Know when your IP leaves, and whether it was meant to.
Data Exfiltration Detection & IP Protection
Know when source code, designs, customer records, or models leave, and whether they were meant to.
DLP chases files. Attackers take data.
The object moves. The story leaves with it.
MCP creates new paths for covert exfiltration. Data also leaves through AI tools, browser uploads, and git clones to personal machines. DLP can follow a file. It struggles to follow the full exit: who moved it, where it went, what channel carried it, and whether the action belonged. The signal is there. The intent is not.
Content inspection can't read a blueprint.
Legacy DLP scans for patterns: card numbers, social security numbers, keywords. But your most valuable data rarely carries a pattern to match. To a content scanner, source code, model weights, and a board deck all look like ordinary text.
Source code
No card number, no keyword. A scanner sees plain text and misses that this is your core repository.
Product & design
Binary CAD and design formats carry no scannable pattern at all, and often no text at all.
Models & data
Weights and embeddings are just numbers. Their value is invisible to a content rule.
Board & strategy
Forecasts and strategy decks read as ordinary documents to a pattern matcher.
Recorded meetings
Audio and video hold no text to inspect, yet often carry the most sensitive context.
Customer & HR
Even structured records slip through once they are renamed, zipped, or pasted in pieces.
Identified by its past, not just its pattern.
Anzenna judges data by its provenance. It reconstructs the full journey of a sensitive dataset across your systems and ties every hop to a person. Importance comes from where data has been and who touched it, not from whether a regular expression happened to match.
Origin
Where it came from and how it is classified. A row from CUSTOMER_FACTS, a clone of the core repo, an export from Salesforce.
Path
Every system it crossed on the way out. Warehouse to laptop to browser to a personal account, reconstructed end to end.
Hands
Who moved it, measured against their peers. An export that is routine for data engineering is a flag for a departing sales rep.
How we see it.
MCP and shadow AI
Anzenna watches MCP activity and browser AI flows as they happen. Every movement is tied back to an identity, giving security teams clear visibility into what is entering AI systems, through which path, and under whose hand.
The browser factor
The browser is often where the quiet exit begins. Anzenna captures file uploads and movements there, then connects them back to sensitive company data so the full exfiltration lineage comes into view.
IP and data exports
Anzenna correlates git operations, Salesforce exports, and other data movements with identity context, HR status, and peer behavior. What looks like a simple export on the surface becomes a complete investigation, with the context and reporting security teams need to find real threats.
Anzenna caught one of our engineers uploading sensitive source code into their personal github repo.
Your stack, unchanged.
Fifteen-minute install. Read-only by default. No agents on endpoints.
Data protection, at a glance.
- Behavioral data loss prevention that catches source code, records and models leaving, and tells deliberate exfiltration from normal work.
- Correlates identity, SaaS, cloud and endpoint to spot data exfiltration that classic pattern-based DLP misses, agentlessly.
- Revoke, quarantine or notify in one click, with a full audit trail.
Data exfiltration detection, answered.
How does Anzenna detect data exfiltration?
Anzenna correlates identity, SaaS, cloud and endpoint activity to spot sensitive content, source code, designs, customer records or model files, leaving the system of record, then reasons over the behavior against a 90-day baseline to separate deliberate exfiltration from normal work. It is agentless, so there is nothing to install.
Is Anzenna a data exfiltration detection solution or a DLP?
Both. It delivers behavioral data loss prevention that catches the context-driven flows pattern-matching DLP misses, and it works as a standalone data exfiltration detection solution or alongside an existing DLP.
Can it catch data leaving to personal accounts or AI tools?
Yes. Uploads to a personal Google Drive, pushes to a personal GitHub fork, and pastes into unsanctioned AI tools are all correlated with the identity behind them and flagged with full business context.
Does Anzenna replace our existing DLP?
Either. It can consolidate DLP and UEBA, or integrate with your current DLP to inherit its classifications while adding the behavioral context and AI coverage rule-based DLP lacks.
Does it require endpoint agents?
No. Anzenna is agentless and connects through APIs across your identity, SaaS, cloud and endpoint systems.
Data protection reads from the same baseline as triage, UEBA, and identity threat detection. See Anzenna for security operations →
Ready to see it on your data?
Thirty minutes. Your environment, not our slides.
Request a walkthrough ↗