Introduction

Governance, Risk and Compliance (GRC) teams manage a growing volume of policies, controls, regulatory obligations, risk signals, audit evidence and third-party information. Traditional automation can make these activities faster, while generative AI can help professionals interpret information and produce analysis. Agentic AI introduces another possibility: systems that can plan tasks, use connected tools, take actions and adapt their next steps within defined objectives.

For GRC leaders, this creates opportunities to move beyond isolated automation towards coordinated workflows. An AI agent could identify a potential control issue, gather supporting evidence, compare it with an internal requirement, prepare an assessment and route the matter to an authorised reviewer. In more advanced environments, multiple agents may coordinate different stages of a process.

However, autonomy also changes the risk profile. An agent that can access sensitive information or initiate actions can create consequences beyond an inaccurate answer. It may act on misleading instructions, misuse permissions, expose data, produce unreliable assessments or trigger downstream failures.

The question for organisations is therefore not simply whether agentic AI can improve GRC efficiency. It is how to establish the boundaries, oversight, accountability and technical safeguards that make its use trustworthy.

What Makes AI Agentic?

Agentic AI describes systems designed to pursue an objective through a sequence of steps. Depending on their design, they may interpret a request, break it into tasks, retrieve information, use tools, evaluate intermediate results and decide what to do next.

A conventional automation workflow follows predefined conditions. A generative AI assistant primarily responds to prompts by producing content. An AI agent can go further by using tools and taking actions within the permissions it has been granted.

Agentic behaviour does not necessarily mean unrestricted autonomy. An agent may be configured to recommend actions only, request approval before execution, or carry out low-impact tasks independently. The level of autonomy is a design choice and should be treated as a risk decision.

Single-agent and multi-agent systems

A single agent may handle a defined workflow, such as collecting evidence for a control assessment. A multi-agent system may divide work across specialised agents: one retrieves evidence, another checks it against a control requirement, and a third prepares an exception report.

Coordination can improve task coverage, but it also introduces dependencies. An error or compromised instruction in one agent may influence the actions of others. Organisations should therefore govern the entire workflow, not only each agent in isolation.

Why Agentic AI Matters for GRC

GRC work frequently involves connected information and decisions. A change in a regulation may affect policies, control requirements, business processes, risk registers and audit evidence. A third-party incident may require reassessment of vendor exposure, business continuity arrangements and compliance obligations.

These activities often cross teams and systems. Agentic AI may help coordinate parts of this work, particularly where tasks are repetitive, information-intensive and governed by clear procedures.

For example, an agent could monitor approved sources for regulatory updates, identify potentially relevant obligations, map them to an existing control library and prepare a change assessment. A compliance professional would then review the interpretation, confirm applicability and approve any changes to the organisation's obligations register.

This model can support more timely analysis without transferring regulatory accountability to the agent. It also connects with established approaches to Integrated Risk Management (IRM), where risks and controls are considered across the organisation rather than managed in disconnected silos.

Opportunities and Use Cases

The most useful GRC applications are those where agents can reduce manual coordination while keeping important decisions reviewable.

1. Continuous risk monitoring

Agents can analyse approved internal data feeds, incident reports, control indicators and external risk information to identify signals that may require attention. They can group related events, prepare summaries and route potential issues to the relevant risk owner.

For example, an agent could identify repeated service interruptions affecting a critical supplier and prepare a preliminary reassessment for the third-party risk team. The risk owner would validate the information and decide whether the supplier's risk rating or treatment plan should change.

2. Compliance obligation analysis

An agent can help compare new regulatory publications with an organisation's existing obligations and control library. It may identify potentially relevant changes, highlight affected business units and prepare an initial impact assessment.

This is decision support, not an authoritative legal interpretation. Applicability, jurisdiction, effective dates and the organisation's actual obligations must be confirmed by qualified personnel.

3. Control testing and evidence collection

Agents can retrieve approved records, organise evidence and compare it with defined control criteria. They may flag missing documents, overdue reviews or apparent inconsistencies for further examination.

For instance, an agent supporting access-control testing could collect authorised access review records and identify accounts that appear to lack a completed review. It should not independently certify the control as effective without appropriate validation.

4. Audit preparation

An agent can help assemble audit evidence, map documents to control requirements, identify gaps and prepare draft responses to auditor requests. This can reduce administrative effort and improve evidence organisation.

The audit team must retain independence and professional judgement. Agent-generated summaries should be traceable to the original evidence, and the agent should not be allowed to conceal exceptions or alter audit records.

5. Third-party risk management

Agents can support the review of supplier questionnaires, security documentation, incident notifications and reassessment triggers. They may compare submitted information with an organisation's risk criteria and prepare follow-up questions.

A human reviewer should validate critical findings, particularly where they affect supplier onboarding, contract decisions, access to sensitive information or service continuity.

6. Policy and control management

Agents can identify policy review dates, compare proposed changes with approved requirements and prepare draft updates. They may also help identify where a policy change could affect related controls, training or evidence requirements.

Final approval and publication should remain with designated policy owners.

7. Incident and issue management

When an incident is reported, an agent may classify the initial information, identify relevant procedures, assemble context and recommend escalation routes. It could also prepare a structured incident summary for the response team.

Actions such as disabling access, notifying regulators, contacting customers or activating crisis procedures should require explicit authority and suitable approval controls.

Risks of Agentic AI in GRC

Agentic systems create risks that extend beyond the familiar challenge of inaccurate AI-generated content. Their ability to interact with tools, access information and initiate actions introduces operational and security concerns.

The OWASP Top 10 for Agentic Applications 2026 provides a useful security reference for organisations assessing risks in autonomous and agentic systems.

Why autonomy changes the risk equation

An incorrect response from a conversational assistant may mislead a user. An agent with write permissions could turn that error into an operational change. If connected agents share information or trigger one another, the consequences may spread before anyone notices.

This is why agentic AI should be assessed as a combination of model behaviour, tools, data, identity, workflow design and human decision-making. Testing only the underlying model is not enough.

Governance Controls for AI Agents

Organisations need a governance model that addresses the full lifecycle of an agent: its purpose, development or procurement, access, deployment, operation, monitoring and retirement.

The NIST AI Risk Management Framework offers a useful foundation through its Govern, Map, Measure and Manage functions. Its Generative AI Profile also provides relevant guidance for evaluating risks associated with generative AI components. These resources can inform an organisation's approach, but they do not replace a control framework tailored to the agent's actual capabilities.

Core governance controls

Establish autonomy levels

Not every GRC task needs the same level of independence. Organisations can define autonomy levels based on potential impact, reversibility and the sensitivity of the information involved.

• Level 1 – Assist: The agent retrieves information, summarises evidence or drafts recommendations. A person performs all consequential actions.
• Level 2 – Prepare: The agent completes a workflow and prepares a proposed action for human review.
• Level 3 – Execute with approval: The agent can execute an action only after an authorised person approves it.
• Level 4 – Bounded execution: The agent can independently perform predefined, low-impact and reversible actions within strict limits and monitoring.

High-impact activities should not be assigned greater autonomy simply because a system performs well in a limited test. The organisation should demonstrate that safeguards, monitoring and recovery mechanisms are effective before expanding an agent's authority.

A practical example: A compliance agent may independently collect evidence and prepare a control assessment. It can recommend that a control be marked as potentially deficient, but the control owner must validate the evidence and approve the status change. Any remediation task that alters production systems should follow the organisation's existing change and access approval processes.

Human Oversight and Accountability

Human oversight should be meaningful, not a final approval button that reviewers routinely click without understanding the action.

The level of review should reflect the consequences of the decision. A low-risk task such as sorting evidence may need periodic sampling. A decision that affects regulatory reporting, customer data, supplier approval or critical services may require explicit review by an authorised professional.

A clear accountability model should answer five questions:

1. Who owns the use case? A named business owner is accountable for the purpose and expected outcome.
2. Who approves the agent's permissions? Security and system owners confirm what the agent can access and change.
3. Who validates its outputs? The relevant risk, compliance, audit or control professional reviews decisions within their remit.
4. Who monitors performance? A designated operational owner reviews errors, exceptions, drift and policy violations.
5. Who can stop it? The organisation identifies personnel authorised to suspend access, disable an agent or isolate connected workflows.

An agent can support a decision, but it does not become the accountable owner of that decision. Organisations remain responsible for their governance arrangements, legal obligations and operational outcomes.

A Practical Roadmap for Adoption

A controlled rollout helps organisations establish evidence before increasing autonomy.

Step 1: Select a bounded use case

Choose a workflow with a clear objective, measurable effort or quality problem, defined data sources and limited consequences if the agent makes an error. Evidence collection or draft control mapping may be a better starting point than autonomous regulatory submissions or production changes.

Step 2: Map the workflow and its risks

Document the agent's inputs, outputs, tools, connected systems, users and decision points. Identify sensitive data, external dependencies, possible misuse and downstream actions. Record what the agent is explicitly not permitted to do.

Step 3: Define permissions and approval gates

Use dedicated identities and least-privilege access. Separate read, draft and execution permissions. Require human approval for high-impact actions and ensure that approval cannot be bypassed through an alternative tool or agent.

Step 4: Test before deployment

Evaluate the agent using representative tasks, incomplete information, misleading documents, malicious instructions and failure scenarios. Test whether it refuses unauthorised actions, preserves source references and escalates uncertainty. Record results and close material gaps before production use.

Step 5: Pilot with monitoring

Deploy within a controlled environment. Monitor completion rates, error types, false positives, escalation quality, unauthorised tool attempts and human override frequency. Compare results with the existing process rather than relying on speed alone.

Step 6: Review and expand carefully

Have business, risk, compliance and security owners review pilot evidence. Expand permissions or use cases only when performance, controls and recovery arrangements justify the change. Reassess after material updates to the model, tools, data or workflow.

How autoResilience Supports Agentic AI Governance

Agentic AI governance requires more than a model policy. Organisations need connected processes for identifying risks, assigning ownership, maintaining controls, documenting evidence and monitoring remediation.

autoResilience supports the broader GRC operating environment through integrated risk, compliance, audit and policy management capabilities. These capabilities can help organisations manage the governance processes surrounding AI adoption; they should not be interpreted as a claim that the platform autonomously governs or controls third-party AI agents.

Relevant capabilities include:

• Enterprise Risk Management (ERM): Record AI-related risks, assess their potential impact and assign accountable risk owners.
• Operational Risk Management (ORM): Track operational exposures arising from agent errors, process failures and technology dependencies.
• Regulatory Compliance Management: Map applicable obligations to business requirements and maintain compliance oversight.
• Policy Management: Maintain AI policies, review cycles, approvals and policy-related evidence.
• Audit Management: Organise audit plans, findings, evidence and remediation tracking.
• Third-Party Risk Management (TPRM): Assess vendors and external providers involved in AI-enabled workflows.
• Risk and control libraries: Maintain structured records of risks, controls, owners and assessments.
• Issue and action tracking: Assign remediation actions and monitor their progress.
• Dashboards and reporting: Support visibility into risk status, control performance and outstanding actions.
• Integrated GRC workflows: Connect relevant governance activities across risk, compliance and audit functions.

The objective is to establish a consistent governance foundation around AI-enabled processes: who owns the risk, which controls apply, what evidence supports an assessment and whether remediation is progressing.

Written by Shambhavi Singh Marketing Executive at Ascent Risk & Resilience

Shambhavi Singh is a Marketing Executive at Ascent Risk & Resilience, where she contributes to brand communication, content strategy, and digital storytelling across the organization's risk and resilience solutions. With a background spanning content writing, voice-over artistry, anchoring, public speaking, and social impact, she brings both creativity and clarity to every message she crafts.

Shambhavi's passion for communication started early in her hometown of Varanasi, where her curiosity for culture and heritage shaped her worldview. Driven by a blend of will and skill, she is committed to building meaningful connections, leading with empathy, and contributing to initiatives that create positive change.