AI Agent Incident Response Playbook for Singapore Organizations

When an AI agent fails in a Singapore-regulated context, the incident response protocol matters as much as the incident itself. What MAS and PDPC may request — and how to be ready.

TL;DR

• AI agent incidents in Singapore-regulated contexts are categorically different from software outages — they involve behavioral deviations, not just system failures, and require behavioral evidence frameworks that most incident response programs do not have.
• Behavioral pacts define what constitutes an "incident" for a given agent deployment — without a pact, incident classification is subjective and defensible only through internal judgment.
• MAS and PDPC each have distinct incident reporting obligations that may be triggered by AI agent failures, with different severity thresholds and notification timelines.
• The trust score impact of an incident — the magnitude and duration of degradation in Armalo's 12-dimension scoring — provides a quantitative signal that helps classify incident severity and track recovery.
• The playbook has five phases: detection, classification, containment, remediation, and closure — and closure requires demonstrating trust score recovery, not just resolving the presenting symptom.

Why This Matters In Practice

AI agent incidents differ from software outages in ways that matter enormously for regulated organizations. A software outage is visible, binary, and measurable: the system is up or down, requests are succeeding or failing, latency is within SLA or breached. The incident timeline is straightforward — detection, diagnosis, restoration, postmortem.

An AI agent incident is behavioral, probabilistic, and contextual. The agent is operating — it is accepting inputs and producing outputs — but its behavior has deviated from its defined obligations in ways that create customer harm, regulatory exposure, or reputational risk. Detection may depend on qualitative review rather than alerting. The deviation may affect only a subset of interactions, making it difficult to bound without structured behavioral evidence. And the "fix" may not be a code deployment — it may require retraining, prompt revision, pact amendment, or in some cases, full agent recall.

Singapore's regulatory environment adds specific obligations. MAS requires prompt notification of material incidents, with "material" defined in the Technology Risk Management Guidelines in ways that now encompass AI system failures that affect customers or operations. PDPC requires notification of data breaches, and an AI agent that inadvertently processes or discloses personal data outside its authorized scope may trigger PDPC notification obligations. The Cybersecurity Act imposes separate notification requirements for operators of Critical Information Infrastructure — which includes several financial institutions.

Organizations that have thought carefully about AI agent incident response before an incident occurs are substantially better positioned than those assembling a response retrospectively. This playbook is designed for Singapore-regulated organizations that want to be in the first category.

Direct Definition

An AI agent incident is any production event in which an AI agent's behavioral output deviates materially from its behavioral pact obligations — whether through harmful content generation, scope boundary violations, data handling errors, adversarial exploitation, or sustained unreliability — in a way that creates or risks creating customer harm, regulatory exposure, or violation of declared commitments.

The definition's dependence on the behavioral pact is intentional. Without a pact that defines the agent's obligations, "incident" is a judgment call. With a pact, "incident" is a measurable event: the agent did X, the pact required Y, the deviation is Z. This specificity is what makes incident classification defensible and what makes remediation verifiable.

Regulatory Incident Reporting Obligations in Singapore

Before the playbook phases, understanding the regulatory reporting landscape is essential.

MAS Technology Risk Management

MAS TRM Guidelines require regulated entities to report "major incidents" to MAS. For AI systems, major incidents typically include: system unavailability affecting a significant number of customers or duration, unauthorized access to customer data or funds, and failures that could adversely affect the entity's reputation or market integrity.

For AI agents, MAS has been developing more specific guidance. The key thresholds for consideration: customer impact (how many customers were affected and in what way), financial impact (any financial loss or risk created by the agent's behavior), and duration (how long the behavioral deviation persisted before detection and containment).

Notification timeline: MAS typically expects notification within one hour of a major incident, with a preliminary report within 24 hours and a full root cause analysis within 14 days.

PDPC Data Breach Notification

Singapore's PDPA requires mandatory breach notification to PDPC and affected individuals when a data breach: (a) affects 500 or more individuals, or (b) is likely to cause significant harm to affected individuals — with "significant harm" defined to include financial, physical, or reputational harm.

For AI agents, a PDPC-notifiable data breach can occur when: an agent processes personal data outside its declared purpose (purpose limitation breach), an agent discloses personal data to unauthorized parties through adversarially-induced behavior, or an agent's behavioral failure results in the exposure of sensitive personal data categories.

Notification timeline: PDPC notification within three calendar days of the organization becoming aware that a breach has occurred. Individual notification must follow where significant harm is likely.

Cyber Security Agency (CSA) Notification

Financial institutions and other Critical Information Infrastructure (CII) operators are subject to mandatory reporting to CSA for significant cybersecurity incidents. AI agent compromises — where an agent is exploited to access systems, exfiltrate data, or execute unauthorized transactions — may trigger CSA reporting obligations independently of MAS and PDPC requirements.

The Five-Phase Incident Response Playbook

Phase 1: Detection

Detection for AI agent incidents is more demanding than for infrastructure outages because behavioral deviations are often not visible in standard monitoring metrics. An agent producing harmful outputs at a 2% rate is still processing 98% of interactions correctly — availability and throughput metrics will not flag this.

Detection mechanisms that work for behavioral incidents:

Trust Oracle alerts: Armalo's Trust Oracle provides real-time trust score monitoring. Configure alerts when composite scores or dimension-specific scores fall below defined thresholds. A safety dimension score dropping from 84 to 71 over a 72-hour period is a behavioral alert, not a system failure — but it signals a meaningful behavioral shift that warrants investigation.

Behavioral sampling: Establish a routine behavioral sampling program — review a statistically representative sample of agent interactions at a defined cadence (daily for high-consequence agents, weekly for lower-consequence deployments). Random sampling catches incidents that escape automated monitoring.

Escalation triggers: Train customer-facing teams to recognize and report AI agent behavioral anomalies. An unusual cluster of customer complaints about an agent's responses is often the first detection signal.

Adversarial probe schedules: Run periodic adversarial evaluation probes against production agents — not just at pre-deployment. A weekly automated probe against a known set of adversarial inputs provides continuous behavioral calibration data.

Detection record: Document the detection method, timestamp, initial characterization, and the team member who detected and escalated. This documentation begins the incident audit trail.

Phase 2: Classification

Incident classification determines the response intensity, escalation level, and regulatory notification obligations. Classification should be performed within the first 30 minutes of detection.

Classification dimensions:

Behavioral severity: How far did the agent's behavior deviate from its pact obligations? Mild (deviation detectable but customer impact unclear), moderate (definite pact violation, limited customer impact), severe (definite pact violation, measurable customer harm or significant regulatory exposure), critical (systemic behavioral failure, large-scale customer impact or imminent regulatory notification obligation).

Impact scope: How many interactions are affected? What is the estimated proportion of affected customer interactions, and which customer segments are affected?

Regulatory trigger: Does the incident meet any of the MAS, PDPC, or CSA notification thresholds? This question must be answered by the compliance function in Phase 2, not after the incident is resolved.

Pact reference: Which specific pact clause(s) does the incident violate? Document the clause reference and the specific behavioral evidence of violation.

Classification record: A Phase 2 classification document should fit on one page and should contain: incident ID, detection timestamp, detecting party, behavioral description, severity classification, impact scope estimate, pact clause violations, regulatory trigger assessment, and incident commander identity.

Phase 3: Containment

Containment prevents additional harm while investigation and remediation proceed. For AI agents, containment options form a spectrum from minimal to maximal intervention:

Monitoring increase: Increase Trust Oracle alert sensitivity and behavioral sampling frequency. Appropriate when the incident is mild and still being characterized.

Human review gate: Require human review before the agent's outputs are delivered to customers. Appropriate when the behavioral deviation is confirmed but the agent's operation provides value worth preserving under human oversight.

Scope restriction: Narrow the agent's operational scope to exclude the contexts where the behavioral deviation occurs. Appropriate when the deviation is associated with specific input types or use cases that can be identified.

Operational pause: Suspend the agent completely and route to human or alternative system. Appropriate for severe or critical incidents where continued agent operation creates material harm or regulatory risk.

Emergency pact update: In some cases, a rapid pact amendment — tightening the behavioral constraints that the incident has revealed were inadequate — combined with a runtime enforcement update can contain the incident without a full operational pause.

Containment record: Document the containment action chosen, the rationale (why this level and not more/less), the implementation timestamp, and the compliance team sign-off.

Phase 4: Remediation

Remediation addresses the root cause of the behavioral deviation. AI agent root causes typically fall into one of four categories:

Model drift: The underlying LLM's behavior has shifted — due to a model update by the provider, distributional shift in production inputs, or accumulated prompt context effects. Remediation: model version pinning, prompt engineering revision, or re-evaluation against updated model behavior.

Pact design gap: The agent's behavioral pact did not adequately specify a constraint that the incident revealed was necessary. The agent operated within its pact but produced harmful outputs because the pact did not prohibit the specific behavior. Remediation: pact amendment, followed by re-evaluation to confirm the amendment closes the gap.

Adversarial exploitation: The agent was manipulated through adversarial inputs to produce outputs outside its behavioral obligations. Remediation: adversarial hardening, input validation updates, and security dimension remediation.

Infrastructure or integration failure: The agent's behavioral deviation was caused by a failure in the supporting infrastructure — incorrect tool responses, data quality issues, integration errors. Remediation: infrastructure fix, data quality correction, integration testing.

Remediation record: Document the root cause determination, the evidence that supports the root cause classification, the remediation action taken, the implementation timeline, and the re-evaluation results confirming the root cause is resolved.

Phase 5: Closure

Closure is not when the symptom is resolved. Closure is when the agent's trust score — specifically the dimensions affected by the incident — has recovered to at or above pre-incident baseline, and the organization has confirmed that the remediation is durable.

Closure requirements:

Trust score recovery: Pull the current Trust Oracle score and confirm that affected dimensions have recovered. If the safety dimension dropped from 84 to 71 during the incident, closure requires the safety dimension to return to at least 84 — ideally higher if the remediation improved the underlying capability.

Pact confirmation: Confirm that the pact version currently active reflects any amendments made during remediation, and that the pact has been formally re-approved by the governance owner.

Re-evaluation record: Run a full pre-deployment-grade adversarial evaluation following remediation and document the results. The closure record should include the evaluation timestamp, methodology, and dimension scores.

Regulatory notification status: Confirm that all required regulatory notifications (MAS, PDPC, CSA as applicable) have been made and that any follow-up requests from regulators have been addressed.

Lessons learned: Document what the incident revealed about agent design, pact specification, monitoring capabilities, or incident response processes — and the specific changes made to prevent recurrence.

Closure record: A closure package should contain: all Phase 1-4 records, the post-remediation trust score, the re-evaluation report, the regulatory notification documentation, and the lessons learned summary.

Incident Response Authority Matrix

Key Takeaways

1. AI agent incidents are behavioral, not binary — detection, classification, and remediation all require behavioral evidence frameworks that standard incident response programs are not designed to provide.
2. Behavioral pacts define "incident" with specificity — without a pact, classification is subjective and difficult to defend to regulators.
3. Singapore's regulatory notification obligations for AI agent incidents span MAS, PDPC, and CSA — each with different triggers, thresholds, and timelines that must be assessed at Phase 2, not after containment.
4. Closure requires trust score recovery, not just symptom resolution — the Trust Oracle provides the quantitative signal that confirms whether an agent's behavioral profile has genuinely returned to an acceptable baseline.
5. Lessons learned must produce specific changes — to pact design, monitoring thresholds, or incident response processes — not just documentation of what happened.

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Singapore organizations building AI agent incident response programs can explore Armalo's behavioral pact framework, Trust Oracle monitoring, and adversarial evaluation system at armalo.ai. The platform provides the behavioral evidence infrastructure that makes AI agent incident response defensible to MAS, PDPC, and board-level scrutiny.