The Agentic GRC Revolution: The End of the Operational Bottleneck

Chatbots that summarize framework requirements. GenAI that drafts policy language. AI assistants that extract key provisions from vendor contracts. AI is everywhere in GRC and some of it is genuinely useful. These tools make existing work faster or easier but  they don't fundamentally change what's possible.

We spent the last year asking our enterprise customers what would actually transform their programs. The enterprises managing risk and compliance across multiple entities, geographies, and frameworks all pointed to the same problem. With Anecdotes, they don't waste time on evidence collection anymore, and they have good visibility into their programs. The crushing bottleneck is operational execution, also known as "the workflows". 

Between every stage of every workflow, there's a bottleneck. A human being needs to review the previous step, assess the output and determine the relevancy of the next step, then finally approve executing the next step. And repeating.. Multiply that across thousands of controls and hundreds of risks, and you see why GRC teams and stakeholders are drowning.

That's the problem we set out to solve and how we arrived at agentic GRC.

Enter Agentic GRC

We define agentic GRC as an operating model where AI agents execute complete workflows autonomously, from trigger through analysis to action, while humans maintain oversight where judgment truly matters.

For example, when a control gap appears, an agent doesn't just flag it. It analyzes the gap, assesses which frameworks are impacted, determines severity based on your program's criteria, creates a remediation ticket in your system with full context, notifies the right owner through your chosen channel, and monitors until resolution is verified. The entire workflow executes itself.

Agentic GRC doesn’t just make things faster, it infuses your workflows with intelligence and completely transforms the role of GRC teams. Now, with their agentic team members taking care of workflow execution, GRC professionals can spend their time on the strategic aspects of their role.

Why Data is the Key to Agentic GRC

For agents to deliver on that promise, you need more than just a powerful prompt or even an orchestration tool, you need data. Contextualized data you can trust.

AI that runs on public compliance frameworks can tell you what NIST CSF requires. It can draft a password policy that sounds reasonable. It can summarize what's typically expected for access reviews. But it can't tell you that three users in your Azure AD have been without MFA for 32 days, which violates your specific organizational policy, and impacts two controls across your NIST CSF and SOC 2 programs. Oh and these gaps need to be assigned to your identity team in Jira with a 48-hour SLA for remediation, right?

For that, you need real-time, audit-grade data from your actual environment. That's what we've been building at Anecdotes for years. Over 230 integrations collecting evidence continuously. Normalized schemas that work across GRC use cases and are trusted by the top auditors in the world.

When agents have access to this foundation, they deliver tailored intelligence instead of generic suggestions. Collection triggers analysis. Analysis triggers notification. Notification triggers remediation. Human oversight happens where you decide it matters.

The Workflows That Transform Programs

Take continuous control monitoring. Traditional CCM detects a gap in your environment and sends an alert. Then someone has to read it, figure out what to do, route it to the right team, and manually track resolution.

Agentic Continuous Control Monitoring completes the entire cycle. The agent detects the gap, analyzes impact across your mapped frameworks, creates a Jira ticket with full context, notifies the control owner via Slack with recommended remediation steps, and monitors until the issue is verified as resolved.

Policy lifecycle management works the same way. Most enterprises review and approve policies, then hope they're being followed until the next annual review. Agentic Policy Lifecycle Management monitors implementation continuously against live evidence from your environment. When violations occur, agents trigger your remediation workflows immediately instead of waiting months for someone to notice during an audit.

Risk management becomes dynamic rather than static. When control effectiveness changes, agents recalculate residual risk automatically, notify risk owners if thresholds are breached and send tailored reports to all relevant stakeholders. 

These are just a couple of examples, the options are endless really. Especially when you account for how each enterprise is unique and how different their workflows look. That is why we took a two-pronged approach: We productized use-case specific agents for common workflows and created Agent Studio where teams can build custom agents that match their unique requirements.

The Future of GRC is Here

For years, GRC teams have been caught in an impossible position: programs growing in complexity while resources stay flat. The only solution was to work harder, move faster, and hope nothing fell through the cracks.

Agentic GRC changes that equation entirely. When agents handle workflow execution, GRC teams finally have the space to be what they've always needed to be: strategic advisors who design programs, make judgment calls, and enable business growth rather than just saying no.

The operational bottleneck that's defined GRC for decades is finally breaking open. What comes next is a GRC function that's continuous, intelligent, and genuinely strategic.

Ready to see how agentic GRC works in practice?Explore our platform and discover what becomes possible when your workflows execute themselves.