailored to AI threats, building cross-functional teams that bridge Security and ML Engineering, implementing baseline monitoring for model behavior, and creating rollback capabilities for rapid model reversion.
2. DETECT
Identify anomalous AI system behavior through continuous monitoring. Detection signals include performance degradation (accuracy drops, latency spikes), behavioral anomalies (unexpected outputs, bias emergence), data pipeline alerts (unusual volumes, schema violations), and infrastructure signals (unauthorized API access, compute anomalies).
3. ANALYZE
Investigate the scope, impact, and root cause of the incident. Determine which models and datasets are affected, construct an attack timeline, identify the attack vector, assess business impact, and classify the threat category.
4. CONTAIN
Limit incident spread by isolating affected AI components. Containment strategies include taking compromised models offline, implementing rate limiting or input filtering, freezing data pipelines to prevent further poisoning, and revoking compromised credentials.
5. ERADICATE
Remove the threat through model retraining from validated clean data, dataset cleansing, backdoor removal, vulnerability patching, and enhanced control implementation.
6. RECOVER
Restore AI systems to full operational status through validated model deployment, staged traffic restoration with enhanced monitoring, performance verification, and stakeholder communication.
7. LEARN
Document lessons learned, conduct root cause analysis, update playbooks, improve detection capabilities, and share anonymized findings with the security community.
AI Threat Taxonomy: Classifying the Attack Surface
Effective incident response requires rapid threat classification. AI-IRF categorizes threats across four primary attack surfaces:
AT-1: Model Integrity Attacks
Attacks compromising the integrity of AI models themselves:
Model Theft — Unauthorized extraction of model weights or architecture
Model Tampering — Modification of model weights to alter behavior
Backdoor Injection — Embedding hidden triggers for malicious behavior
Model Inversion — Reconstructing training data from model outputs
AT-2: Data Pipeline Attacks
Attacks targeting training, fine-tuning, or inference data:
Training Data Poisoning — Injecting malicious samples to influence behavior
Label Manipulation — Altering ground truth to cause misclassification
Data Exfiltration — Theft of training data including PII
Supply Chain Compromise — Attacks on third-party data sources or pretrained models
AT-3: Inference-Time Attacks
Attacks exploiting model behavior during inference:
Adversarial Inputs — Crafted inputs causing misclassification
Prompt Injection — Manipulating LLM prompts to bypass controls
Jailbreaking — Circumventing safety alignment
Membership Inference — Determining if specific data was used in training
AT-4: Infrastructure Attacks
Attacks targeting underlying AI infrastructure:
API Exploitation — Exploiting model serving vulnerabilities
Compute Resource Abuse — Hijacking GPU/TPU resources
Model Registry Tampering — Deploying malicious model versions
Orchestration Compromise — Attacking MLOps platforms
The AI-IRF Maturity Model
Knowing where you stand is the first step to improvement. The AI-IRF Maturity Model provides a five-level framework for assessing and advancing your AI incident response capabilities:
Level | Name | Characteristics |
1 | Initial | Ad-hoc responses, no AI-specific procedures, reactive only |
2 | Developing | Basic documentation, initial monitoring, some trained personnel |
3 | Defined | Formal playbooks, dedicated team, comprehensive monitoring |
4 | Managed | Metrics-driven, automated detection, integrated with MLOps |
5 | Optimizing | Proactive threat hunting, continuous improvement, industry leadership |
Maturity Assessment Dimensions
Organizations should assess their maturity across six dimensions:
Governance — Policies, roles, accountability, compliance alignment
People — Team structure, skills, training, cross-functional collaboration
Process — Playbooks, workflows, escalation procedures
Technology — Monitoring tools, automation, MLOps integration
Measurement — KPIs, metrics collection, continuous improvement
Intelligence — Threat awareness, vulnerability tracking, industry collaboration
Your overall maturity level is determined by your lowest dimension score—capabilities must be balanced to be effective.
Getting Started with AI-IRF
Implementing AI-IRF doesn't require a massive transformation. Start with these foundational steps:
Conduct an AI asset inventory — Identify all models, datasets, and infrastructure
Perform a baseline maturity assessment — Evaluate your current state across all dimensions
Identify high-risk AI systems — Prioritize protection efforts based on business impact
Develop initial playbooks — Start with the most likely incident scenarios
Implement basic monitoring — Track model performance and data pipeline health
Establish a cross-functional team — Bridge Security and ML Engineering
Run a tabletop exercise — Test your initial procedures
Define success metrics — Establish a quarterly review cadence
Framework Alignment
AI-IRF is designed to complement, not replace, existing security frameworks:
NIST Cybersecurity Framework — Maps to Identify, Protect, Detect, Respond, Recover functions
NIST AI RMF — Extends risk management with operational response procedures
MITRE ATT&CK — AI threat taxonomy extends ATT&CK with AI-specific techniques
ISO 27001 — Integrates with existing ISMS for AI-inclusive security management
OWASP ML Top 10 — Provides response context for common ML vulnerabilities
The Bottom Line
AI systems are becoming critical infrastructure. They're making decisions that affect lives, businesses, and society. Yet most organizations are flying blind when it comes to AI security incidents—relying on generic IT playbooks that don't address the unique characteristics of machine learning systems.
AI-IRF provides the structured methodology security teams need to protect these systems effectively. Whether you're just beginning to think about AI security or looking to mature an existing program, the framework offers a clear path forward.
The question isn't whether your AI systems will face a security incident. It's whether you'll be ready when it happens.
The complete AI-IRF Framework document, including detailed procedures, checklists, and assessment tools, is available for download. If you're interested in implementing AI-IRF in your organization or contributing to the framework's development, I'd love to connect.
About the Author
Larry Barksdale is a cybersecurity executive with over two decades of experience leading incident response and threat operations at the highest levels of financial services and federal institutions. For the past three years, he has focused exclusively on the emerging discipline of Artificial Intelligence Incident Response, pioneering methodologies to address the unique security challenges posed by AI and machine learning systems.
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