Prepare, respond, investigate and learn from AI-related incidents before they become unmanaged business risk.
Book ConsultationAI incidents can arise from inaccurate outputs, unsafe recommendations, biased decisions, weak human oversight, data misuse, supplier failure or unexpected system behaviour. Without a clear incident management process, organisations may respond too slowly, escalate inconsistently or fail to capture the evidence needed for governance, legal, regulatory and assurance purposes.
AI incident management is the discipline of identifying, triaging, escalating, containing, investigating and learning from AI-related incidents. It brings structure to how incidents are classified, who makes decisions, when systems should be suspended or restricted, how evidence is retained and how corrective actions are tracked through to completion.
INTAI helps organisations build practical AI incident management arrangements that work alongside existing operational, technology, data protection and risk processes. Our approach defines incident categories, severity levels, escalation routes, response roles, supplier responsibilities and post-incident governance reporting.
The result is a clearer, faster and more accountable response to AI-related incidents, with stronger evidence, better decision-making and a structured route for remediation and lessons learned.
Establish what qualifies as an AI incident, including operational failure, harmful output, control breakdown, supplier issue, bias concern or oversight failure.
Create severity levels that reflect customer impact, operational disruption, regulatory exposure, data risk, financial impact and reputational sensitivity.
Define who is notified, who decides, when legal or compliance input is required and when an AI system should be restricted, paused or withdrawn.
Review the incident evidence, affected systems, failed controls, supplier involvement, user impact and decisions taken during the response.
Track corrective actions, update procedures and report lessons learned so incident findings improve future governance and control effectiveness.
Practical outputs aligned to the incident management process, from classification and escalation through to investigation, remediation and executive reporting.
Take the AI Risk Assessment™ to understand where AI use may create operational, data, customer, financial and reputational exposure, and whether your organisation has the awareness, ownership and escalation foundations needed to respond when something goes wrong.
Take Assessment Book ConsultationAn AI incident is an event in which an AI system, AI-enabled process or use of AI causes, contributes to or creates a credible risk of harm, disruption, significant error or unacceptable business impact. This can include unsafe or inaccurate outputs, discriminatory outcomes, data misuse, loss of human control, unauthorised AI use, supplier failures or breakdowns in expected safeguards.
Relevant scenarios can include harmful or materially incorrect outputs, discriminatory outcomes, inappropriate automated decisions, confidential data exposure, security events, hallucinated content used in important work, model or service failure, unauthorised AI use, inadequate human intervention and failures involving third-party AI suppliers.
Accountability should be defined before an incident occurs. Depending on the issue, response may involve the accountable business owner, technology, AI governance, risk, compliance, legal, data protection, information security, communications and senior management. Clear decision rights are particularly important where an AI system may need to be restricted or suspended quickly.
AI incidents can overlap with technology and cyber incidents but may involve different failure modes. An AI system can operate exactly as technically designed while still producing harmful, unfair, misleading or inappropriate outcomes. Incident processes therefore need to consider system behaviour, decision quality, human oversight, data, affected people and business impact as well as security and availability.
Suspension should be considered where continued operation could create unacceptable harm or exposure and the issue cannot be adequately contained through existing controls. Organisations should define authority for restricting, disabling or withdrawing AI in advance rather than attempting to establish decision rights during a serious incident.
Reporting obligations depend on the nature of the incident, the AI system, the organisation's role, jurisdiction and applicable sector requirements. Potential notification duties should therefore form part of the incident assessment process, with qualified legal or regulatory advice obtained where formal interpretation is required.
Supplier responsibilities should be established through due diligence, contracts and operating procedures before deployment. During an incident, organisations may need rapid access to logs, technical information, service history, root cause analysis and remediation commitments, while retaining their own ability to protect customers, employees and business operations.
A proportionate investigation should establish what happened, when it began, who or what was affected, the immediate and potential impact, contributing causes, failed or absent controls and decisions made during the response. It should also preserve sufficient evidence to support remediation, governance reporting and any required external review.
Incident closure should include more than resolving the immediate problem. Root causes and lessons learned should inform changes to controls, monitoring, testing, policies, training, supplier arrangements, human oversight and the conditions under which the AI system is permitted to operate.
INTAI can review existing incident arrangements and help integrate AI-specific scenarios into established response processes. This can include incident definitions, severity criteria, ownership, escalation routes, containment decisions, investigation templates, supplier responsibilities, evidence requirements, governance reporting and lessons-learned processes.
Establish clear incident definitions, escalation routes and response procedures so your organisation can contain problems quickly and strengthen controls.
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