Understand AI use
See how employees, assistants, and agentic tools are actually using AI.
Runtime AI Governance for discovery, classification, policy evaluation, and evidence.
See how AI is being used. Govern what AI can reach.
Your organisation owns the controls over reachable tools, credentials, shared state, and delegated authority. 3LS brings AI use into view for users, assistants, and agentic workflows through discovery and policy evaluation.
- Observe
- AI use
- Detect
- Sensitive content
- Evaluate
- Policy
Visibility:
On-device collection brings supported AI activity into view for classification and policy evaluation.
Policy ownership:
Your organisation owns the policy evaluation and the resulting evidence.
Detect sensitive content
Find PII, secrets, and risky interactions in the observed evidence available to policy evaluation.
Evaluate policy
Turn observed evidence into an explainable policy evaluation and retain the result.
How it works
Move from vague AI risk to visible, understandable decisions.
Instead of treating AI as a black box, 3LS helps teams understand how AI is being used, where sensitive data is involved, and how organisational policy applies.
Phase 1
then
AI activity is observed
Available evidence shows where a user, assistant, OAuth app, or tool is using AI.
Phase 2
then
Purpose is classified
3LS classifies what the interaction appears to be for and how the AI is being used.
Phase 3
then
Risk evidence is surfaced
Sensitive content and risky behaviour are surfaced for evaluation.
Phase 4
Policy is evaluated
Teams evaluate the available context and risk against organisational policy, then retain the result.
What the story answers
Three questions every security team needs answered
Prompt classification answers how your users are using AI. Sensitive-content detection answers what information is present. Policy evaluation answers what the available evidence means.
Prompt classification
How are your users using AI?
Classify drafting, coding, research, data handling, and tool-driven behavior into clear operating patterns.
Outcome: Visibility into AI usage
PII detection
What sensitive data is present?
Highlight personal information, secrets, and restricted content inside prompts, tool inputs, and outputs.
Outcome: Fewer accidental exposures
Policy evaluation
What does policy say?
Evaluate the available context against organisational policy and keep the reasoning operators need to review.
Outcome: Explainable policy evaluation
Operator view
Turn AI interactions into clear decisions
Give security teams a readable trail of what was detected, how policy was evaluated, and which evidence supported the result.
Recent findings
Intent, sensitivity, and policy evaluation
3 evaluations captured
Activity
Intent
Sensitivity
Policy result
Customer records pasted into an assistant
Data handling
PII detected
Needs review
Prompt requests external tool access
Tool use
No sensitive data
Within policy
Prompt includes an API token and a request to share it
Data sharing
Secret detected
Escalate
Solutions
Go deeper on the capabilities behind the story.
Start with one visual story on the homepage, then explore the capabilities that help teams understand AI use, detect sensitive data, and make defensible policy decisions.
Prompt classification
Understand how your users are using AI
See whether AI is being used for drafting, coding, research, summarization, data handling, or tool-driven work.
Explore capabilityPII detection
Spot sensitive data before it spreads
Surface personal information, credentials, and restricted content inside prompts, tool inputs, and outputs.
Explore capabilityPolicy evaluation
Decide what the evidence means
Evaluate organisational policy with the context available. Pre-execution grant or deny is limited to a supported managed MCP stdio path.
Explore capabilityCompliance evidence
Prove AI governance before the review
Collect runtime evidence for prompts, uploads, OAuth grants, tool actions, supplier exposure, and high-risk AI decisions.
Explore capabilityFrom the blog
Start With the Research
A few articles that explain the operating model behind the product: where AI use becomes visible, why incidents expand, and how control decisions should be made.
Thought Leadership9 min read
AI Chat Security Risk and the Shadow Database Problem
AI chat feels private to users, but providers and operators control storage, sharing, indexing, and transcript exposure. That makes chat security a governance and data-exposure problem.
Read article →
Thought Leadership8 min read
The Enterprise AI Visibility Gap Behind Shadow AI Risk
After an AI exposure, the hardest question is usually what your organization cannot answer: where AI is active, what was pasted in, and what was shared. That visibility gap turns shadow AI into an incident multiplier.
Read article →
Thought Leadership9 min read
Workspace Features Create New Enterprise Data Paths
Microsoft is adding native AI controls while OpenAI is turning ChatGPT into a shared agent workspace. Both trends point to the same requirement: one runtime governance layer across prompts, files, memory, tools, and actions.
Read article →
Understand AI behavior before it becomes an incident.
Start with visibility, move to clear findings, and introduce controls only where they matter.