From Automation to Autonomy: How Generative and Agentic AI Are Redefining GRC

For today’s chief information security officer (CISO), governance, risk, and compliance (GRC) is no longer a back-office reporting function. In fact, it is rapidly becoming the control layer for enterprise risk and trust. As regulatory scrutiny intensifies and digital ecosystems grow more complex, traditional GRC models, anchored in periodic assessments and manual workflows, are proving insufficient.

Generative AI (Gen AI) and agentic AI are reshaping this reality. They are enabling a shift from reactive compliance to continuous assurance, from static risk registers to real-time risk visibility, and from manual workflows to orchestrated risk management.

This transformation is unfolding across two critical dimensions.

1. AI for GRC is redefining how CISOs monitor, assess, and respond to risk.
2. GRC for AI introduces a new mandate: governing AI systems themselves as they scale across the enterprise.

Together, these shifts position GRC as both a beneficiary of AI and the primary mechanism for controlling its risks.

The Imperative for Modern GRC

The urgency is reflected in the scale and cost of risk. According to the US Federal Bureau of Investigation’s Internet Crime Report (2025), the losses incurred due to internet crime exceeded $20 billion during the year. Interestingly, the number of complaints increased relatively at a slower pace than the losses incurred, indicating that the average loss per incident is growing.

Reflecting the growing recognition of AI’s systemic impact, governments worldwide are moving from exploratory guidelines to enforceable mandates. As noted in Avasant’s Responsible AI Platforms 2025 Market Insights™, over 1,000 AI-related regulations have been enacted across 69 countries, with the US alone introducing 59 in 2024—marking a 2.5x increase over 2023. Figure 2 below presents the timeline of recent Gen AI governance policies introduced by various countries.

At the same time, most organizations remain constrained by fragmented ownership models, siloed data, and legacy GRC tools. Risk insights are often delayed, incomplete, or disconnected from operational reality. This creates a critical gap: while threats and regulatory expectations evolve in real time, GRC processes continue to operate in periodic cycles.

To close this gap, CISOs are increasingly prioritizing continuous, data-driven GRC models. This requires not just platform modernization, but a fundamental shift in how risk is sensed, interpreted, and acted upon—one that AI is uniquely positioned to enable.

AI for GRC: From Reactive Oversight to Continuous Risk Management

The application of Gen AI and agentic AI within GRC is fundamentally changing how CISOs operationalize risk management. Instead of relying on static workflows, AI-enabled GRC platforms continuously ingest signals across the enterprise, interpret them in context, and trigger actions in near real time.

1. Continuous control monitoring: One of the most immediate impacts is in continuous control monitoring, where AI systems validate control effectiveness by analyzing system logs, configurations, and audit artifacts on an ongoing basis. This shifts assurance from periodic testing to continuous validation. Platforms such as ServiceNow and Drata are embedding AI and automation into control monitoring workflows, enabling continuous evidence collection, real-time anomaly detection, and automated alerts. The outcome is not just efficiency; it is a materially improved risk posture.

   Lemonade, a consumer-focused insurance company operating in the US and Europe, illustrates this shift through its implementation of Drata’s continuous compliance platform. The company reduced audit preparation effort by up to 80% and substantially reduced the time spent interacting with auditors during its SOC 2 audit. For CISOs, this translates into greater confidence in control effectiveness without the operational burden of traditional audits.

2. Risk identification and prediction: Beyond monitoring, AI is enabling a more proactive and predictive approach to risk. By integrating internal telemetry with external threat intelligence and frameworks such as MITRE ATT&CK, AI-driven models can identify emerging threats before they materialize.

   Service providers such as PwC and TCS are embedding multi-agent architectures and ML models into their platforms, enabling enterprises to dynamically adjust risk scores in response to evolving conditions. This represents a critical shift from static risk registers to adaptive, real-time risk management systems, enabling CISOs to prioritize threats based on real-time business impact.

3. Third-party risk management (TPRM): The transformation is even more pronounced in TPRM, an area historically constrained by manual processes and limited visibility. Agentic AI is replacing periodic, questionnaire-driven assessments with continuous monitoring models. AI agents can autonomously retrieve vendor data, validate responses, and correlate external risk signals such as cyber posture or financial exposure.

   Providers such as Wipro and HCLTech are augmenting their GRC and TPRM capabilities through AI-driven document processing and ecosystem integrations. For instance, Wipro has partnered with ServiceNow and Black Kite to deliver an AI-powered TPRM managed service on the ServiceNow platform, combining workflow automation with continuous third-party security monitoring and AI-based document scanning and gap assessment to improve accuracy while reducing assessment timelines. Similarly, HCLTech is enhancing TPRM through integrated risk data and insights, automated due diligence, and continuous monitoring capabilities, enabling more proactive vendor risk evaluation and remediation. For CISOs, this significantly reduces blind spots in the extended enterprise while improving both speed and depth of risk assessments.

4. Regulatory mapping and compliance reporting: Gen AI is also redefining regulatory mapping and compliance reporting, one of the most resource-intensive areas of GRC. By interpreting regulatory texts and mapping them to internal controls, AI systems can generate audit-ready narratives, automate compliance documentation, and continuously track regulatory changes. Platforms such as MetricStream, ServiceNow, and Vanta are embedding these capabilities directly into GRC workflows.

   Duolingo, a language-learning company, adopted Vanta to demonstrate the operational impact of AI-driven compliance automation. The platform enabled Duolingo to centralize its compliance processes into a single system of record, streamline evidence collection, and reduce the manual effort required for audit readiness, supporting a more efficient path to ISO 27001 certification. This shift allows CISOs to move from reactive reporting to more continuous, real-time compliance oversight, reducing both operational effort and regulatory risk.

5. Autonomous response and remediation: The most transformative development, however, is the emergence of autonomous response and remediation. Agentic AI is enabling closed-loop GRC systems where risks are not only detected but also acted upon through orchestrated workflows. AI agents can initiate remediation workflows, orchestrate cross-functional actions, and generate executive-level insights.

CISO Takeaways: AI for GRC

According to Avasant, CISOs must rethink how GRC is operationalized in an AI-driven environment. The shift is not incremental—it requires a move toward continuous, risk-driven management.

• Move from audit readiness to continuous assurance. Leading enterprises are collapsing audit cycles into always-on validation through AI-driven control monitoring, significantly reducing both risk exposure and operational overhead.
• Prioritize platforms over point solutions. Move away from fragmented point solutions toward unified, AI-enabled GRC platforms that can deliver real-time risk visibility and integrated workflows.
• Shift focus from detection to orchestration. The true value of agentic AI lies in autonomous execution—enabling systems not only to identify risks but also to initiate remediation and orchestrate enterprise-wide responses.

GRC for AI: Establishing Control Over the AI Enterprise

As enterprises scale AI adoption, CISOs face a new and equally critical challenge: governing AI systems themselves.

According to Avasant’s Applied AI Services 2024–2025 Market Insights™, April 2025 publication, 68% of Gen AI projects are in production, while 30% of agentic AI projects have moved past the pilot/POC stage. Unlike traditional IT systems, AI introduces unique risks, ranging from bias and lack of explainability to model drift and regulatory noncompliance, that require dedicated governance frameworks.

In our Governance, Risk, and Compliance Services 2026 Market Insights™, we called out the need to govern the complete AI value chain:

1. Discover: The starting point for this is visibility. CISOs must establish a comprehensive view of all AI assets across the enterprise, including models, datasets, and agents. Platforms such as ServiceNow’s AI Control Tower and OneTrust’s AI governance solutions are enabling this by providing centralized inventories and life cycle tracking.

   Zendesk’s deployment of OneTrust illustrates how organizations can detect unauthorized AI usage, centralize controls, and significantly reduce governance overhead. Without this visibility, governing AI at scale becomes untenable.

2. Classify: Once visibility is established, organizations must implement risk-based classification frameworks aligned with emerging regulations such as the EU AI Act. This involves assessing AI systems by risk tier and evaluating attributes such as fairness, bias, and explainability.

   Platforms such as IBM watsonx.governance are enabling enterprises to operationalize this at scale, managing thousands of AI use cases while aligning with frameworks such as ISO 42001 and NIST AI RMF. For CISOs, this ensures that AI deployments are not only innovative but also compliant and defensible.

3. Monitor: Continuous oversight is equally critical. AI models are inherently dynamic, requiring real-time monitoring for drift, bias, and performance degradation. AI governance platforms enable continuous tracking and monitoring of model behavior and automated alerts for deviations. This ensures that AI systems remain trustworthy throughout their life cycle, rather than only at deployment, a critical requirement as regulators increasingly scrutinize ongoing compliance.
4. Control: In parallel, CISOs must enforce runtime controls and guardrails to manage AI behavior in production environments. These controls prevent unsafe outputs, enforce organizational policies, and trigger remediation workflows when violations occur.

   Providers such as Infosys and HCLTech are embedding these capabilities into their AI governance solutions, enabling proactive risk management.

Importantly, agentic AI is also beginning to automate governance itself. AI agents can orchestrate approval workflows, compliance checks, and audit processes across the AI life cycle, reducing manual overhead while maintaining accountability through human-in-the-loop mechanisms. This is essential for scaling AI adoption without proportionally increasing governance complexity.

Takeaways for CISOs

As AI adoption scales, Avasant emphasizes that governance must evolve in parallel, becoming a core pillar of the CISO agenda:

• Establish a centralized AI inventory. You cannot secure what you cannot see. The foundation of AI governance is full visibility. Organizations cannot govern AI effectively without a centralized inventory of models, datasets, and agents.
• Adopt risk-tiered governance early. Regulatory expectations around explainability, fairness, and accountability will intensify, making early adoption of structured classification frameworks critical.
• Embed continuous monitoring into AI governance. Treat AI governance as an ongoing process, with real-time monitoring for drift, bias, and compliance deviations rather than point-in-time validation

The Road Ahead: From Risk Management to Autonomous Risk Intelligence

Looking ahead, Avasant expects the role of a CISO to evolve from oversight to orchestration. Rather than managing discrete controls and compliance processes, CISOs will increasingly oversee AI-driven systems that increasingly automate risk management processes across the enterprise. This will require new operating models, greater collaboration across business and technology functions, and a stronger emphasis on human-in-the-loop governance to ensure accountability and trust.

Ultimately, the organizations that succeed will be those that embrace GRC as a strategic capability—one that enables resilience, accelerates decision-making, and builds trust in an increasingly AI-driven world. From Avasant’s perspective, the future of GRC will not be defined by compliance alone, but by the ability to operationalize intelligent, autonomous, and governed risk management at scale.

For CISOs, the mandate is clear: move beyond compliance, embrace AI-driven transformation, and position GRC as the control plane for enterprise resilience and trust.

By Gaurav Dewan, Research Director, Asmita Gaur, Research Analyst