AI agents are smart. Maybe too smart.
They can query databases, pull documents, and summarize sensitive information in seconds. But that’s exactly the problem: if your agent isn’t aware of trust and context, it can access the wrong data without anyone noticing.
At IndyKite, we’ve seen this happen in real-world RAG pipelines. The agent doesn’t make a mistake-it tries to accomplish its goal blindly because it doesn’t know what it’s allowed to touch.
The risk: Autonomous agents and data
AI agents operate across multiple systems:
- Vector databases
- APIs
- Internal knowledge graphs
Without proper checks, they can:
- Read sensitive customer data meant only for support agents
- Pull confidential internal docs into prompts
- Expose regulatory or PII information
It’s not a bug. It’s a missing trust layer.
Preventing wrong access with context
Instead of letting the agent retrieve anything it can see, we enforce Knowledge-Based Access Control (KBAC) at runtime, through our MCP server for example:
Here, indykite.mcp. vector_db.search() triggers Indykite’s KBAC engine automatically, and performs an authorized semantic search on the graph data. In particular, this search automatically checks::
- Subject identity: Who is making the request?
- Purpose/context: Why are they asking?
- Trust score: Is this resource safe for them?
- Filter out from the result set all resources the Actor doesn’t actually have access to.
This ensures the AI agent can never accidentally access or expose data it shouldn’t.
Another example, using an external Vector database:
# Agent tries to retrieve data
agent_id = "support-bot:42"
requested_data =vector_db.search("Show tickets for user123")
# Context-aware filter
trusted_results = [
requested_data.forEach {
# Use AuthZEN to evaluate retrieved data, either one by one or through batching
using AuthZEN /evaluations endpoint
if indykite.authzen.evaluate(subject=agent_id, resource=r, action = read, context="support")
trusted_results.add(requested_data($current)
}
]
# Only safe, permitted data reaches the agent
Why this matters for developers
Every AI system you build has autonomous actors. Treating them like “just another user” is dangerous.
By baking in trust at the data layer, you:
- Prevent accidental data leaks
- Maintain compliance with regulations
- Make AI systems explainable and auditable, because you can rely on deterministic guardrails.
Join the builders who care about AI agent trust
Your AI agents can do incredible things but only if they respect boundaries.
At IndyKite, we give developers tools like Knowledge Based Access Control to enforce those boundaries at runtime, so trust is always computed, not assumed.
👉 Explore IndyKite’s sandbox
👉 Try KBAC and AuthZen examples
👉 Share your agent boundary stories
Smart AI without trust is just a liability.
