Adapting Incident Response for AI: Key Strategies for Organizations
AI incidents are a growing concern, leading to a significant shift in how organizations must rethink their incident response (IR) strategies. According to the 2026 CISO AI Risk Report, 71% of companies allow AI access to critical business systems, yet only 16% manage that access adequately. This discrepancy highlights the urgent need for organizations to reassess their incident response frameworks specifically tailored for AI.
Having spent over a decade in the security field across various sectors—including energy, banking, and telecom—I’ve observed a recurring issue: businesses often believe their existing IR playbooks adequately cover AI-related incidents, but this assumption frequently proves false upon closer examination.
Understanding AI Incident Types
AI incidents rose by 56.4% from 2023 to 2024, with 233 documented cases. Most traditional IR frameworks, like NIST SP 800-61, MITRE ATLAS, and the GLACIS AI Incident Response Playbook, categorize incidents into six types, but fail to address a more critical division: model-originated failures versus those induced by human actions. These categories require distinct detection and containment strategies.
Model-originated failures, such as biases and inaccuracies, can occur during normal operation of AI systems. For example, the Epic Sepsis Model, used extensively across U.S. hospitals, failed to identify two-thirds of actual sepsis cases during validation, resulting in an alarming percentage of missed diagnoses without outward signs of malfunction.
On the other hand, externally induced failures arise from factors like data manipulation or adversarial attacks, as seen with Tesla's Autopilot. In such scenarios, inappropriate inputs can lead to serious safety concerns. Then there's the hybrid scenario, blending both types of failures, which currently carries the heaviest legal implications—such as when Air Canada’s chatbot fabricated a policy, making the airline legally accountable despite no direct security breach.
The Limitations of Traditional Frameworks
Conventional incident response models rooted in the CIA triad (confidentiality, integrity, availability) often fall short when confronted with AI challenges. For instance, when a chatbot inaccurately relays information, it doesn’t disrupt availability or integrity in a traditional sense, yet the impacts can be significant. This highlights the inadequacy of classical IR frameworks that were not designed for probabilistic AI systems.
Current statistics underscore this gap; the average time to detect an AI incident is around 4.5 days, with 67% of these incidents stemming from model errors rather than cyber attacks. This discrepancy shows that organizations are primarily investing in perimeter security tools, misaligned with the nature of AI incidents.
Building a Robust AI Incident Response Capability
To effectively address AI incidents, organizations should cultivate several critical components before a crisis strikes.
The first is an AI Bill of Materials (AIBOM) for every production AI system. This document, akin to a software SBOM, details aspects like the underlying model and training data. Understanding these components is essential for probing incidents related to model failures or data contamination.
The second element is the creation of model cards for production AI systems that are easily accessible during incidents. This should include valuable details such as training data sources, performance thresholds, and known biases—information typically written for data scientists but rarely usable in real-time security scenarios.
Additionally, having a data scientist on the incident response call tree can provide immediate expertise on model behavior under duress, a role analogous to having a network engineer during traditional IR incidents.
Finally, teams should establish documented rollback thresholds for their models. Clear criteria for containment or fallback actions can significantly reduce response time during incidents.
Essential Actions Before the Next Incident
Organizations must proactively evaluate and enhance their detection triggers for AI systems. Implementing anomaly scoring and drift monitoring will be crucial, as traditional security information and event management (SIEM) tools won’t suffice for this layer of detection.
Equally important is the need to redefine containment strategies. Instead of taking AI systems offline, preserving operations while switching to rule-based alternatives may mitigate fallout. Each model should have agreed-upon rollback criteria documented to facilitate swift decision-making during crises.
It’s vital to engage legal teams preemptively, especially as legal responsibilities concerning AI incidents are increasingly accepted in court. Organizations like Air Canada exemplify the necessity of having legal minds involved before a public relations crisis arises.
Finally, crafting an AI inventory that parallels asset registers can serve as a foundational tool for risk assessments. Focusing on high-risk systems with customer data access or those critical to operational integrity will help ensure preparedness in the event of an incident.
As AI continues to envelop various sectors, organizations must remember that readiness involves updating playbooks—not just a one-time effort but an ongoing process. With a significant percentage of businesses already encountering AI-related incidents, proactive measures to enhance understanding and response capabilities aren’t just advisable; they're essential.
This article is published as part of the Foundry Expert Contributor Network.
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