Future of Agentic AI in Governance
Panel Discussion with Industry Leaders - How Autonomous AI Agents Are Reshaping Compliance, Risk, and Governance
Future of Agentic AI in Governance
Panel Discussion
About This Panel
Agentic AI - AI that does not just answer questions but autonomously takes actions, monitors environments, and executes multi-step tasks - is no longer a research concept. It is being deployed in enterprise GRC platforms today. And it is fundamentally changing what compliance automation means.
In this 58-minute panel discussion, Col. Saurabh Agrawal (Founder, Cyber Soldiers) is joined by industry leaders to discuss:
- What agentic AI actually means in a GRC context - and what it does not mean
- Real deployments: where AI agents are delivering genuine compliance value today
- The risks of agentic AI in regulated environments - and how to govern the governors
- What India's regulatory landscape means for AI agent deployment
- Where this technology will be in 24 months - and what CISOs need to prepare for now
This session is designed for: CISOs, Compliance Heads, Risk Officers, GRC Platform Evaluators, and senior security professionals exploring AI-driven compliance automation.
Featured Speakers
Col. Saurabh Agrawal, SM (Retd)
Founder & CEO, Cyber Soldiers
Expertise: AI Governance, Cyber Security Strategy
Industry Expert Panel
CISOs from Leading Enterprises
Expertise: Enterprise Security, Compliance Leadership
Download the panel discussion summary and key takeaways
A 2-page PDF covering every actionable insight from this session.
Chapter Markers
Click any chapter to jump to that section of the webinar.
Introduction and panelist credentials
What is agentic AI? Defining the term correctly
Real-world GRC agent deployments: what is actually working
The risks of agentic AI in regulated environments
India's regulatory landscape and AI agent governance
The 24-month outlook: what CISOs must prepare for
Audience Q&A
Session Transcript Summary
Introduction (00:00 – 06:30)
The session opens with a provocation: "The compliance team of 2027 will not be reviewing audit evidence - an AI agent will have already reviewed it, flagged the gaps, assigned remediation tasks, and updated the risk register. The compliance team's job will be to oversee the agent, not do the work itself."
Panelists introduce themselves and share their current experience deploying or evaluating agentic AI in enterprise environments. The facilitator frames the session around one central question: "Is agentic AI in GRC a genuine step-change, or is it a new label on old automation?"
What is Agentic AI? Defining the Term Correctly (06:30 – 14:00)
The term "agentic AI" is being overused and misapplied. This segment provides a precise definition:
What agentic AI is:
An AI agent is a system that perceives its environment, makes decisions, and takes autonomous actions to achieve a defined goal - without requiring human input at each step. In a GRC context, an AI agent does not just answer the question "is this control compliant?" - it monitors the control continuously, detects when it fails, generates evidence of the failure, assigns a remediation task to the right owner, tracks completion, and updates the compliance dashboard automatically.
What agentic AI is NOT:
- • A chatbot that answers compliance questions (this is a retrieval system, not an agent)
- • A dashboard that shows you compliance status (this is reporting, not agency)
- • Automated evidence collection (this is integration, not agency)
True agentic AI in GRC is characterised by three properties: Autonomy (it acts without being asked), Goal-directedness (it pursues a defined compliance objective across multiple steps), and Adaptability (it adjusts its actions based on what it observes in the environment).
Real-World GRC Agent Deployments (14:00 – 24:30)
The panel shares current deployment experiences across three agent categories:
Category 1 - Evidence Agents
The most mature and widely deployed. These agents integrate with cloud infrastructure, identity systems, HR platforms, and security tools to collect compliance evidence continuously. Organisations using evidence agents report reducing audit preparation time from 3–6 weeks to 2–3 days.
Category 2 - Monitoring Agents
These agents continuously evaluate whether controls are operating effectively and alert when a control fails or degrades. Examples include access review agents, patch compliance agents, and policy acknowledgement agents.
Category 3 - Risk Assessment Agents (Emerging)
These agents analyse data from multiple sources - threat intelligence feeds, control status, asset inventory, incident history - and dynamically update risk scores in real time. This is the most complex category and the least mature, but represents the most transformative potential for GRC.
The Risks of Agentic AI in Regulated Environments (24:30 – 36:00)
The panel addresses the critical question: "If I deploy AI agents to manage compliance, how do I govern the AI itself?"
Risk 1 - Autonomous actions with compliance consequences
An AI agent that autonomously remediates a control failure is taking an action with real operational consequences. Governance of agentic actions requires: clear action boundaries, human-in-the-loop thresholds, and full audit trails.
Risk 2 - Explainability under Indian regulations
RBI and SEBI increasingly require that automated decisions be explainable. Agents must produce explainable, traceable outputs - not black-box decisions.
Risk 3 - Adversarial manipulation of agents
Adversaries who understand how an agent works can craft inputs designed to cause misreporting. Agents must be designed with adversarial robustness testing.
Risk 4 - Over-reliance and skill atrophy
As AI agents automate more of the compliance workflow, teams risk losing deep framework knowledge. Governance of agents requires deep framework expertise, not less.
See Netra's AI agent architecture in action
A live walkthrough of how our compliance agents monitor, detect, assign, and evidence automatically.
The 24-Month Outlook: What CISOs Must Prepare For (46:00 – 52:00)
The panel's consensus view on where agentic AI in GRC is heading:
By end of 2026:
Multi-agent GRC systems - where specialised agents for evidence, monitoring, risk, and remediation work together - will be commercially available from leading platforms.
By 2027:
The regulatory audit process itself will evolve. Auditors will begin accepting AI-generated continuous evidence trails as primary audit evidence - shifting from point-in-time to continuous assurance.
What CISOs must do now:
- Evaluate your GRC platform's agent architecture - is it genuinely agentic or is "AI" a marketing label?
- Define your agent governance policy - what actions can agents take autonomously, and what requires human approval?
- Map your AI agent deployments to your DPDPA register - agents processing personal data are within scope
- Start with evidence agents - they are mature, low-risk, and deliver immediate value
- Build your team's agent oversight skills - compliance professionals must understand how to supervise AI agents, not just use them
Audience Q&A Highlights (52:00 – 58:00)
Q: How do we justify the cost of AI-powered GRC to a board that sees compliance as a cost centre?
A: Frame it as risk reduction with a calculable number. DPDPA maximum penalty: ₹250 crore. Annual cost of an AI-powered GRC platform: a fraction of that. The question is not "can we afford this?" - it is "can we afford not to have it?"
Q: Our IT team is worried that compliance agents will have too much access to production systems. How do we address this?
A: This is a legitimate concern. Address it by: defining minimum necessary access for each agent function, deploying agents with read-only access where possible, ensuring agent actions go through standard change management, and maintaining full agent audit trails that IT can review independently.
Q: Which framework should we start with if we are deploying our first AI agent?
A: Start with access review automation - it is the highest-volume, most time-consuming manual compliance task, it is framework-agnostic (ISO 27001, SOC 2, and DPDPA all require it), and the ROI is immediately visible. Once running, evidence agents for patch compliance and policy acknowledgement are natural next steps.
See Netra's AI agents in action
A live 20-minute demo showing how our evidence agents, monitoring agents, and risk agents work together to keep your organisation continuously audit-ready.
Watch Full Webinar
58-minute panel discussion with industry leaders on AI governance.
On This Page
Share
Related Resources
The State of Compliance and Audit in India 2026
An overview of governance, risk and compliance trends shaping Indian organisations, with practical priorities for security leaders.
Interactive GuideAI-Powered Compliance Checklist
A practical, step-by-step checklist to achieve compliance readiness using AI automation. Covers ISO 27001, SOC 2, and SEBI CSCRF frameworks.