The Identity Context Layer
AI Agents Need
"Grant Sarah access." But which Sarah? IdentityRM gives AI agents the context to decide correctly—and the audit trail to prove it.
Patent Pending • MCP Native • SOC 2 Ready
We were building custom RBAC for every AI integration. IdentityRM gave us a single control plane. Our security team finally sleeps at night.
AI Agents Are Deploying. Governance Isn't.
of enterprises will deploy AI agents by 2026
Gartner predicts autonomous AI in every workflow.
Most have no governance plan.
average cost of an identity-related breach
AI amplifies risk. One misconfigured agent
can grant access to thousands.
is becoming the standard for AI tools
Anthropic's Model Context Protocol is how
AI agents will interact with enterprise systems.
Companies building AI governance now will lead. Everyone else will scramble to catch up.
Agents Can't See What They Can't Access
Persona boundaries make out-of-scope entities invisible—not just inaccessible.
No Over-Privileged Agents
Agent managing Engineering/Seattle can't accidentally touch Finance or London. They don't exist in its world.
Zero Enumeration Attacks
A compromised agent can't probe for what exists. "User not found" ≠ "Access denied." It's truly invisible.
Delegated Admin Without Risk
Regional managers get AI agents that manage their region. Not yours. Not corporate. Just their subtree.
Persona Switching
Same agent, different contexts. Switch from "Store A" to "Store B" mode—permissions adjust instantly.
Four Questions Every AI Agent Needs Answered
Before your AI can act, it needs context. We provide it.
"Which Sarah?"
Your agent says "Sarah." We know which one.
Natural language resolution with confidence scoring. "Sarah from Platform" → sarah.williams@acme.com (0.95)
"Who owns this?"
Queryable org graph, not spreadsheets.
Unlimited hierarchy depth. Corporate → Region → Franchise. Managers manage their subtree. No one else's.
"What access existed then?"
Time-travel queries. Point-in-time reconstruction.
Auditor asks: "Who had admin access March 15th at 3 PM?" You answer in seconds, not weeks.
"Why did the AI decide that?"
Every AI decision. Auditable. Queryable.
Not just what happened—why. Which factors. What checks. MCP tools for scoped audit queries.
Other Platforms Log Actions.
We Log Reasoning.
Agent ID & Model Version
Which AI made this decision, on which model
Decision Mode
Autonomous vs human-approved—compliance needs this
Validation Factors
user_exists, quota_ok, policy_allows, temporal_valid
Tool Chain
Exact sequence of MCP tools invoked before decision
Correlation ID
Trace from AI request → service → database → audit
// Find risky autonomous decisions in your scope
query_audit_events(
decision_mode: "autonomous",
missing_factors: ["quota_ok"],
since: "7d"
)
Result: 3 decisions flagged for review
AI granted access without checking quota—within your scope only
AI That Gets Smarter From Corrections
Every human correction becomes training data. Your IAM system improves itself.
AI Makes Decision
"Disable user john.doe"
Reason: Suspected inactive
Human Corrects
"Re-enable user john.doe"
Reason: User is active, AI wrong
Preference Pair
✗ Rejected: Disable without HR check
✓ Chosen: Check HR status first
Training Export
JSONL format
Fine-tune ready
Direct Preference Optimization (DPO)
State-of-the-art technique for RLHF without a separate reward model.
Every correction automatically generates training pairs you'd pay a labeling team millions to produce.
The Competitive Moat
Other platforms log corrections as separate events—no decision chain.
IdentityRM maintains: original action → correction → reason → actor type.
Your IAM system generates its own training data.
The Gap in Traditional IAM
IdPs authenticate. We contextualize. They don't know which Sarah.
The decision trace feature alone justified the investment. When the auditor asked 'why did the system grant this access?'—we had an answer in 30 seconds.
Ready to Govern Your AI Agents?
The identity context layer is the foundation. Everything else depends on it.
Patent Pending • 60+ MCP Tools • DPO Learning Loop • Multi-IdP