Blog - cybersecurity reporting

From Black Box to Glass Box: How Explainability in AI Supports Regulatory Readiness

AI explainability is no longer optional. As global regulations like the EU AI Act take hold, CISOs and risk leaders must ensure their AI systems are transparent, traceable, and defensible. Learn how explainable AI supports compliance, strengthens risk governance, and prepares your organization for regulatory scrutiny.

AI Governance Starts with Why: A Strategic Guide for Policy-Driven Leaders

Effective AI governance starts with purpose, not paperwork. Learn how policy-driven leaders align AI strategy with risk, trust, and business goals.

Preparing and Facilitating Effective BCR Tabletop Exercises

Discover how to plan and facilitate impactful business continuity and resilience (BCR) tabletop exercises that go beyond box-checking. Learn strategies for scenario design, team engagement, and continuous improvement to strengthen your organization’s crisis response and operational resilience.

Why a ‘Human in the Loop’ is Essential for AI-Driven Privacy Compliance

As AI becomes more embedded in privacy operations, human oversight is more important than ever. This article explores why human-in-the-loop (HITL) systems are essential to responsible automation—offering clarity, accountability, and regulatory alignment in privacy compliance programs where trust and context matter most.

6 Steps to Optimize Your Organization’s Risk Matrix 

Facing a barrage of ever-changing regulations? Discover 6 actionable steps to optimize your organization’s risk matrix—learn how to identify key regulatory requirements, quantify risks with data-driven metrics, and implement ongoing monitoring to proactively manage threats. Enhance compliance, streamline risk assessments, and stay ahead with a robust incident management strategy using proven tools like Radar® Compliance.

Responsible AI in Privacy & Compliance: How Transparency Builds Trust

In highly regulated industries, AI-driven privacy compliance demands transparency to build trust and satisfy regulators. Transparency isn’t just ethical—it’s a regulatory imperative for demonstrating consistent, repeatable, and auditable AI processes.