Why did we build this product?

The rise of AI regulations globally (EU AI Act, U.S. Executive Order, state-level laws) is creating urgent demand for organizations to identify and manage their AI-related risks. Our customers need a way to evaluate their AI systems and ensure compliance across jurisdictions, frameworks, and internal policies, while also ensuring a defensible audit trail. AI Risk is our response to that need.

What problem is AI Risk solving?

AI Risk provides a structured and defensible assessment process to identify and document risks tied to the use of AI systems. It supports organizations in evaluating regulatory exposure, applying consistent risk frameworks, and establishing AI governance across legal, privacy, security, and compliance teams.

Who will use this product?

AI Risk is intended for cross-functional teams that include Compliance, Legal, AI Governance, and Risk Management. AI Steering committees and their component members. AI steering committees and executives responsible for AI governance risk and compliance will find this especially valuable.

What makes this different from other AI governance tools?

Unlike general-purpose risk registers or generic GRC tools, AI Risk: Codifies regulatory guidance (e.g., EU AI Act, NIST RMF) into configurable assessments. Supports multi-framework alignment and documentation. Provides built-in auditability and traceability.

What are the top use cases?

Regulatory Alignment: Mapping AI systems to the EU AI Act or NIST AI RMF. Pre-Deployment Review: Assessing risk before releasing a model or tool. Cross-Functional Collaboration: Legal, compliance, and AI teams jointly evaluating risk. Audit Trail: Documenting decisions and rationale for audits and regulators.

Will this tie into incident workflows?

Yes. Incidents involving AI system misuse or harm can trigger assessments. Likewise, risk findings can link back into Compliance or Privacy obligations when thresholds are met.

Can I see reporting by risk level or framework coverage?

Yes. Assessments will support: Risk distribution by system | Framework coverage dashboards | Gaps or missing documentation

Can AI Risk handle multiple systems or models at once?

Yes. Assessments are per system/model but the interface will support a portfolio-level view.

Is this built to scale with new regulations?

Yes. New frameworks, jurisdictions, or internal review processes can be added via configuration. We plan to provide prebuilt updates for major regulatory shifts post-GA.

How does AI Risk integrate with existing AI governance software?

Radar AI Risk connects seamlessly with other AI governance tools, enabling centralized oversight without the need to replace existing systems.

Can AI Risk support high level reporting for executives and boards?

Yes. The platform offers high level dashboards and scorecards designed for leadership to quickly assess organizational AI risk posture.

How does AI Risk help with training data compliance?

AI Risk tracks and documents training data quality, provenance, and use, ensuring compliance with privacy and ethical guidelines.

Is Radar AI Risk suitable for organizations just beginning to adopt AI?

Absolutely. Whether you are starting small or scaling enterprise-wide, Radar AI Risk provides the structure to manage compliance from the beginning of development and deployment.

Search Results | RadarFirst

Key Trends in AI Governance: Insights from IAPP GPS 2026

At IAPP Global Privacy Summit 2025, a clear shift emerged as organizations prioritize AI governance, privacy, and safety over pure productivity. As AI systems grow more complex, experts stress that proactive governance is now a critical business priority.

2026 IAPP GPS Key Takeaways

Chris Washington, Chief Technology Officer, and Alex Layng, VP of Product, highlighted a shift from traditional privacy concerns to a broader focus on AI governance, emphasizing the need for organizations to actively manage and monitor AI-generated outputs. As native AI technologies and agentic systems rapidly expand, they stressed that companies must implement proactive governance […]

Episode I: Policy Is Not Proof. What Regulators Actually Expect

AI is outpacing compliance, and organizations must be ready to govern responsibly. Privacy professionals already have the skills to lead the charge—risk assessment, process standardization, and building trust with regulators. In this session, industry leaders Ron Whitworth (Truist) and Lauren Wallace (RadarFirst) reveal why privacy maturity is the launchpad for AI governance and how […]

Why the Future of Privacy Programs Requires a Unified Platform Approach

Privacy is no longer just about reacting to incidents. Organizations must now manage AI risk, data, and compliance in a continuous, connected way. A unified platform brings these efforts together, giving teams the visibility and control needed to operate efficiently and stay ahead of evolving regulations.

When Does the 30-Day Clock Start Under Regulation S-P? A Guide for Privacy Incident Management

When does the 30-day clock start under Regulation S-P? Learn how “awareness” drives privacy incident management and compliance timelines.

2026 Privacy Incident Benchmarking Report

Data-driven insights for privacy, compliance, legal, and IT risk leaders.

Colorado’s AI Policy Shift Signals a New Era: Why AI Incident Management Is Now a Regulatory Imperative

Colorado’s updated AI policy marks a shift from governance to accountability. As AI systems drive consequential decisions, failures are becoming regulatory events. Organizations must be prepared to detect, investigate, and respond to AI incidents to manage privacy risk and meet evolving compliance expectations.

Why AI Incident Management Must Evolve: Insights from NIST’s New Monitoring Report

NIST’s latest privacy engineering efforts reinforce a key shift. Privacy must be embedded into risk management, not treated as a standalone function. Here’s what that means for today’s teams.

Privacy Workflow Platform for DSARs, PIAs & Vendor Risk

Modern privacy programs require more than incident response. This guide shows how to operationalize DSARs, PIAs, and third-party risk assessments within a unified, defensible platform.

Where Broker-Dealers Will Operationally Fail Under Amended Regulation S-P

[…] financial risk management and regulatory risk management perspective, this shift is significant. In 2024, the SEC adopted major amendments to Regulation S-P, transforming it from a safeguards-focused privacy rule into a documented federal incident response and notification regime with defined timelines, harm standards, vendor escalation requirements, and recordkeeping obligations. For broker-dealers and other covered […]

Navigating Elevated Cyber Risk. The Regulatory Decision Layer of Incident Management

[…] cyber threats escalate amid global conflict, organizations face growing pressure to determine whether security events involve personal data and trigger regulatory obligations. While security tools detect incidents, privacy and legal teams must assess regulatory impact, document decisions and manage notification requirements. Structured privacy incident management helps organizations move from technical alerts to defensible regulatory […]

HIPAA, AI Incident Management, and Privacy Tools for Compliance Leaders

As federal agencies explore using AI to detect and prevent healthcare fraud, privacy and compliance leaders face a critical reality. Innovation cannot come at the expense of protected health information. AI systems rely on vast amounts of claims, billing, and patient data, which means privacy incident management must evolve beyond traditional breach response. For […]

The Amended Regulation S-P Incident Response Framework: From Awareness to Defensible Documentation

[…] federal notification timeline. Each step must be supported by structured documentation that demonstrates when decisions were made, by whom, and based on what facts. As firms modernize privacy incident management programs, many are turning to governed AI incident management workflows to standardize intake, enforce timelines, and preserve audit ready records. Under amended Reg S-P, […]

AI in Healthcare Fraud Detection: What It Means for Privacy and Compliance Leaders

As federal agencies explore using AI to detect and prevent healthcare fraud, privacy and compliance leaders face a critical reality. Innovation cannot come at the expense of protected health information. AI systems rely on vast amounts of claims, billing, and patient data, which means privacy incident management must evolve beyond traditional breach response. For […]

Why Privacy Incident Management and AI Risk Response Are Now Central to Trust and Compliance

As AI legislation expands and privacy enforcement intensifies, incident response is evolving. It is no longer just about data breaches. It now includes AI driven harms, automated decisions, and model accountability. Organizations need integrated privacy and AI incident management built on strong data governance and clear workflows. Regulators expect operational readiness, not just written […]