Agentic AI: The Future of GRC

Understanding the Role of Trust in Financial Transactions

Before the advent of credit bureaus, establishing trust during loan applications was a daunting task. Financial institutions would verify the same information for each application, regardless of the applicant’s previous history. This wasn’t due to a lack of effort from bankers, but rather because the existing systems failed to retain information effectively.

The Challenges of GRC Today

The governance, risk, and compliance (GRC) framework currently faces a similar predicament. Each audit, vendor evaluation, and questionnaire compels organisations to reconstruct their security status repeatedly. Fresh screenshots, updated spreadsheets, and new explanations are a must. This redundancy arises not from team inefficiencies but from the absence of a comprehensive system that remembers how the organisation operates.

There exists a noticeable gap between the rapid changes within organisations and the slow rebuilding of trust. Modern companies adopt new tools quicker than internal processes can adapt. Vendors appear and disappear weekly, infrastructure changes daily, and shifts in identity occur quietly with every permission granted.

The Necessity of AI in GRC

In this dynamic landscape, compliance teams continually gather evidence that may already be outdated by the time it is reviewed. AI has transitioned from being just a tool for efficiency in GRC to a crucial necessity for handling tasks that manual efforts cannot address. However, much of the dialogue surrounding “AI in compliance” often focuses solely on generative AI.

While the notion of AI drafting policies or writing control descriptions is intriguing, it overlooks a key aspect: GRC is fundamentally a verification activity. It demands certainty, provenance, and insight into the underlying systems.

Why Generative AI Alone Can’t Modernise Compliance

A model might generate text coherently about a misconfiguration it cannot actually pinpoint. This illustrates why generative AI cannot alone modernise compliance — it lacks the necessary grounding. The real evolution stems from a distinct category of AI: agentic systems capable of observing, reasoning, and acting within operational environments.

These systems do not make educated guesses; instead, they verify facts. They connect directly to identity management systems, cloud service providers, ticketing platforms, and vendor ecosystems.

Achieving Continuous Awareness

They detect which permissions escalated overnight, recognize expired evidence, monitor changes in vendor subprocessors, and identify control behavior that has deviated from expected norms. By doing so, they replace reconstruction with real-time awareness.

Traditionally, such capabilities seemed out of reach; today, they are establishing themselves as foundational components.

Transforming Vendor Risk Management

The change can be seen most clearly in areas previously deemed unmanageable, particularly in managing risks associated with an expanding vendor ecosystem. Tasks once impossible to track manually are made visible instantly when new vendors are introduced into the organisation.

The Real-time Assessment of Vendors

As soon as an application appears within single sign-on (SSO) or procurement records, it becomes visible. Its controls align with relevant criteria, and technical checks link to the applicable governance controls.

Instead of awaiting responses from questionnaires that reveal risks weeks later, the vendor’s status is evaluated right at entry. Evidence evolves from a time-bound artifact into a continuously updated representation of reality, refreshed each time a control is altered or a technical signal changes.

Modernising Security Questionnaires with AI

Moreover, AI significantly simplifies the traditionally tedious process of completing long security questionnaires known for delaying enterprise sales. Responses no longer rely on disjointed files or the recollections of whoever last handled a deal.

Instead, information is sourced from existing policies, prior responses, and approved terminology, ensuring consistency across various platforms. Multilingual output and browser autofill features further alleviate workload, enabling teams to answer hundreds of questions in minutes as opposed to days. What used to derail deals can now become an established and reliable process.

The Quiet Evolution of GRC

These advancements do not make GRC noisier or flashier; rather, they provide a more subdued approach. They eliminate the recurring panic cycles that have characterised compliance for the past decade. Work may now proceed at an organisation’s natural pace instead of being dictated by documentation timelines.

Dispelling Common Misconceptions About AI in GRC

However, as with any technological transition, some assumptions continue to cloud perceptions about AI in GRC. One widespread belief is that automation threatens compliance teams. In truth, AI replaces tedious tasks such as repetitive evidence gathering, extended questionnaire responses, and the reactive monitoring that often falls short.

Decision-making, analysis, and accountability remain firmly in human hands; AI merely clears the way for these responsibilities to be carried out with increased lucidity. Another mistaken notion is that AI could render organisations riskier.

Conversely, the expansion of cloud environments and vendor networks increases potential risks from aspects that humans can no longer vigilantly track. AI mitigates that blind spot, observing changes that human capacity might overlook.

Integrating AI into the Core of Compliance

The most limiting misconception posits that AI can be “added” as a minor enhancement to compliance, existing aside from the main framework instead of being integrated. Intelligence needs to reside within the identity management and infrastructure layers where actual controls function.

Failing to do so merely repeats previous mistakes by applying intelligence superficially while leaving the core program unchanged. These misconceptions persist not from a resistance to progress, but from attempting to apply outdated mental frameworks to a radically new structure.

Once these misunderstandings are addressed, AI shifts from being perceived as mere automation to becoming the essential infrastructure that compliance has always required.

Embracing Continuous Trust through Agentic AI

The industry stands on the brink of a transformation reminiscent of the pivotal changes credit systems experienced decades ago: the understanding that trust is not something that can be reconstructed time and again. Instead, it must be maintained continuously, reflecting the actual behaviour of the organisation rather than fragmented data collected during audit periods.

Agentic AI provides that continuity, equipping GRC with something it has historically lacked: a living memory.

This transition will unfold quietly but impactfully, evident in the reduction of escalated issues, the disappearance of relentless evidence searches, and audits becoming routine without unexpected complications.

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