AI in GRC: What AI Agents Actually Do in Cyber Governance, Risk and Compliance
AI in GRC is the use of artificial intelligence (AI) to do the reading, matching and drafting in governance, risk and compliance (GRC) programs.
In cyber GRC, AI agents review evidence, map controls, draft policies, and turn findings into assigned tasks.
People still own the risk decisions, and the gain is faster execution inside the workflows you already run.
This guide covers what an AI agent is in GRC, which tasks agents handle today, and where human oversight stays essential.
It also covers how to govern the AI itself and how to evaluate a GRC tool with AI capabilities.
What Does AI Actually Do in GRC?
AI in GRC takes on high-volume reading, matching, and drafting work that cyber GRC teams would otherwise do by hand.
AI reads evidence and questionnaires, compares them against control requirements, drafts policies and risk write-ups, and turns findings into tasks with owners.
Much of GRC work runs on unstructured text: policies, audit evidence, vendor questionnaires, risk descriptions, and regulatory updates.
Traditional workflow tools can route documents and extract predefined information.
Large language models (LLMs) can interpret more varied text and draft responses for a person to check.
That is the practical change AI brings to cyber GRC: less time collecting and comparing, more time deciding.
Security operations teams already use AI at scale.
IBM's 2026 Cost of a Data Breach research linked AI and automation in security operations to almost $2 million lower average breach costs.
In the same release, IBM reported that more than 50% of surveyed organizations use agents for threat detection and containment.
Only 18% apply agents to vulnerability management, the work where security findings become risk treatment and compliance tasks.
Neither figure measures GRC directly, but the gap shows where AI agents in GRC could add value next.
What Is an AI Agent in GRC, and How Is It Different From Automation?
An AI agent in GRC is software that pursues a defined goal, such as validating supplier evidence, by planning and completing the steps itself.
Rule-based automation follows a predefined path, while an AI agent chooses its next step and tools based on what it finds.
A conventional AI assistant, often called a copilot, mainly responds to a person's requests.
Vendors use these labels differently, so judge a tool by how it chooses its steps, not by what it is called.
The table shows typical configurations.
How each approach starts, what data it reaches, and who approves its output depend on the implementation.
Typical configuration | Rule-based automation | AI assistant (copilot) | AI agent |
How does it choose its steps? | Follows a predefined path | Responds to each request | Chooses steps and tools based on what it finds |
How does it usually start? | A trigger or schedule you configure | A person's prompt | A goal or event, such as a new vendor upload |
What inputs does it usually handle? | Structured fields and fixed formats | Text a person pastes or selects | Documents, questionnaires, tickets and system data |
What does it usually produce? | A status change, ticket or reminder | A draft for the person who asked | A finding with a status, score, explanation or task list |
Cyber GRC example | Send a reminder when a control review is overdue | Rewrite a risk description for the board | Check a supplier's SOC 2 report against its questionnaire answers and flag gaps |
Where do people usually sit? | Design and maintain the rules | Review every output | Set limits, approve results and handle exceptions |
The three approaches stack rather than compete.
Rule-based workflows still run approvals, reminders and escalations.
AI agents fit into those workflows at the steps that used to need a person to read, compare and write.
Which Cyber GRC Tasks Can AI Agents Handle Today?
AI agents in cyber GRC handle tasks with clear inputs, a known standard to check against, and an output that a person can verify.
Today, those tasks span third-party risk, audit findings, remediation, controls, policies, risk documentation, risk quantification, and contracts.
Third-party evidence review: an agent compares a supplier's questionnaire answers, comments and uploaded evidence, then flags gaps, irrelevant files and expired certificates. The agent returns a score and a recommendation: accept, accept with conditions, request clarification or reject.
Third-party threat intelligence: an agent searches open sources for data leaks, ransomware mentions and known vulnerabilities tied to a supplier, then scores the exposure.
Control evidence assessment: an agent checks whether submitted documents show that each control is documented through policies and operating records and logs.
Findings extraction: an agent reads audit, penetration test, and vulnerability scan reports, then logs each finding with the severity the report assigned. The agent merges a finding repeated across many assets into one gap, and flags overlaps with findings already on record.
Remediation planning: an agent turns a vulnerability or finding into a mitigation plan with steps, compensating controls, and a way to verify closure. A second agent breaks the plan into tasks that stay pending until a manager approves them.
Control generation: an agent reads a risk or exposure description and proposes security controls with implementation steps and priorities.
Control mapping: control mapping is the process of linking each internal control to the framework requirements it satisfies. An agent proposes those links across ISO/IEC 27001:2022 and NIST CSF 2.0, and your team validates coverage before reusing any test or evidence.
Policy drafting: an agent turns controls into a draft internal policy, or revises an existing policy and logs each change for the audit trail. Your policy owner approves the final wording.
Risk documentation: a risk register is the record of identified risks, their owners, ratings and treatment plans. An agent scores risk descriptions for completeness, coaches the employees who wrote them and rewrites them for executives.
Risk quantification: an agent estimates annual rate of occurrence (ARO) and single loss expectancy (SLE) for a risk scenario.
Contract review: an agent extracts the critical clauses from a supplier contract, with exact quotes and deadlines.
The Commugen no-code cyber GRC platform includes AI agents that follow the same pattern: each one reads, checks, or drafts, and returns a finding a person can act on.
Start with tasks that are narrow, repeated, and easy to check against a known answer.
Where Do AI Agents in GRC Still Need Human Oversight?
AI agents in GRC need human oversight wherever an output becomes a decision, such as accepting a risk or approving a policy.
An AI agent can prepare the evidence for that decision, but a named owner must review the output and sign off.
Language models can state a wrong fact with full confidence.
In GRC, one wrong "compliant" status can carry straight into an audit report or a board pack.
A practical oversight policy for every agent has three checkpoints:
Require named human approval for consequential decisions and sensitive record changes, such as risk acceptance, regulator filings and customer attestations.
Define which actions each agent may take automatically, and spot-check those outputs on a schedule.
Log who approved each agent output, so auditors can trace every consequential decision to a person.
AI regulation points the same way.
Article 14 of the EU AI Act, Regulation (EU) 2024/1689, requires human oversight of high-risk AI systems, commensurate with their risks, autonomy and context.
Whether a GRC tool counts as high-risk depends on its intended use, so check the classification for each deployment.
Access control is the second oversight gap.
IBM's 2025 Cost of a Data Breach research found that 97% of organizations with a breached AI model or application lacked AI access controls.
Give each agent the narrowest access its task needs, and review that access like any privileged account.
How Should You Govern the AI Inside Your GRC Program?
Govern the AI inside your GRC program the same way you govern any other risk: inventory it, assess it, assign owners, and monitor it.
The NIST AI Risk Management Framework and ISO/IEC 42001:2023 help you structure that work and identify the documentation relevant to your assessments.
Inventory every AI feature and agent in your GRC stack, including vendor add-ons.
Record each one in your risk register with an owner, a purpose and the data it touches.
Assess each use against the AI RMF functions or your ISO/IEC 42001:2023 controls.
Set access, review, and logging rules for each agent.
Monitor output quality, and reassess when the model, the vendor or the use case changes.
NIST published the AI Risk Management Framework (AI RMF 1.0), NIST AI 100-1, on January 26, 2023.
The AI RMF is voluntary guidance, not a regulation.
The AI RMF organizes AI risk work into four functions: govern, map, measure and manage.
ISO/IEC 42001:2023 specifies requirements for establishing, implementing, maintaining and continually improving an AI management system (AIMS).
Teams with an ISO/IEC 27001:2022 information security management system (ISMS) can extend their audit and review routines to AI, adding AI-specific requirements.
For the security side, NIST released the Cyber AI Profile, NIST IR 8596, as a preliminary draft on December 16, 2025.
The draft profile builds on the NIST Cybersecurity Framework with three focus areas: securing AI system components, AI-enabled cyber defense, and thwarting AI-enabled attacks.
Treat the draft Cyber AI Profile as emerging guidance.
Final publication alone would not make it a legal requirement.
In the EU, the AI Act entered into force on August 1, 2024.
The AI Omnibus, in force since July 27, 2026, moved the rules for Annex III high-risk AI systems to December 2, 2027.
Rules for high-risk AI in regulated products now apply from August 2, 2028.
How Do You Evaluate a GRC Tool With AI Capabilities?
Evaluate a GRC tool with AI capabilities by testing the AI on your own evidence, frameworks and workflows, not on a vendor demo script.
Check what the AI produces, how a reviewer approves it, where your data goes and whether every output leaves an audit trail.
Pick two or three tasks your team repeats every month, such as vendor evidence review or policy updates.
Run a pilot on past questionnaires and audit evidence where you already know the right answer.
Compare agent findings with your analysts' findings, and record both the misses and the hours saved.
Confirm that consequential AI outputs route to a named approver before they change a record.
Ask whether the vendor trains models on customer data and whether a self-hosted deployment is available.
Make sure the tool logs inputs, outputs and approvals in a form your auditors accept.
Check whether your team can change workflows and add frameworks without depending on vendor services.
Treat these steps as selection criteria, and weight them by your own risks and obligations.
Step 7 matters because frameworks and regulations change, and an AI feature locked inside a rigid workflow ages fast.
A no-code platform lets your GRC team adjust the workflow around the agent when a framework or regulation changes.
How Can Commugen Help You Put AI to Work in Cyber GRC?
Commugen adds AI agents to its no-code cyber GRC platform for evidence review, policy drafting, mitigation planning and risk documentation.
The agents run inside your existing workflows, alongside the risk registers, control tests and tasks you already manage in the platform.
The AI agents for cyber GRC also turn mitigation plans into assignable tasks with owners and help tailor risk documents to different audiences.
Commugen states that customer data is not used for model training and offers a self-hosted deployment option.
To see the agents work on your own frameworks and evidence, book a demo.
Frequently Asked Questions
Will AI agents replace GRC analysts?
AI agents can automate parts of a GRC analyst's work, such as checking supplier evidence or writing first-draft policies and risk descriptions.
Organizations still need people accountable for risk and compliance decisions, including risk treatment, exceptions, and audit readiness.
The effect on GRC team size and roles will vary by organization.
What is the difference between generative AI and agentic AI in GRC?
Generative AI in GRC produces content, such as a draft policy or a summary of an audit report.
Agentic AI systems often use generative AI alongside tools and workflow logic to pursue a goal across several steps.
The two overlap, and both still need defined limits and human review of consequential outputs.
How do AI agents support continuous compliance?
Continuous compliance means keeping controls tested and evidence current all year, not only before an audit.
AI agents support continuous compliance by reviewing new evidence as it arrives and flagging gaps against framework requirements.
GRC teams can then fix gaps earlier, although testing frequency still depends on each control and obligation.
Do AI agents in GRC work with ISO/IEC 27001:2022 and NIST CSF 2.0?
Yes, AI agents in GRC can map controls across ISO/IEC 27001:2022 and NIST CSF 2.0 and check evidence against both.
NIST CSF 2.0 describes cybersecurity outcomes and does not prescribe how to achieve them, so each mapping needs review.
Teams reuse control tests and evidence only after validating coverage and scope, and review mappings when frameworks change.
Is AI in GRC safe to use with sensitive audit evidence?
AI in GRC can process sensitive audit evidence when the deployment has appropriate security, privacy and contractual controls.
Check data retention, downstream model providers, data leakage, prompt injection through uploaded documents, and what actions each agent may take.
Validate those controls against your own risks and obligations, and give every agent least-privilege access.
