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Home › Tools › Agentic AI Deployment Gate

Agentic AI Deployment Gate

Use this screen to identify control gaps and review priorities for an AI agent. A questionnaire result does not authorise production use.

3-5 minutes 10 scored questions No login
  • Checks action scope, tool access, data sensitivity, human override, kill switch, logging, delegation, testing, ownership, and business impact.
  • Applies critical override logic when structural blockers exist, even if the numeric score looks moderate.
  • Focuses on diagnostics and complements the AI Controls Toolkit rather than duplicating implementation artifacts.
Run the gate Download Security Playbook (Excel) Read the framework guide
Header hook image
AI agent operating inside a bounded autonomy control perimeter with human approval checkpoints and monitored tool access
Production decisions for AI agents hinge on bounded autonomy, least privilege, human override, and a provable audit trail.
In-browser scoringAssessment logic runs on the page. No backend scoring or account creation.
Critical override logicIrreversible actions, broad write access, weak human approval, poor logging, and missing kill switch raise the final risk state.
No stored answersThe page does not save assessment inputs. Close the browser and the session is gone.

Run the deployment gate

Answer every question based on the design you actually plan to deploy, not the control model you hope to add later.

Question 1 of 10
Informational only. Not legal advice. This tool does not determine compliance with any law, regulation, or standard.

Final score
0
Out of 120
Critical triggers
0
Override conditions hit
Recommended state
Operational decision

Top 5 missing controls

Red-flag warnings

If any of these appear, the issue is structural, not cosmetic.

    Why this matters

    Agentic deployment decisions need more than instinct.

    Use the result to define the ownership, vendor review and agentic governance evidence you need. ACT includes supplier and subprocessor review records and proposed contract wording for local legal and procurement review. Your team verifies the evidence and resolves open issues.

    Enquire about ACT Download Security Playbook (Excel) Read the agentic governance guide

    What this gate is actually checking

    Most teams evaluate AI agents the wrong way. They focus on model quality, response quality, or a successful demo. That is not the decision you need to make before production. The real question is whether the agent is governable once it can call tools, write into systems, touch sensitive data, or chain actions over time. This gate is built around that operational question, not a marketing definition of agent readiness.

    The scoring model intentionally concentrates on the control points that break first in live environments: action scope, tool access, data sensitivity, human override, kill switch ownership, usable logging, delegation discipline, structured testing, named ownership, and material business impact. Those are the places where a prototype becomes an operational risk. The tool treats them as first-order governance issues because production failures usually stem from weak control architecture, not from a lack of optimism.

    What it does

    It classifies your current design into one of four operational states and exposes the missing controls that matter most right now.

    What it does not do

    It identifies control gaps and review priorities. It does not generate a policy, matrix, risk register or approval workflow.

    How the scoring works

    The numeric score runs from 0 to 120. Lower is better. But the numeric score is not the whole story. The gate also applies critical override logic when specific structural blockers are present. That means an agent can still land in a worse result state even if the raw score looks moderate. This is deliberate. Broad write access, no mandatory human approval, no tested kill switch, no usable audit trail, or material legal or customer impact are not issues you average away.

    That approach is aligned with the practical logic behind risk-based governance. ISO/IEC 42001 expects organizations to integrate AI-specific risk assessment, impact assessment, internal audit, monitoring, and documented information into the management system. NIST AI RMF organizes the work across GOVERN, MAP, MEASURE, and MANAGE, while the Playbook stresses prioritization based on impact and likelihood. The 2025 NIST Cyber AI Profile draft also reinforces inventory, data provenance, threat prioritization, and incident-aware operations. This tool borrows that operating logic at a lighter diagnostic layer rather than pretending a quick screen can replace formal governance work.

    The result is intentionally blunt. If the agent can take irreversible actions, has broad write access, touches sensitive data, or runs without human approval and usable logs, the right answer is not "we will monitor it closely." The right answer is to stop, redesign, and document the control model before rollout.

    Read the supporting guide pages

    This gate is meant to sit on top of your existing guide architecture. Use the pages below to explain the control rationale behind the score and connect to the full implementation toolkit.

    Agentic AI Governance

    The broad operating model for bounded autonomy, ownership, escalation, and board-level oversight.

    OWASP Agentic Top 10

    The threat surface that explains why weak agent design becomes a production security problem quickly.

    UC Berkeley Risk Profile

    The cleanest frame for bounded autonomy and defense-in-depth containment.

    Singapore IMDA Framework

    The accountability and lifecycle logic for governing agents beyond experimentation.

    Next artifact for agentic deployment governance

    If the deployment gate flags autonomy, tool-use, override, or escalation risk, use the incident log and shutdown playbook to define evidence and response structure before rollout.

    View ACTOpen operating model preview

    Frequently Asked Questions (FAQs)

    What does this tool assess?

    It screens the minimum governance conditions for deploying an AI agent beyond sandbox. It checks action scope, tool access, data sensitivity, human override, kill switch, logging, delegation, testing, ownership, and business impact.

    Does a low score mean the agent is safe?

    No. A lower score means obvious governance blockers are less severe. It does not provide safety assurance, compliance assurance, or production-fitness approval. It is a triage layer, not a formal approval process.

    Why is human override weighted so heavily?

    Because agentic systems can take actions with real operational consequences. If high-impact actions do not require mandatory human approval, the governance model is structurally weak even if other controls look mature.

    Why does logging matter so much?

    Without prompt, tool, action, and outcome logs, you cannot investigate incidents, prove what happened, or demonstrate operational control to internal stakeholders, customers, or auditors.

    Will this tool store my answers?

    Scoring runs in your browser. This assessment does not save answers between page loads. Page fonts load from Google Fonts.

    Source review dates are stated in the relevant guides. It provides operational implementation guidance and product information only; it is not legal advice, tax advice, audit assurance, certification assurance, conformity-assessment advice, buyer-approval assurance, or security assurance. Validate legal, regulatory, contractual, tax, audit, and security decisions with qualified professionals.