CISOs and platform teams deploying agents that can change production systems.
Whether Endram provides the runtime control and evidence needed for ai security platform.
The control gap
Agent credentials are often broader than the task at hand, while ordinary monitoring sees the damage only after a tool has executed.
Define the platform boundary before comparing vendors. Model and prompt defenses address untrusted instructions and unsafe content; data controls protect retrieval and disclosure; identity proves principals; secret systems protect credentials; runtime authorization decides whether a proposed external action may execute. A credible platform should state which layer it enforces and which controls remain with the customer instead of presenting one dashboard as complete AI security.
Use a production-action proof: a verified agent proposes a high-impact call outside its delegated resource, requests approval for a permitted exception, changes one argument after review, and retries during a policy outage. Inspect the decisions, latency, replay behavior, and evidence chain. Endram focuses on the tool-action boundary and works with native IAM, server validation, sandboxing, and model defenses rather than replacing them.
What good looks like
Each call is evaluated before execution, with narrow automatic access, explicit denials, and human approval for consequential actions.
- Runtime allow, deny, and approval decisions
- Delegated-user and agent identity separation
- Resource and environment-aware policy
- Immutable decision receipts
A production workflow
- Inventory agent tools and action classes
- Observe real calls in shadow mode
- Promote high-confidence rules to enforcement
- Review decision trends and tighten scopes
Evidence to require
- Agent-to-tool capability map
- Allowed, denied, and expired requests
- Approver identity and rationale
- Policy version and execution result
Buyer checklist
- Can the product enforce a decision before the external tool executes?
- Can policy distinguish the agent, delegated user, tool, resource, and environment?
- Can reviewers see the exact requested action and approve it without broadening future access?
- Does every allow, deny, and approval retain the policy version and reason?
Practical answers
Common implementation questions
What does Endram control for ai security platform?
Endram evaluates the concrete tool call at runtime. It can allow, deny, or pause the call for approval using agent identity, delegated authority, action, resource, environment, and request context.
Does Endram replace the tool's own IAM?
No. Keep native IAM and OAuth scopes as the outer boundary. Endram adds a decision layer for the actions an agent attempts inside those credentials.
Can teams evaluate policies before enforcing them?
Yes. Shadow mode records the decision Endram would make without interrupting the call, so teams can measure impact before switching a policy to enforcement.