Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

For more than twenty years, I have worked in roles focused on Governance, Risk, and Compliance (GRC), Identity and Access Management (IAM), and enterprise risk transformation. My background includes experience with global banks, Big 4 advisory firms, and leading GRC programs across multiple regulated industries. Through this work, I have seen firsthand how regulatory requirements, audit demands, and cyber-risk concerns drive technology investments. More recently, I have observed how AI is beginning to reshape these areas, creating new opportunities and challenges for organizations.

 

Q2. Which AI-driven GRC/IAM capabilities do you expect enterprises to invest in between 2026–2030 due to regulatory pressure, audit expectations, or cyber-risk exposure—and what spend shifts do you anticipate each to drive?

Looking ahead to the next five years, I expect organizations to focus their investments on capabilities that provide real-time assurance and predictive risk intelligence:

Continuous Control Monitoring: It is one area where I see significant change. Regulators are moving away from periodic audits and instead expect real-time compliance validation. As a result, organizations are shifting budgets from manual testing to platforms that use telemetry to continuously monitor controls.

AI-based Identity Threat Analytics: Predictive identity risk engines will replace static IAM tools, driven by insider threat concerns and zero-trust mandates.

Regulatory intelligence automation: This is another area where I see value. AI can help organizations keep up with global regulatory changes, reducing the manual effort required for compliance and enabling faster policy updates.

Behavioural Risk Scoring: AI-driven anomaly detection for insider threats will redirect spend from traditional SIEM toward integrated GRC/IAM ecosystems.

Explainable AI for Audit: Platforms that provide transparent AI decisions will gain traction as auditors demand interpretability.

 

Q3. What % of global BFSI organizations are moving from periodic control testing to continuous, real-time monitoring, and what’s driving that shift?

At present, around 35 to 40 percent of global banking and financial services organizations have either piloted or partially adopted continuous, real-time monitoring. I expect this number to rise to over 70 percent by 2030. Several factors are driving this shift:

• Regulatory mandates such as DORA (EU) and OCC guidelines (US)
• Rising cost of failed audits and operational risk events
• Cloud-native architectures enabling telemetry-driven monitoring

 

Q4. What proprietary data signals or telemetry sources are becoming differentiators for next-generation AI-driven GRC/IAM platforms?

Differentiation will hinge on data richness and interpretability:

Identity Behavior Graphs: Mapping anomalous access patterns across hybrid environments.

Regulatory Change Feeds: Enriched with NLP-driven interpretation for proactive compliance.

Control Effectiveness Telemetry: Real-time signals from cloud and on-prem systems.

Third-party Risk Signals: Combining external threat intelligence with contractual compliance data.

 

Q5. Across enterprise programs, what vendor capabilities consistently translate into multi-region rollouts, and renewal-driven expansion—and what failure patterns reliably prevent vendors from scaling?

In my experience, several key factors consistently drive successful multi-region rollouts and expansion:

• Adopting an API-first architecture, which enables seamless integration
• Localizing regulatory content to ensure compliance with multiple jurisdictions
• Building scalable data ingestion pipelines that accommodate a wide variety of telemetry sources

However, I have repeatedly observed certain issues that prevent vendors from scaling effectively:

• Over-reliance on manual configuration
• Lack of AI explainability (audit pushback)
• Poor integration between identity lifecycle management and legacy HR or ERP systems

 

Q6. What are the biggest challenges in integrating AI-driven risk intelligence with legacy GRC and IAM systems, and which vendors or architectures are proving able to overcome these at scale?

Challenges

• Persistent data silos in legacy systems
• Latency in control data from on-prem environments
• Vendor lock-in due to proprietary connectors

Architectures that work

• Event-driven microservices with streaming telemetry
• Vendors leveraging open standards (SCIM, OpenID) and graph-based risk engines for interoperability

 

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

How does your platform measurably reduce regulatory non-compliance risk and audit costs at scale across multi-region enterprises?

Closing Perspective

Best of my experience, the convergence of AI, regulatory technology, and identity security is not just a trend; it’s a structural shift. Enterprises that embrace real-time risk intelligence, explainable AI, and open architectures will lead the next decade of compliance innovation. Vendors that fail to deliver scalable, transparent, and integrated solutions will struggle to survive in a market where trust and auditability are non-negotiable.