by Peter Adamson and Kala K
It’s time to lose the rigid GRC Perception
AI integration into GRC (Governance, Risk and Compliance) is less discussed compared to areas like marketing or operations. Yet, AI is rapidly transforming how organizations manage oversight, threats, and regulatory adherence. AI adoption is supercharging automation of manual processes, integration of data silos and predictive analytics and data science foundations.
Agentic AI takes this even further: moving from just passive analysis to autonomous systems that can act independently. For instance, flagging risks in real-time, adjusting compliance protocols, or even executing decisions like alerting regulators or rerouting resources. This "agentic" capability, powered by models that plan, reason and adapt, enables proactive resilience in volatile environments. Agentic AI can orchestrate GRC by embedding intelligence across processes, moving beyond automation to effective collaborative decision making.
However, this introduces new AI specific challenges such as bias and data privacy, calling for the ongoing adoption of "GRC 2.0" frameworks that treat AI as both - a tool and a source of risk. Currently, most GRC transformation discussion centers around the expanding scope to govern AI itself, skills, roles, tools and business capabilities.
In this blog, we take a different approach by investigating and validating the impact of AI on four (4) key questions that existed even before AI — a model for continuous oversight to effects of binary problem solving, missing evidence, and clashes among domain experts. We share findings and provide new insights around cultural and process shifts required to turn AI-native GRC into a strategic advantage.
Periodic to Continuous Oversight
Traditional GRC systems were often rigid and rules-based, unable to adapt quickly to changing regulatory and risk environments or integrate with modern development tools. This is exacerbated by pockets of manual processes which are time consuming, resource intensive, and prone to human error, creating inefficiencies and limiting the organization's ability to proactively monitor and manage risks continuously. Modernisation was also slow due to both regulatory inertia and high cost. Therefore, performance of Pre-AI GRC was leaning heavily on core domain expertise, interdisciplinary collaboration(e.g. IT, sales, finance), and human soft skills such as good articulation and communication to effectively link automation with fragments of manual processes for de-risking. More than data itself, the skills to interpret available data is more valuable to diagnose problems and ensure smooth direction of GRC functions.
With AI now increasingly entrenched in GRC, the same domain specific skills to lead, collaborate, articulate, communicate and interpret data needs to be honed further for effective AI Prompt Engineering and directing a number of different AI agents (smaller and narrow) to establish a much robust type of GRC oversight. For instance asking an AI agent to “flag transactions violating GDPR.” Smaller, specialized AI agents (e.g., one for cyber risk, another for regulatory tracking) enable modular, robust oversight, aligning with tools like ServiceNow AI Agents or MetricStream AI.
Strategic Advantage: AI infuses agility into GRC functions allowing GRC professionals to diagnose, identify gaps and scale functions with DRY (’Don’t Repeat Yourself) principles very similar to their IT counterparts. Moving away from a periodic and reactive model to proactive and continuous oversight with smart scenario planning (e.g., deepfakes, supply chain interruption), and automated de-risking.
Binary Decision Making Approach
Particularly in science based organisations like research, health, engineering, aerospace and mining, the evidence-based approach is typical in decision making processes. “Evidence for” and “evidence against” an issue is the usual binary approach to problem solving and shaping decisions. For instance, certifying a drug as safe or a process as compliant. This rigid, human-driven method is slow and continuously struggles to adapt to a fast changing risk portfolio.
AI disrupts this by integrating real-time data, predictive analytics and cheaper scenario simulations, reducing reliance on binary verdicts. For example, an AI system might flag a 73% likelihood of a compliance breach, enabling proactive mitigation rather than a delayed pass/fail audit. Agentic AI goes further, autonomously adjusting protocols. For example, rerouting resources during a cyber threat without waiting for human approval. Though binary decisions will continue to persist in regulated sectors where human accountability is required. For instance in fields like health or aerospace, regulations (e.g., FDA approvals, FAA certifications) often mandate binary outcomes (approved/not approved). AI can inform these decisions with richer data, but the final call may still require a human to sign off on a binary verdict.
Strategic Advantage: This shift from binary to dynamic decision making empowers organisations to respond faster and more effectively to GRC issues. This will also require GRC professionals to hone skills in prompt engineering and probabilistic interpretation to validate and explain AI outputs. The trend will continue to prioritise human intervention for context heavy judgments to address regulatory constraints and risk of AI reliance.
The Missing Evidence
However, in high stake fields such as healthcare, aerospace, or geophysics, the critical evidence may often be the “evidence missing”, meaning critical decisions often hinge on evidence that’s incomplete or delayed. For instance a doctor under pressure to make a diagnosis may need three days for a culture to come back from the lab but non-treatment of the patient might mean death within twelve hours. This “missing evidence” creates a quandary: traditional GRC processes, reliant on complete data for risk assessments or compliance checks, falter under time constraints.
AI reshapes this challenge by “beating nature’s timeline”. Through real-time data integration, predictive analytics, and agentic systems, AI is capable of synthesizing incomplete datasets to deliver actionable insights. Consider a Phase III drug trial where a GRC professional gets a real-time alert: a single site reports a 300% spike in minor adverse events. The complete data needed to understand why. The patient details, other medications information is delayed by 72 hours. Using a generic AI prompt would fail or provide suboptimal outcomes. Instead, the GRC expert crafts a targeted prompt leveraging their domain knowledge:
{{"Act as a pharmacovigilance expert. Cross-reference this incomplete adverse event spike at Site #742 with all other real-time data: drug lot numbers and clinician IDs. Generate ranked hypotheses for the anomaly, such as a data entry error or a bad drug batch, and recommend one immediate action."}}
Guided by this expert framing, the AI instantly finds the spike correlates to one new clinician. The team contacts the site and confirms a simple dropdown menu error, averting a potential trial halt hours before the "complete" data ever arrives.
Strategic Advantage:The AI provided speed, but the GRC professional’s expertise asked the right questions of the incomplete data, turning a crisis into a corrected typo and proving that in the era of AI, domain knowledge is the new prompt engineering. AI when combined with deep domain expertise speeds bridging gaps between incomplete evidence and urgent decisions. This shift empowers organizations to act swiftly to risk and opportunities.
The Clash of Domain Experts
In science-based sectors like oil and gas, domain experts often speak different languages - literally and figuratively. Terms like “risk” and “uncertainty” mean different things to offshore drilling engineers and geologists, creating friction in high stakes GRC decisions. For example, a geologist’s “low risk” assessment based on probabilistic data might alarm an engineer seeking certainty, delaying critical risk management or compliance actions with billions at stake. These clashes, fueled by jargon and differing evidence priorities, disrupt collaboration and amplify the “missing evidence” challenge.
AI transforms this dynamic by integrating siloed data, harmonising jargon through natural language processing, and generating shared insights from datasets. Some key AI powered tools with these capabilities are Integrated Risk Management (IRM) Platforms (e.g., ServiceNow GRC, OneTrust, and MetricStream), digital twins and Data Integration Platforms(e.g., JupiterOne). Agentic AI takes it further by autonomously aligning priorities. For instance, flagging discrepancies in risk models for both parties. Making AI a powerful enabler, but it can't fully resolve interdisciplinary conflicts without a strong cultural and process framework to support continuous workforce moderation.
Strategic Advantage: GRC 2.0 provides the cultural and technical framework or the common language and shared platform that allows AI to thrive as a translator, not just a tool. Effective GRC professionals no longer just write policies; they orchestrate these platforms and design the AI prompts that ensure a geologist's "uncertainty" is automatically translated into an engineer's "preventive maintenance task." By architecting this seamless flow of insight, they resolve expert clashes, accelerate strategic responses, and build a truly resilient organization.
Path to future forward GRC
The future forward GRC function will blend AI's computational power with human judgment, contextual understanding, and interdisciplinary collaboration. It’s a live framework which aligns experts, tools and best practices for addressing aforementioned challenges and achieving full potential of GRC functions. This includes the following call to action:
Adopt GRC 2.0 frameworks that treat AI as both a tool and a managed risk.
Invest in upskilling GRC teams in AI literacy, prompt engineering, and data interpretation.
Foster a culture of collaboration between humans and AI systems, leveraging AI as a co-pilot for decision-making.
Implement modular, AI-augmented tools (e.g., IRM platforms, digital twins) that enable real-time risk intelligence.
Conclusions - AI-Native GRC is Strategic
AI is fundamentally reshaping GRC from a periodic, reactive function into a continuous, proactive strategic capability. By tackling long standing challenges, inflexible oversight, binary decision-making, missing evidence, and clashes between experts, AI introduces agility, foresight, and automation at scale. It empowers organizations to preempt risks, align interpretations across domains, and act decisively even with incomplete information. Yet, for all its analytical power, AI does not replace the GRC professional; instead, it elevates their role. The future of GRC lies in merging AI’s computational speed with human expertise in prompt crafting, contextual judgment, and ethical oversight, enabling practitioners to focus less on manual controls and more on strategic resilience.
In this AI augmented era, GRC shifts from a cost center to a competitive advantage. Organisations that embrace GRC 2.0, adopting adaptive frameworks, up-skilling teams in AI collaboration, and deploying modular agentic tools, will not only navigate complex regulatory landscapes but also build trusted, responsive, and resilient enterprises. The transformation has already begun: AI-native GRC is here, and it’s strategic.