From Shadow Runtime AI to Complete Visibility, Detection and Response.
AI workloads are dynamic, abstracted, and deeply embedded into modern applications, making them hard to track with traditional security tools. This creates a growing “shadow AI” problem where models, agents, and tools operate without oversight. Our AI workload discovery solves this by continuously analyzing cloud-native logs and runtime signals to identify all AI components in your environment—including hosted services, models, tools, and MCP servers. Each component is correlated to the workload executing it, the identity behind it, and its behavior over time. By baselining this activity, security teams can quickly spot anomalies such as new models, unauthorized integrations, or unusual usage patterns. More importantly, this visibility feeds directly into detection and response, enabling full attack storylines that include the AI layer.
AI is already running across your cloud, often without visibility. We added AI workload discovery that uses cloud logs and runtime telemetry to automatically detect models, services, tools, and MCPs, map them to real workloads and identities, and baseline their behavior. The result: full, real-time visibility and context so you can detect and respond to risky AI usage instantly.
AI adoption didn’t happen gradually. It exploded.
One day you had a controlled environment. The next, your cloud is running foundation models, hosted APIs, agents, copilots, MCP servers, and who knows what else, often without security even being aware.
This isn’t just “shadow IT” anymore. It’s shadow AI, and it operates at machine speed.
The Problem: You Can’t Secure What You Can’t See
AI workloads don’t behave like traditional infrastructure:
They’re ephemeral (spin up and disappear quickly)
They’re abstracted (hidden behind APIs and managed services)
They’re often triggered indirectly (via apps, users, or other services)
Traditional security tools weren’t built for this.
They might show you:
A container running
An API call executed
A role assumed
But they won’t tell you:
That this workload is calling an LLM
Which model is being used
What tools or MCPs are connected
Which data is accessed.
Whether this behavior is expected or risky
So AI spreads… silently.
The Shift: AI Workload Discovery
To secure AI, you first need to discover it, completely and in real time.
We’ve added support for AI workload discovery that does exactly that.
Using:
Cloud-native logs (Network & DNS logs, Audit logs, SaaS logs)
Runtime telemetry (API level payloads)
We detect and map:
Hosted AI services (e.g., managed LLM platforms)
Models being invoked
Tools and external integrations
MCP servers and agent frameworks
The actual workloads executing them
Discovery alone isn’t enough. You need context.
Every detected AI component is automatically:
→ Mapped to the executing workload
Which service, container, function, or identity is using it?
What is the blast radius if AI escapes to workload level permissions ?
→ Correlated with identity and access
Who initiated it? What permissions are involved?
→ Baseline modeled
Is this normal behavior, or something new?
→ Tracked over time
How is usage evolving? What changed?
This creates a living inventory of your AI surface area—not a static list.
→ Detect Risky or Unexpected Usage
Because everything is baselined, you can instantly spot:
New models being introduced
Unapproved tools or MCP connections
Privileged identities invoking AI services
Workloads behaving outside their norm
Why This Matters Now
AI is becoming part of every workload:
Backend services calling LLMs
Agents making decisions and taking actions
Tools extending model capabilities
Developers embedding AI into pipelines
This creates a new attack surface:
Prompt injection → tool abuse
Model misuse → data exposure
Identity abuse → privileged AI actions
Without visibility, you’re blind to all of it.
AboutStreamSecurity
Stream Security is an AI Detection & Response (AI DR) company built for the era of AI-driven environments across cloud, on-prem, and SaaS. As AI agents operate with real permissions and attackers move at machine speed, Stream enables security teams to keep pace by continuously computing a real-time, deterministic model of their entire environment. Powered by its CloudTwin® technology, Stream instantly understands the full impact of every action across identities, permissions, networks, and resources, allowing organizations to detect, prioritize, and safely respond to threats before they propagate. This transforms security from reactive detection into a true control plane for modern infrastructure.
