What

Clearly AI is an AI-assisted platform for security and privacy teams that automates design reviews, threat modeling, and related assessment workflows. It is positioned as a way to help organizations catch high-risk issues before production while reducing the manual effort typically required to review new features, products, and vendors.

The product appears to serve product security, privacy, third-party risk, and AI governance teams, especially in fast-moving engineering environments where development speed outpaces security review capacity. Its core workflow is to connect to existing tools, ingest internal policies and standards, automatically assess work against those criteria, and present prioritized findings for human review.

Features

Automated threat modeling and design reviews — Generates security review outputs from product documentation and helps teams identify high-risk issues earlier in the software delivery lifecycle.
Policy- and standards-aware analysis — Ingests an organization’s own policies, frameworks, and security requirements so assessments reflect internal expectations rather than generic rules.
Workflow coverage across multiple domains — Supports product security, privacy, third-party risk, and AI governance use cases in a single platform, which can reduce process fragmentation.
Integrations with common work tools — Connects with systems such as Jira, GitHub, Confluence, and Google Drive to fit into existing documentation and development workflows.
Human-in-the-loop review — Surfaces prioritized findings for teams to review and decide on, which helps preserve human judgment in security-sensitive decisions.
Enterprise-oriented controls and safeguards — The site emphasizes enterprise-grade security controls and built-in measures against hallucinations, though detailed technical controls are not described on the page.

Helpful Tips

Validate depth of analysis by use case — For products like this, assess performance separately for threat modeling, privacy assessments, and third-party reviews, since quality can vary by workflow.
Check how well it learns internal standards — The practical value often depends on how accurately the system applies your policies, review criteria, and documentation norms in real scenarios.
Plan reviewer workflows early — Human-in-the-loop systems work best when teams define who triages findings, how exceptions are handled, and when security sign-off is required.
Audit source traceability and evidence quality — Prioritize tools that show where findings came from in documents or systems so reviewers can verify conclusions quickly.
Confirm integration depth, not just connector availability — A listed integration does not always mean full workflow automation, so buyers should verify what data is synced, triggered, and written back.