Compliance has long been characterised by what many security leaders now recognise as “audit chaos” – a constant cycle of manual evidence collection, fragmented workflows and point-in-time reporting that struggles to match the pace of AI risk.
In response, a new model is growing: agentic compliance. By embedding AI agents directly into trust and security workflows, organisations are beginning to move from reactive, audit-led processes to continuous, real-time assurance. These systems can coordinate tasks, collect and review evidence and surface material risks, fundamentally reshaping how compliance is delivered.
But while the shift is necessary, it is not without risk. Without the right controls, agentic compliance risks scaling the problems it aims to solve. Human-in-the-loop remains non-negotiable.
From audit chaos to continuous assurance
Traditional compliance models were never designed for the pace and complexity of modern environments. They rely on static frameworks, manual validation and retrospective reporting, all of which create a gap between what is happening in a system and what can be evidenced.
Agentic systems offer a way out of this cycle. This means automating the most time-consuming parts of compliance. For most mid-market teams, it’s using AI to do the repetitive work around onboarding, evidence collection, control mapping and questionnaire responses so a lean team can operate a stronger programme without adding headcount.
The impact is not just efficiency, but also resilience. Instead of scrambling at audit time, organisations can maintain a continuously audit-ready state where controls, evidence and risk signals remain aligned as systems evolve.
This is why adoption is accelerating. According to Vanta’s latest State of Trust report, there’s clear openness to agent involvement in the market today, with 71% of teams comfortable with agents contributing to security strategy. In fact, 80% are already using or plan to use them for defence.
But increased adoption does not automatically mean increased control.
Automation without autonomy
There is a tendency to frame agentic compliance as a move towards full autonomy. At present, the most effective implementations are more constrained.
Most organisations are currently equipped to govern assisted automation rather than full autonomy. Agents can synthesise data, identify gaps and recommend remediation, but they do not replace human decision-making. The agents are designed to operate with broad context across a trust programme – surfacing issues, coordinating workflows and recommending remediation – but with humans retained in the loop for final decisions.
This distinction matters because compliance is an accountability function as well as operational. Decisions carry regulatory, financial and reputational consequences, and cannot be outsourced without clear visibility, accountability and control.
As a result, the shift from guidance to action is intentionally controlled. Agents can accelerate identification and prioritisation of fixes, but approval, particularly where risk is non-trivial, remains with the human operator.
The trust paradox in AI-driven compliance
The real challenge with agentic compliance is not what the technology can do, but how organisations govern it.
According to our report, 61% of organisations say their use of agents is outpacing their understanding, suggesting the constraints aren’t technical capability, but control, accountability and clarity around decision-making.
As agents take on more responsibility for monitoring, analysing and recommending actions, organisations must be able to explain how those decisions are made and who is ultimately accountable for them.
This becomes even more complex in a landscape where AI is both the threat and the defence.
Organisations are increasingly deploying agents to manage trust programmes designed to protect against AI-driven attacks. But this raises a fundamental question: how do you trust the system that is designed to assure trust? I don’t mean whether an agent is ‘trusted’ in the abstract; but rather whether its actions are bounded, explainable and accountable. Trust comes from being able to continuously prove what the system is doing.
Agents must operate within a defined framework of controls, permissions and oversight. You do not ask the agent to be the final source of trust. You make it a governed operator inside a system of permissions, policy guardrails, continuous evidence collection, with human oversight and accountability.
Compliance at machine speed
While agentic systems can reduce operational burden, they also introduce a more subtle risk: accelerating compliance without improving security.
The risk with AI is that it can accelerate the dynamic of security theatre (where effort is focused on proving security rather than improving it) if it’s just layered onto static checklists. In this scenario, compliance becomes faster, but not better.
What’s important is that teams shift their focus from output to outcomes. Rather than treating compliance as a reporting exercise, organisations must ensure that automation is grounded in real operational context.
This includes embedding visibility directly into core workflows, continuously assessing vendor risk and generating reliable answers to security questionnaires, ensuring that every output is backed by current, verifiable evidence.
Scaling trust, not just automation
Agentic compliance represents a necessary evolution in how organisations manage risk. But its success will depend on how deliberately it is implemented.
Teams do not need a large implementation project or a specialist AI function. Instead, they start by automating the most time-intensive workflows and expand from there.
The differentiator will be how well organisations govern automation as it scales. The strongest AI programmes pair automation with clear boundaries, traceability to source data and accountability at every layer.
Agentic compliance can eliminate audit chaos. But without control, it risks replacing it with something far harder to detect and far more difficult to unwind.
