By Raghuveer Kancherla, Co-Founder, Sprinto
Before credit bureaus existed, trust was rebuilt from scratch each time a person applied for a loan. Banks re-verified the same information for every application, no matter how many times a customer had proven themselves elsewhere.
Not because bankers lacked diligence, but because the system lacked memory.
GRC today is stuck in an eerily similar loop. Every audit, every vendor assessment, every questionnaire forces companies to recreate a picture of their security posture from scratch. New screenshots. New spreadsheets. New explanations. New proof. The work is repeated not because teams are inefficient, but because the underlying system retains no living memory of how the organization actually operates.
That gap between how fast companies change and how slowly trust is rebuilt is impossible to ignore. Modern organizations adopt tools faster than their internal processes can track. Vendors come and go weekly, infrastructure rewrites itself daily, and identity boundaries shift silently with every permission grant. And somewhere inside that motion, compliance teams are still asked to assemble evidence that may already be outdated by the time it is reviewed.
AI isn’t an efficiency tool in GRC anymore; it’s a necessity that does what manual effort can’t.
Yet much of the public conversation assumes “AI in compliance” simply means generative AI. The idea that a model could draft a policy or write a control description is appealing, but it avoids the real constraint: GRC is not a writing exercise. It is a verification exercise. It requires determinism, provenance and an understanding of the underlying systems. A model can write convincingly about a misconfiguration it cannot actually detect. This is why generative AI alone cannot modernise compliance, it lacks grounding.
The real shift comes from a different class of AI entirely: agentic systems that can observe, reason and act within the operational environment. These systems do not guess; they verify. They connect directly to identity systems, cloud providers, ticketing workflows and vendor ecosystems. They understand which permissions escalated last night, which evidence quietly expired, which vendor changed its sub-processors, which control drifted two standard deviations from expected behaviour. They replace reconstruction with awareness.
For years, such capabilities were aspirational. Today, they are becoming foundational.
The impact of this shift is visible in the areas that were previously unmanageable. The management of risks from the sprawling vendor ecosystem is the first. Once impossible for any team to track manually, using AI, these risks become visible the moment new vendors are added anywhere in the organisation.
As soon as an application surfaces in SSO or procurement data, it is pulled into view, its controls are automatically mapped to the relevant criteria, and its technical checks are linked to the controls that govern it. Instead of waiting for questionnaires to uncover risk weeks later, the vendor’s standing is assessed at the point of entry. Evidence stops being a point-in-time artifact and becomes a continuously refreshed reflection of reality, updated each time a control changes or a technical signal shifts.
Again, with AI, the long tail of security questionnaires, known to slow enterprise sales, becomes far easier to manage. Responses no longer depend on scattered files or the memory of whoever handled the last deal. They are drawn from existing policies, past responses, and approved language, and applied consistently across spreadsheets and browser portals. Multilingual output and simple browser autofill further reduce the strain, allowing teams to complete hundreds of questions in minutes rather than days. A process that once pulled deals off course becomes routine and predictable.
These shifts do not make GRC louder or more glamorous. They make it quieter. They remove the periodic panic cycle that has defined compliance for a decade. Work happens at the pace of the organization instead of the pace of documentation.
Still, as with any technological shift, a few assumptions continue to distort how people think about AI in GRC. One of the most persistent is the idea that automation threatens compliance teams. But in reality, the only tasks AI displaces are the ones no practitioner wants to spend time on, the endless evidence gathering, the repeated questionnaire responses, the monitoring that never quite keeps up.
The decisions, interpretations and accountability remain with people; AI simply clears the ground so those responsibilities can be exercised with more clarity.
Another assumption is that AI will make organizations riskier. The reality is the opposite. As cloud environments and vendor landscapes expand, the real risk comes from what humans can no longer manually track. AI reduces that blind spot. It sees the changes humans simply don’t have the bandwidth to notice.
And perhaps the most limiting belief is the idea that AI can be “added” to compliance as a small feature, something that sits beside the program rather than inside it. Intelligence must live inside the identity and infrastructure layers where controls actually operate. Anything less repeats the same mistake: it adds intelligence to the edges while leaving the core of the program unchanged.
These misconceptions persist not because people resist the future, but because they apply old mental models to a fundamentally new architecture. Once those assumptions fall away, AI stops looking like automation and begins to resemble the missing infrastructure compliance has always needed.
The industry is now approaching the same inflection credit systems hit decades ago: the realization that trust cannot be reconstructed endlessly. It has to be carried forward and observed continuously, reflecting the actual behaviour of the organisation rather than the fragments teams can gather during an audit window.
Agentic AI offers that continuity. It gives GRC something it has never truly had: a living memory.
The transformation will be quiet. It will be visible in the problems that stop escalating, in the evidence that is no longer chased, and in audits that close without unexpected fallout.