The new surface,
visible.
Shadow AI usage, rogue agent behavior, and AI-enabled exfiltration. The new attack surface, made visible by a platform built to watch it.
Shadow AI usage, rogue agent behavior, and AI-enabled exfiltration. The new attack surface, made visible by a platform built to watch it.
Forty-seven employees touched an unapproved AI tool last week. Twenty-one are using personal accounts on managed devices. Eight tools you've never heard of are flagging signals this morning.
AI tools arrived faster than any security program was built for. But listing what's installed is the easy part. The hard question is the one every breach starts with: is this normal for this person, on this team, right now?
Claude, Cursor, Copilot, Cline, and a dozen peers arrive through self-service installs. Each carries its own permissions. None of them check in with security.
Every agent is a host for plugins, skills, hooks, CLIs, and IDE extensions, each adding reach the agent never shipped with.
Pulled from public registries and handed filesystem, shell, and network access, usually before anyone asks what they can reach.
API keys, database URLs, and private keys sit in config files that any loaded agent can read and any prompt can leak.
A single Bash(*) or Write(*) rule hands an autonomous process the whole machine, with no human in the loop.
When data starts moving, the missing piece is the person and the pattern: whose machine, what they normally do, and whether this fits.
Employees are feeding AI systems with organizational data outside managed channels: source code into ChatGPT, financials into Claude, customer records into AI extensions. Anzenna captures these AI data flows and ties them back to employee identity, behavioral history, and surrounding context through automatic investigations. Security teams can see exactly what is entering AI, and when it crosses the line.
AI agents request broad scopes that give them read and write access far beyond what most humans ever hold. Anzenna flags risky scopes, MCP server installs, and agent activity that begins to move beyond its intended role. What looks harmless at first can widen quietly. Anzenna makes that drift visible.
AI agents can be granted write access to production systems: committing code, modifying databases, sending emails, and triggering workflows. Misconfigured or compromised AI is not only a data risk. It is an operational risk. Anzenna tracks the configurations and behaviors that can turn agent access into business disruption.
Anzenna reads every AI artifact across your fleet through the EDR, MDM, identity, and developer-tool integrations you already run. Then it does the part that matters: it connects each one to an employee, a device, and a pattern of behavior.
The assistants people actually run, from Claude to Cursor to Copilot, each mapped to the employee and device behind the install.
Every connector graded official, community, or unknown, with the filesystem, shell, and network reach it was handed.
AI extensions across VS Code, Cursor, JetBrains, and the browser, weighed against your allowlist.
Custom skill packs loaded into an agent, and the standing instructions they inject into every session.
Third-party add-ons that extend what an agent can do, including the ones that arrived without a ticket.
API keys, database URLs, and private keys exposed in config, flagged wherever an agent or plugin could read them.
Pre-prompt, post-tool, and event hooks, with the command each one fires and the moment it runs.
Command-line tools and wrappers that drive agents outside the IDE, where most monitoring never looks.
The catalog grows with the ecosystem. As new AI artifact types appear, Anzenna learns to see them.
Discovery is table stakes. What changes the outcome is what Anzenna does next, the same worldview behind every Anzenna investigation, pointed at AI.
Every agent, connector, and credential pulled into one living graph of people, devices, and apps. Weak signals that mean nothing alone, read together.
Behavior judged against peer-group baselines, not static rules. An engineer wiring an MCP to their own repo is normal. The same agent reaching across the org is not. Anzenna knows the difference.
When something crosses the line, an Investigation Agent writes the case: evidence, narrative, and a proposed response, routed to where your team already works. Human in the loop, audited end to end.
An MCP server can be approved on Monday and behave like an exfiltration tool on Thursday. Anzenna learns what normal looks like for each agent and the person running it, then weighs live behavior against that baseline.
Anzenna learns each agent's normal tool-call volume, targets, and data movement over a rolling window, per device, per user, and against the employee's peer group.
Sudden spikes, new high-risk targets, and bursts of data movement are scored against that baseline and matched to known abuse patterns.
A confirmed signal opens a case the Investigation Agent has already written: the evidence, the identity behind it, and a recommended response, routed to SIEM, Slack, email, and Jira. Read-only by default.
Anzenna doesn't add another daemon to your endpoints. It reads the AI surface through the EDR, MDM, identity, and developer tools already deployed: read-only, metadata only, live in fifteen minutes.
Anzenna doesn't replace what you run. It reads those tools and connects the fragments none of them can see on their own.
We had no insights into our AI usage and Anzenna was able to provide us with a comprehensive visibility layer.
Fifteen-minute install. Read-only by default. No agents on endpoints.
Thirty minutes. Your environment, not our slides.
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