Count the agents your teams are running. Now count the rows in your agent inventory.
The gap between those two numbers is the shadow agent problem. In most organizations, platform teams can estimate model spend, but they cannot produce a reliable inventory of which agents exist, who owns them, and what they attempted to do when governance blocked a request.
That last part matters more than many teams expect.
Blocking a request is useful. Keeping a gateway-verified record of what was blocked, why it was blocked, and which identity initiated it is what turns governance into an operational control instead of a best-effort policy.
This is where Threat Intercepts become valuable.
Kimss AI is a Secure Enterprise Agent Control Plane — a model-agnostic API gateway. Customers bring their own agents and infrastructure. Kimss provides registry, SSO identity mapping, gateway kill switch controls, and gateway-verified audit for routed traffic. It does not host models or resell compute.
Threat Intercepts are the forensic layer around denied or governed traffic. They are intentionally a paid surface because blocked-traffic telemetry has operational and compliance value beyond simple usage logs.
Why blocked traffic matters more than successful traffic
Most AI observability discussions focus on successful requests:
- Which model was called
- Token usage
- Cost attribution
- Latency
- Agent inventory
Those are useful for operations and FinOps. But security and platform teams usually get pulled into incidents because of denied behavior, not normal behavior.
Examples:
- An internal agent repeatedly exceeds token guardrails against a sensitive endpoint
- A deprecated automation keeps attempting to access a revoked provider key
- An employee tool continues generating requests after access was disabled at the gateway
- A self-built MCP integration attempts routes outside approved policy
- A rogue script rotates models after one provider is blocked
Without intercept telemetry, the platform only knows the request failed. It loses the surrounding operational context.
With gateway-level intercept records, teams can answer:
- Which identity initiated the request?
- Which registered or discovered agent attempted it?
- Which route or provider policy blocked it?
- Was the traffic recurring?
- Did the behavior continue after a kill switch action?
- Was the event isolated or part of a broader rollout?
Those answers are why blocked-traffic logs become a paid forensic capability instead of just another dashboard widget.
The difference between usage logs and gateway-verified intercepts
A common mistake in enterprise AI governance is assuming application-side logs are sufficient.
They usually are not.
Application logs are self-reported. They can be incomplete, disabled, or bypassed entirely. They also rarely provide a unified view across multiple agent frameworks, providers, and teams.
Kimss works differently because governance happens at the API gateway layer.
If traffic routes through https://api.kimss.ai, the gateway can:
- Apply policy controls
- Enforce kill switches at the gateway
- Attach SSO-mapped identity context
- Produce gateway-verified audit records for routed traffic
That distinction matters during investigations.
A self-reported application log can say:
“The agent stopped sending requests.”
A gateway log can show:
“The gateway denied 417 requests after the kill switch was activated.”
Those are operationally different statements.
Kimss scopes this honestly: only traffic routed through the gateway is gateway-verified. It does not scan networks, DNS, endpoints, or SaaS estates for AI activity.
Why teams discover shadow agents during intercept analysis
Zero-annotation discovery changes how teams find unmanaged agents.
Routing existing traffic through the gateway can be as simple as a base_url change. When unattributed traffic appears, Kimss creates inventory rows labeled by model, such as:
Discovered · gpt-4o
If teams send X-Kimss-Agent-Id, the inventory becomes explicitly named.
In practice, many organizations first notice unmanaged tooling because blocked requests begin appearing in intercept records.
Examples include:
- Weekend prototypes still running in CI
- Internal copilots using personal API keys
- Old automation jobs pinned to retired providers
- Contractor-built agents without ownership metadata
Threat Intercepts often become the first reliable evidence that these systems exist.
A minimal OpenAI-compatible integration
Kimss exposes an OpenAI-compatible inbound endpoint at:
https://api.kimss.ai/v1
For many teams, onboarding starts with a base URL swap.
Example using the OpenAI Python client:
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["KIMSS_API_KEY"],
base_url="https://api.kimss.ai/v1"
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "user",
"content": "Summarize today's deployment risks."
}
],
extra_headers={
"X-Kimss-Agent-Id": "deploy-risk-agent"
}
)
print(response.choices[0].message.content)
Environment configuration:
export OPENAI_BASE_URL=https://api.kimss.ai/v1
export KIMSS_API_KEY=your_key_here
That routing step enables:
- Agent inventory creation
- SSO identity mapping
- Gateway governance
- Kill switch enforcement at the gateway
- Gateway-verified audit for routed traffic
No model migration is required because customers continue using their own providers and infrastructure.
Why intercept telemetry becomes a budget conversation
Security telemetry is expensive to retain and investigate. AI governance introduces another challenge: the signal-to-noise ratio is still immature in most organizations.
Teams do not want every successful inference surfaced as a high-priority event.
Blocked traffic is different because it represents:
- Policy friction
- Misconfiguration
- Unauthorized automation
- Drift from approved workflows
- Attempts after revocation or restriction
That makes intercept telemetry materially more useful for investigations.
It also explains why Kimss separates free onboarding from paid governance surfaces.
The Developer tier exists so teams can route real traffic quickly:
- 25,000 governed requests/month
- No credit card
- No trial
- 14-day retention
- Hard HTTP 429 at cap
That is enough for engineering teams to validate inventory, routing, and governance patterns before expanding into broader operational controls.
Paid tiers add governance capabilities intended for production operations, including Threat Intercepts and Team & Access collaboration features.
The operational value of denied-request history
A blocked request in isolation is not especially interesting.
A timeline of blocked requests attached to identities, agents, and routes becomes operationally useful.
Platform teams can correlate:
- Which deployments introduced policy violations
- Which identities repeatedly triggered governance rules
- Whether kill-switch actions actually stopped traffic
- Which discovered agents never transitioned into managed inventory
- Which endpoints consistently hit token guardrails
This is especially relevant in enterprises where multiple frameworks coexist:
- LangGraph
- MCP-based tooling
- Internal SDK wrappers
- OpenAI-compatible clients
- Azure-hosted orchestrators
Without a gateway layer, each stack produces fragmented evidence.
With a centralized control plane, the organization gains a consistent governance boundary.
Governance only works when the sanctioned path is faster
One reason shadow AI spreads quickly is that centralized approval processes are often slower than experimentation.
Developers optimize for shipping.
If governance requires SDK rewrites, annotation projects, or provider migrations, most organizations will never achieve complete coverage.
The practical advantage of OpenAI-compatible routing is that teams can onboard incrementally.
A single base_url change can:
- Create inventory visibility
- Attach identity context
- Enable policy enforcement
- Produce gateway-verified audit records
- Surface intercept telemetry
That lowers the operational cost of governance adoption.
The result is not “perfect visibility.” No serious platform team should promise that.
The result is governed visibility for traffic that actually routes through the gateway.
That is a much more defensible operational claim.
FAQ
What are Threat Intercepts in Kimss AI?
Threat Intercepts are governance and forensic records for blocked or denied gateway traffic. They help platform and security teams investigate which identities, agents, or routes triggered policy enforcement on traffic routed through the Kimss gateway.
Does Kimss AI host models or provide compute?
No. Kimss AI is a Secure Enterprise Agent Control Plane and model-agnostic API gateway. Customers bring their own providers and infrastructure, including Azure, OpenAI, Anthropic, and OpenAI-compatible backends.
How do I start using Kimss AI?
Most teams begin by routing existing OpenAI-compatible traffic through:
https://api.kimss.ai/v1
The Developer tier includes 25,000 governed requests per month with no credit card and no trial.
Get Free API Key at https://kimss.ai
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