The Question Security Teams Can't Answer

Most organizations discover Claude usage the hard way. A compliance audit surfaces unlogged activity. An incident report mentions customer data in a prompt. Then security scrambles to reconstruct what moved where, with no central record tying actions to individuals.

The gap isn't malicious intent. It's architectural. Claude activity lives outside traditional logging infrastructure. Your DLP tools don't intercept the exchange. Your identity platform doesn't track the session. When leadership asks who used it last quarter, the answer requires forensic work.

That delay creates exposure. Between initial use and eventual discovery, sensitive data moves through systems you didn't explicitly authorize.

Why This Matters Now

AI agents now handle workflows that used to require multiple human approvals. Claude can retrieve documents, summarize customer records, and connect to internal systems. That utility comes with risk concentration. One compromised prompt can expose more data than a compromised inbox.

The challenge isn't Claude itself. It's the lack of visibility into how employees use it. Security teams govern endpoints, applications, and identities with mature tooling. But agentic AI sits outside those frameworks. Activity happens in conversational exchanges that traditional security controls never see.

This creates two problems. First, you can't inventory what you don't observe. Second, you can't detect risky behavior in tools you don't monitor. When Claude connects to a database you didn't approve, or escalates permissions beyond its documented scope, that action may not surface until someone manually reviews logs weeks later.

The window between risky action and detection is where compliance failures happen. Organizations need the same centralized visibility for AI agents that they built for SaaS applications over the past decade.

Three Strategic Gaps Exposed

Activity Lives Outside Your Central Security Log

Claude usage doesn't flow through your SIEM or identity provider by default. Employees interact directly with the tool. Those exchanges leave no trace in the systems your security team monitors daily.

• Compliance audits require reconstructing activity from scattered sources
• Incident response teams lack a timeline of who did what
• Shadow AI proliferates because detection depends on manual discovery
• Governance policies can't enforce controls on invisible activity

Sensitive Data Moves Through Prompts Your DLP Tools Never See

Data loss prevention systems inspect email attachments, file transfers, and application traffic. They don't intercept conversational AI exchanges. When an employee pastes customer PII into a Claude prompt, that movement bypasses your existing controls.

• Customer records, financial data, and credentials move without triggering alerts
• Legal holds and eDiscovery processes miss conversational AI entirely
• Compliance frameworks require documenting data flows you currently can't track
• Training programs can't address behaviors you don't measure

Agents Escalate Privileges Without Explicit Approval

AI agents don't always stay within the boundaries you intended. Claude might connect to systems, request elevated permissions, or execute actions beyond its original scope. If those escalations aren't logged centrally, you discover them only after something breaks.

• Unapproved integrations create new attack surfaces
• Privilege creep happens faster with agentic workflows than with human access
• Lateral movement becomes easier when agents connect systems you didn't explicitly authorize
• Rollback becomes complicated when you don't have a clear record of what changed

The Strategic Shift Required

Governing AI agents requires the same discipline organizations applied to SaaS adoption. That means centralized inventory, real-time activity logging, and detection logic tuned for agentic behavior. You can't secure what you don't observe. You can't audit what you don't log.

The shift is from reactive discovery to proactive monitoring. Instead of finding out Claude connected to a database after the fact, security teams need to see that connection in real time, tied to the person who initiated it. Instead of reverse-engineering prompt history during an incident, teams need a searchable record of who asked what and when.

This isn't about blocking AI usage. It's about bringing it into the same governance framework you use for other business-critical tools. That means visibility before incidents, inventory before audits, and detection before escalation.

• Centralize AI activity logging across all tools employees use
• Tie every action back to an accountable individual
• Detect risky behaviors like prompt injection, PII exposure, and privilege escalation in real time
• Build an audit trail that satisfies compliance requirements without forensic reconstruction

How Agent Risk Manager Addresses This

KnowBe4's Agent Risk Manager integrates with the Claude Compliance API to bring Claude activity into a centralized security console. It provides the same visibility for Claude that organizations already have for collaboration platforms and endpoint tools.

• Activity Lives Outside Your Central Security Log: Agent Risk Manager surfaces Claude usage in a unified AI Activity Log. Every action ties back to the person behind it. Security teams see who used Claude, when, and what systems it touched without manual log aggregation.
• Sensitive Data Moves Through Prompts Your DLP Tools Never See: The platform detects when prompts contain PII, credentials, or other sensitive information. It flags those exchanges in real time so security teams can respond before data moves further.
• Agents Escalate Privileges Without Explicit Approval: Agent Risk Manager identifies when Claude connects to unapproved systems, requests elevated permissions, or exhibits behaviors outside documented scope. Those alerts surface in the same console teams already use for other AI governance workflows.

The product also consolidates governance across multiple AI tools. Organizations using other platforms see activity from those tools in the same interface, reducing the number of consoles security teams monitor daily.

Who This Is For

• Security leaders who need centralized visibility into AI agent usage
• Compliance managers preparing for audits that now include conversational AI
• Security operations teams detecting risky behavior before it becomes an incident
• Organizations using Claude alongside other AI tools and needing unified governance

Agent Risk Manager is currently available to select customers in early access. Visit https://knowbe4.optrics.com/ai-agent-risk-manager.html

FAQ

Does this require deploying new infrastructure?
Agent Risk Manager integrates via the Claude Compliance API. It connects to existing Claude deployments without requiring agent installation or network reconfiguration. The integration pulls activity into KnowBe4's console for centralized monitoring.

Can it detect prompt injection attempts?
Yes. The platform includes detection logic for prompt injection, PII exposure, privilege escalation, and unapproved access. Security teams configure thresholds and receive alerts when risky behaviors occur.

Does it work with AI tools other than Claude?
Agent Risk Manager governs multiple AI platforms through a single console. Organizations using tools beyond Claude can monitor those platforms in the same interface, reducing the number of dashboards security teams check daily.

How does it tie activity back to individuals?
The platform maps AI actions to the people who initiated them through User Activity views. This creates accountability and supports incident response workflows that require identifying who performed specific actions and when.