A conventional security incident process, built around categories like data breach, malware, unauthorized access, often doesn't clearly capture AI-specific failure modes: a prompt injection that caused an agent to take an unauthorized action, a model producing harmful or policy-violating content at scale, an agent looping in a way that caused significant unexpected cost. Without explicit AI-specific incident categories and reporting triggers, these events risk falling into a gap where nobody's sure whether they should be reported, to whom, or how urgently.
Define AI-specific incident categories explicitly
Extend the organization's incident classification scheme to explicitly include AI-specific categories: successful prompt injection resulting in a policy violation or unauthorized action, data exposure through model output or retrieval, agent behavior significantly outside its intended scope, and AI-specific availability or cost incidents like a runaway agent loop. Define these categories explicitly, rather than expecting them to get reported under some loosely related conventional category, and it cuts the ambiguity about whether and how a given AI-specific event should get reported at all.
Set specific reporting triggers, not just general judgment
Rather than leaning solely on individual judgment about what counts as reportable, define specific triggers: any denied action attempt above a certain risk threshold, any confirmed successful injection regardless of whether it resulted in a completed unauthorized action, any single AI system exceeding a defined cost or resource anomaly threshold. Specific triggers cut the variance in whether similar events get reported consistently across different teams and different people with different personal thresholds for what feels worth escalating.
Build a clear, fast escalation path specific to AI incidents
Once an AI-specific incident gets identified, the escalation path needs to reach people who can actually assess and respond to AI-specific risk quickly, which might not be the same on-call rotation handling conventional infrastructure incidents. Confirm the escalation path for AI incidents reaches someone with the specific context to evaluate an AI-specific event's severity and appropriate response, rather than defaulting to a general security on-call rotation that has to research the specific system before it can assess anything meaningfully.
Severity classification needs AI-specific criteria
Adapt incident severity classification to account for AI-specific factors: whether the incident involved an action with real-world effect versus only generated content, how many users or how much data was potentially affected, whether the underlying vulnerability, a specific injection technique, a missing authorization check, is likely to recur if nobody fixes it immediately. A severity framework built entirely around conventional infrastructure incident patterns can misclassify an AI-specific incident's actual severity if it never adapts for this.
Coordinate with legal and communications for externally visible incidents
For incidents affecting customer-facing AI systems, coordinate incident response with legal and communications functions specifically for AI-related incidents, since customers and the public may have less established expectations for how organizations should communicate about an AI-specific failure compared to a conventional data breach. Getting ahead of this coordination before an incident happens produces a faster, more consistent response than figuring out the right approach for the first time during an active incident.
Track incident patterns over time, not just individual events
Beyond responding to individual incidents, keep a record of AI incident patterns over time: which systems generate the most reported incidents, which categories recur most often, whether specific mitigations put in after past incidents have actually cut recurrence. This pattern-level view supports prioritizing broader, systemic fixes instead of only addressing each individual incident in isolation.
Feed incident findings back into governance and risk classification
Every AI incident is a real-world data point about a system's actual risk, often more informative than a theoretical risk assessment completed before launch. Feed incident findings back into that system's risk classification and the organization's broader risk register, since an incident might reveal a system's actual risk profile differs from what got originally assessed, warranting reclassification and possibly additional controls.
Where TELEON fits
TELEON's audit trail, applied through the gateway or supported middleware, can surface many of the specific triggers described above, denied high-risk actions, unusual tool call patterns, cost or volume anomalies, directly from recorded activity, supporting faster identification of AI-specific incidents as they occur.
Defining the incident categories, escalation paths, and severity criteria, and coordinating the organizational response, remain governance and incident management processes that have to be designed and operated directly by the organization.
A short checklist
- Define explicit AI-specific incident categories, separate from
conventional security categories.
- Set specific reporting triggers rather than relying solely on
individual judgment.
- Build an escalation path that reaches people with AI-specific
assessment context quickly.
- Adapt severity classification to account for AI-specific factors.
- Coordinate legal and communications planning for externally visible
AI incidents in advance.
- Track incident patterns over time, not just individual events.
- Feed incident findings back into risk classification and governance.
AI-specific incident reporting and escalation needs its own explicit categories, triggers, and paths, since a conventional security incident process built around different failure patterns tends to leave AI-specific events in an ambiguous gap where reporting depends on inconsistent individual judgment rather than a clear, defined process.