Sponsored article

Workflow automation was supposed to solve GRC’s scaling problem. Instead, it just created faster procedures that were stalled by humans who had to make decisions. With regulatory demands exploding and programs growing more complex, workflow automation isn’t keeping pace anymore. Agentic GRC is what comes next.

Listen to the full episode of Security You Should Know featuring Anecdotes.

Got feedback? Join the conversation on LinkedIn.

Thanks to our sponsor, Anecdotes

Anecdotes is the enterprise agentic GRC platform built for organizations that refuse to compromise. With comprehensive solutions across governance, risk, and compliance, deep customization capabilities, and AI agents that run on an audit-grade data infrastructure, Anecdotes enables the world’s top enterprises to manage GRC programs as unique as their businesses.

Where workflow automation fell short

Workflow automation did create efficiencies. Automated routing replaced manual emails. Scheduled reviews replaced calendar reminders. Pre-built templates have replaced the need to always start from scratch. For GRC teams drowning in spreadsheets, these improvements mattered.

The problem is that these efficiencies aren’t enough to keep up with the scope and needs of modern enterprise GRC programs.

Your risk assessment workflow triggers, identifies a new risk, then stops and waits for someone to validate the risk description. After approval, it waits for someone to assess the likelihood and impact. Then it waits for risk rating approval. Then it waits for a treatment plan. Then, for control mapping. Then it waits for mitigation verification. Six steps, six human checkpoints, and that’s one of the simpler workflows. 

Workflow automation connected the steps without eliminating them. Enterprises got better orchestration of the same human-dependent processes. Meanwhile, regulatory complexity compounds. SOX ITGC, ISO 27001, NIST CSF, and state privacy laws stacked more and more compliance requirements, resulting in additional workflows, each adding human touchpoints. All the while, team sizes and budgets stay mostly flat.

Enter agentic GRC

Recent advances in AI reasoning and orchestration have created a new operating model: agentic GRC. In this model, AI agents independently execute workflows, while human experts provide strategic oversight. Each agent has access to real-time data and context from across your GRC program: your policies, your risk framework, your control environment, and evidence data from your systems. With this foundation, it can execute multi-step processes from trigger through taking actions, making autonomous decisions, and stopping for human intervention only at the stages you designate as requiring it.

Agentic GRC solves both the scale and budget challenges enterprise GRC teams face. Workflows automate the connections between tasks but still need humans at every junction. Agents handle both the thinking and the execution, involving humans only where and when their expertise matters.

Take that risk assessment. An agent identifies the risk, categorizes it by reasoning through your taxonomy and context (not just keyword matching), scores likelihood and impact using live data from your control environment, determines treatment approach based on your risk appetite, maps the relevant controls, and routes mitigation tasks to appropriate owners. 

The entire process executes without stopping at each step because the agent works within the parameters and oversight structure you’ve established. It gets human approval at the stages where you’ve determined intervention is necessary, handling everything else autonomously.

Building agentic GRC

The agentic GRC market is evolving along familiar lines. Just like workflow automation is split between templated solutions and customizable platforms, agents are following the same path. Some vendors offer pre-built agents for common processes. Others provide tools to build custom agents tailored to your workflows.

Pre-built agents are essentially templates that handle common GRC workflows: continuous control monitoring that detects gaps and automates remediation, policy lifecycle management that monitors real-time implementation and triggers corrective action when needed, and risk assessments that recalculate risk levels as your control environment changes. For standard processes, these pre-built agents can get you operational immediately.

Custom agents are where this becomes transformative for enterprises. Your evidence collection process from engineering platforms like GitHub follows review cycles unique to your organization, different teams own different evidence types, and what constitutes sufficient evidence depends on your auditor relationships and program maturity. Your remediation workflow for control gaps and policy exceptions routes through stakeholders based on your organizational structure, escalates based on your SLA (service level agreement) requirements, and integrates with ticketing systems in ways that align with how your teams operate. No-code agent builders let teams define the triggers, logic, and actions that match their exact processes.

Enterprises can learn from the workflow automation era and stop forcing unique processes into templates. As they transition into agentic GRC, they should combine pre-built agents with agent orchestration capabilities.

The future is taking shape

Workflow automation bought GRC teams time. Agentic GRC restores that function. This is the shape of the AI-native category: agents that execute repeatable work, human experts who own the strategy, and a program that finally scales with the business it governs.

The operational bottleneck that has defined enterprise GRC for the past decade is finally breaking open,and the enterprises making the shift are building programs that scale with business growth instead of drowning in it.