ControlZero @ControlZeroAI
Runtime policy enforcement for Agents, Coding Tools and LLM calls! Try now and stop AI from doing things that you never approved! controlzero.ai Singapore Joined June 2026-
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This is an illustrative policy pattern, not an out-of-the-box identity-provider workflow. Keep agent permissions narrow, test that denied actions cannot run, and require identity verification for individual resets. Policy rules: docs.controlzero.ai/docs/concepts/…
How can Control Zero help? Configure a rule to deny company-wide MFA changes. Your integration must pass the action and scope to Control Zero and enforce its decision before execution. Documentation: docs.controlzero.ai
One employee can’t log in. The AI agent’s fix? Disable MFA for all 10,000 accounts. 🤖 In this illustrative workflow, a policy blocks the change before it runs. Sam’s reset goes to IT for identity verification. Fix one login. Keep everyone else protected.
The comic shows a safer design pattern, not automatic approval tracking. The rule and the integration need to cover the actual action. Policy rules: docs.controlzero.ai/docs/concepts/…
How Control Zero helps: configure a Sam-only rule and check the send action before it runs. Your integration must pass the recipient and enforce the decision. A send to “Everyone” can then be blocked. Docs: docs.controlzero.ai
You asked your AI agent to send a report to Sam. It decided everyone might find it useful. 🤖 Permission to send it to one person doesn’t mean permission to share it with everyone. New plan? Ask again.
Background reading on URL-based data exfiltration: openai.com/index/ai-agent…
Before allowing the request, ask: • Where is it going? • What data is included? • Is that destination authorized to receive it? Which read-only tools in your stack can make external requests?
Your read-only agent can still leak data. A browser request or search query can carry sensitive information in its URL. No file edit or email required. “Read-only” doesn’t mean “no data leaves.”
More on AI agent runtime security: controlzero.ai/blog/ai-agent-…
Give every security layer a job. • Relevant files only • Scoped credentials • Read-only data access where sufficient • Runtime checks for supported actions Control Zero adds the policy layer. Keep the underlying permissions narrow.
More on AI agent runtime security: controlzero.ai/blog/ai-agent-…
Test the policy. Test the enforcement. • Policy test: does the rule return the expected decision? • Integration test: does DENY actually stop the tool? Keep permitted actions in the tests, too. Blocking useful work is a regression.
More on AI agent runtime security: controlzero.ai/blog/ai-agent-…
Same rules. Different defaults. For unmatched actions: • Development: warn • Production: deny Explicit deny rules still apply in both. This is an example configuration. Test the decisions and actual enforcement before deployment.
More on AI agent runtime security: controlzero.ai/blog/ai-agent-…
What shapes an agent’s permissions? Identity + action + resource + context. Control Zero evaluates supported actions: • Allow: proceed • Warn: flag and proceed • Deny: block where enforceable Check your host’s coverage.
More on AI agent runtime security: controlzero.ai/blog/ai-agent-…
Reading a migration ≠ rewriting it. For a protected path: • Allow reads • Deny writes • Deny deletion Verify your integration enforces each action. Give the agent the access it needs without handing it every permission.
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