Nikolay Donets

Building AI risk intelligence for systems that act

Peer-reviewed AI risk intelligence for design reviews, launch decisions, and live operations. Use it on the web or in MCP clients.

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Risks Scored
63,640
Scored and classified
Papers Analyzed
10,839
Peer-reviewed sources
Updated
Weekly

See what's new this week in research updates

The same evidence where your team works

Use the web product or connect in Claude, Codex, Cursor, or VS Code. Same scored risks. Same research base. Same confidence.

We're moving a support agent to live traffic next week. Review the main risks and launch checks.

Running a grounded review

Inspect system

Mapped tools, retrieval, and sensitive-data paths.

Query risk intelligence

Matched against 63,640+ scored risks and pulled the strongest evidence.

Injection Leading to Unauthorized Data Exfiltration

Security and resilience High score 5312.4

PII Dissemination via Application

Privacy and data governance High score 4876.2

Exploitation of Tool-Calling

Operational reliability High score 4620.8

Trace evidence

Linking guidance back to papers and excerpts.

Draft launch guidance

Preparing mitigations, owners, and launch checks.

Evidence from real systems — ready before you ship

Donets Risk Intelligence shows what research already says about systems like yours. Risks are scored, traced to papers, and mapped to your frameworks before launch.

Your team proposes a new automated decision system. Here's what happens next.

  • System specced

    A team specs a new automated decision system. Donets Risk Intelligence returns the risks already documented in peer-reviewed research on systems like yours.

  • Every risk traced to published research

    Each risk is scored and linked to a peer-reviewed paper.

    Bias amplification in automated decisions — evidence score 8.7

    "Automated pipelines that remove human review amplify pre-existing bias in training data, particularly in high-stakes financial decisions..." — Fairness and Abstraction in Sociotechnical Systems, 1,340 citations

    NIST AI RMF MAP 2.3 Art. 10 EU AI Act

  • Your team knows what to do next

    The documentation builds itself alongside the work.

    • Risks identified and traced to research
    • Framework controls mapped
    • Documentation ready for audit
    Team action: choose a mitigation approach for the identified risk

Every risk maps to: NIST AI RMF · ISO 42001 · EU AI Act · UK SS1/23 · US SR 11-7 · Singapore MAS FEAT

Evidence from real systems. Your team builds.

One objective baseline, tailored for every stakeholder

One evidence base for engineering, compliance, vendor review, and leadership.

Move audits and compliance faster

Map systems to the EU AI Act, NIST AI RMF, and ISO 42001 without chasing engineering for answers.

"Impact assessment generated. 12 risks mapped to Article 9 controls. Audit-ready."

Ship AI with fewer surprises

Find vulnerabilities before release and surface the context engineers need in the workflow they already use.

"Pre-deployment review passed. Injection and adversarial risks mitigated."

Assess third-party models independently

Evaluate off-the-shelf models against an independent baseline instead of relying on vendor self-disclosure.

"Vendor evaluation complete. Independent risk profile established."

Give leadership a clearer baseline

Give leadership a clear view of exposure, mitigations, and readiness for deployment.

"Leadership review ready. Exposure, mitigations, and rollout status in one place."

Three steps to your first action plan

1

Create your account

Sign up and get your API key. One key connects your browser, your tools, and your workflows.

2

Connect your tools

Seamlessly integrate with your existing workflow — Claude, Cursor, Codex, or your internal apps.

3

Take action

  • Turn findings into launch checks, controls, and owners.
  • Move from a draft review to a concrete release decision.
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Build AI you can stand behind.

From PR review to board review, one evidence base traced to published research.

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Explore Risk Intelligence

Every risk is scored by evidence strength and mapped to the frameworks your team uses, from EU AI Act to NIST and ISO 42001.

Enterprise

Team access & SSO Data residency GRC integration Methodology documentation Dedicated support
Enterprise Options

Turn compliance from a bottleneck into an automated workflow. Suggest an integration.