Understand AI use
See how employees, assistants, and agentic tools are actually using AI.
Runtime AI governance before prompts, uploads, OAuth grants, and delegated tool actions.
See how AI is being used. Control it before it leaves.
3LS Platform gives teams company policy, control, and observability for users, assistants, and agentic workflows before sensitive data or delegated authority leaves through prompts, uploads, OAuth grants, or delegated tool actions.
- Observe
- AI use
- Detect
- Sensitive content
- Decide
- Controls
Policy decision receipt
Customer export summary
Interaction
User uploads a customer CSV to an external assistant for summarization.
Classification
Data handling
Sensitivity
PII and customer records
Control outcome
Warn the user, retain evidence, and require policy acknowledgement before data leaves.
Mechanism:
On-device agent intercepts AI activity before it leaves the endpoint.
Privacy:
Detections and policy decisions stay within your organization.
Detect sensitive content
Spot PII, secrets, and risky interactions before a prompt is sent, a file is uploaded, or an agent delegates work to a tool.
Apply controls
Choose when to allow, warn, or block and give teams evidence for every decision.
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 when controls should step in.
Phase 1
then
AI boundary decision appears
A user, assistant, OAuth app, or tool is about to send company context outside the organization.
Phase 2
then
Intent is understood
3LS classifies what the interaction is for and how the AI is being used.
Phase 3
then
Sensitivity is detected
Sensitive content and risky behavior are surfaced before the interaction becomes an incident.
Phase 4
Controls are applied
Teams can allow, warn, or block based on context, risk, and policy.
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. Controls answer what should happen next.
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
Controls
What should happen now?
Apply allow, warn, or block decisions that match the interaction, the sensitivity, and the business context.
Outcome: Consistent outcomes
Operator view
Turn AI interactions into clear decisions
Give security teams a readable trail of what was detected, what it meant, and what action was taken.
Recent findings
Intent, sensitivity, and control outcomes
3 decisions captured
Activity
Intent
Sensitivity
Action
Customer records pasted into an assistant
Data handling
PII detected
Warn
Prompt requests external tool access
Tool use
No sensitive data
Allow
Prompt includes an API token and a request to share it
Data sharing
Secret detected
Block
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 apply the right controls.
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 capabilityAI controls
Choose the right action for each interaction
Apply allow, warn, and block decisions based on context so teams can guide AI use without slowing everyone down.
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.