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The problem

AI adoption has
outpaced visibility.

AI is already embedded across your org. Security teams can’t see it, can’t scope the risk, can’t answer the basic questions.

See the solution Why this matters
The scale

What executives see vs. what security teams find.

Most orgs dramatically underestimate adoption.

73%
of employees use AI at work without IT approval
Industry research
12+
shadow AI tools per organization, on average
Industry research
89%
of security teams lack visibility into AI usage patterns
Industry research
The questions

What security teams can’t answer today.

Without AI-specific visibility, fundamental questions stay open.

Discovery & inventory

  • Which AI tools are being used across the org?
  • Sanctioned or unsanctioned?
  • How many AI integrations exist in production?
  • Which departments adopt fastest?

Usage & behavior

  • What data is being sent to AI models?
  • How do users interact with AI day-to-day?
  • Which models handle regulated information?
  • Volume and frequency, per user?

Risk & anomalies

  • When does AI usage deviate from normal?
  • Which users show suspicious behavior?
  • Any signs of exfiltration via prompts?
  • What needs immediate investigation?

Compliance & audit

  • Can we demonstrate oversight for auditors?
  • Where are the policy violations?
  • How do we report on regulated environments?
  • Where are the governance gaps?
Why traditional tools fall short

Existing stacks were built for a different threat landscape.

CASB & DLP
See app usage, not AI interaction.
  • Track file uploads, miss conversational AI
  • Monitor SaaS, can’t distinguish AI usage
  • Detect movement, lack prompt context
SIEM & log analysis
Aggregate events, not AI behavior.
  • Correlate auth, miss usage intent
  • Flag volume, can’t assess AI risk
  • Manual queries to investigate AI events
Network security
Inspect traffic, not semantic content.
  • Block bad domains, allow legitimate AI
  • Monitor bandwidth, not prompts
  • Lose visibility into encrypted API calls
IAM & PAM
Control access, not usage.
  • Authenticate users, miss what they do
  • Least privilege, can’t scope AI permissions
  • Audit login, miss interaction history
EDR
Protect devices, not data in motion.
  • Detect malware, allow paste-to-ChatGPT
  • Process execution, miss browser AI
  • Block software, not web-based AI tools
Vulnerability mgmt
Scan infrastructure, not AI surface.
  • Software CVEs, miss model vulns
  • Patch exploits, not prompt injection
  • Lack AI-specific threat intel
The consequences

Operating reactively. Responding to incidents after the fact.

Data exfiltration risk

Employees share PII, code, financials, secrets with AI models. By the time data leaves, it’s too late.

Compliance violations

Can’t prove HIPAA, GDPR, SOC 2, or internal-policy controls — audit failures and penalties.

Insider threat enablement

Malicious insiders use AI to exfiltrate or reverse-engineer. Traditional programs lack the telemetry.

Shadow AI sprawl

Free trials, personal accounts, unvetted services — an expanding surface no one sees.

Incident response gaps

When incidents touch AI, investigation stalls — no forensic record of who did what or with what data.

Policy enforcement failure

AI AUPs without visibility are suggestions, not controls. Liability follows.

Security decisions need visibility.

Before policy. Before risk scoring. Before response.

See the solution Request a demo