In the age of AI, incident response is becoming a wholly different activity for security teams. Just a few years ago, a cybersecurity incident was almost always an attack or insider threat with a human behind it. At the Gartner Cybersecurity and Risk Management Summit 2026 in National Harbor, Md., analyst Craig Porter explained that internal AI agents are now commonly generating unintended events that must be managed by CISOs and their teams.
"At least 80% of unauthorized AI transactions will be caused by internal violations of enterprise policies concerning information oversharing, unacceptable use or misguided AI behavior rather than malicious attacks," Porter said.
In his session, Porter identified three key issues Gartner consistently sees:
- No shared definition of an AI incident. Agents might generate incidents due to model drift, prompt injection or autonomous agents doing things they were never architected to do.
- Risks are invisible. Many significant risks are beyond the SOC's observability, requiring greater oversight outside the traditional perimeter.
- Reactive response no longer scales. AI is moving so quickly that by the time teams investigate systems, it might already have made thousands of decisions.
The session reinforced that the CISO's role is dynamic, with responsibilities shifting as swiftly as the threat landscape. Because AI can cause systems to behave in ways with far-reaching consequences for businesses, Porter recommended that CISOs overhaul incident response protocols to account for the technology's complex role in enterprise cybersecurity.
Define the AI incident taxonomy
With a host of new AI-fueled events, organizations need to define -- or redefine -- what constitutes an AI cybersecurity incident and evolve playbooks to align with that definition. AI systems can be compromised, misused or fail in ways that affect security, privacy and operations.
Gartner has found that CISOs still struggle to clearly categorize these blurry areas and need to expand taxonomies to include AI threats, prompt injection, data and model poisoning, bias exploitation, deepfakes and more. Porter said that teams need to develop new AI playbooks with dedicated roles to handle internal and insider risk, third-party threats and external AI incidents.
Focus on incident resilience
"We're seeing a shift from incident response to resilience. The key takeaway here is that traditional incident response no longer scales," Porter said. "AI incidents force us to investigate behavior, design and decision-making."
In an AI era, incident response requires a broader charge with predefined AI escalation protocols based on regulatory and technical severity, clear system restoration processes and new AI-specific metrics. CISOs also need to define triaged cross-functional representation -- legal, model owners, compliance, HR and business owners.
Apply continuous oversight
AI behavior is dynamic and oversight cannot be periodic. Porter stressed the importance of logging AI transactions and applying third-party controls. Expanded observability can include model and system artifacts, decision and behavior evidence, data flow and lineage, shadow AI responses, telemetry and API-based policy enforcement. To account for third-party risks, Porter also recommended integrating AI triage into vendor risk workflows.
The AI era requires CISOs to fundamentally rethink what constitutes a cybersecurity incident and how to handle it once identified. As security teams recognize that authorized AI models pose risks, preparation will be vital in the form of regular cross-functional training, tabletop exercises, disaster recovery and business continuity planning.
"There may be no attacker here. That's the fundamental challenge of AI. The system is behaving as it was authorized to, but it's still creating risk," Porter said.
Richard Livingston is an editor with Informa TechTarget's SearchSecurity site, covering cybersecurity news, trends and analysis.
