Endpoint DLP
Endpoint DLP that actually prevents in real time
Bold runs AI locally on the device to classify data by meaning, analyze intent, and stop sensitive data loss in real time.
The endpoint is the new
control layer in the AI era
Sensitive work moved onto the device. Existing tools can't see that layer, and the ones that can't act fast enough to stop anything.
The data moved to the device
Employees create and move sensitive data in desktop AI apps, IDEs, and agents that never touch a network gateway at all.
Existing tools can't follow it
Network, cloud, and browser tools go blind the moment data reaches the device. It's the one layer they structurally can't reach.
Security is blind to the basics
Answering what data exists, who touched it, and where it went means correlating logs across tools with no context.
Why DLP still struggles with prevention
Security teams have relied on cloud and network-based tools that use pattern-matching. They’re too slow to prevent in real time or too noisy to actually prevent with accuracy.
Legacy DLP
Legacy DLP matches patterns at the network or gateway, which works for predictable formats but breaks down in the complexity of the AI era.
- Struggles to classify source code, financial models, or proprietary data, so the riskiest data stays invisible
- Generates so many false positives that teams turn blocking off and fall back to monitoring
- Each new data type needs a new rule, so protection scales with headcount instead of the tool
"Next-gen" AI-DLP
The new wave of DLPs use AI to improve data classification and reduce noise, but their AI runs in the cloud, leading to the same challenges.
- Every decision takes a round trip, so the verdict arrives after the action it was evaluating
- It documents the loss for an analyst to review instead of stopping it in the moment
- Desktop AI apps that encrypt their own traffic never reach the cloud inspection point at all
Any tool reasoning about risk in the cloud is deciding after the data is already moving. Only on-device AI can decide before it leaves.
Accurate, real-time prevention on the device
Bold runs AI locally that’s smart enough to understand real risk and fast enough to block it.
User and agent intent analysis
Bold reads the full context around an action, the data, the actor, and their intent, to tell real work from real risk instead of firing on a pattern match.
Every signal carries the data, the actor, and the intent behind it
Distinguishes human activity from AI-agent activity on the device
Real incidents surface immediately, and the false-positive flood is gone
Real-time blocking, even offline
The decision happens on the device, not in the cloud. Pull the network cable and Bold keeps classifying and blocking while other tools go dark.
No round trip, so the verdict lands before the action completes
Full protection offline, in air-gapped environments, and on desktop AI apps
Cloud and API-first tools go dark the moment the connection drops
Local AI data classification
Bold classifies by meaning, not pattern, to catch the complex data regex never could.
Classifies any data type, including unstructured and proprietary data
No policy library to manually write, tune, or maintain for every new data type
Semantically understands what the data is, no matter how new, specific, or unique
What real-time, on-device DLP changes
With Bold's data protection running on the endpoint, you get:
Risk is stopped, not filed
Bold stops risk before data leaves the device, instead of flagging it for an analyst to triage after.
Real work is never blocked
Bold tells real work from real risk instead of blocking on a pattern match, so legitimate work passes untouched.
No one waits on a verdict
Bold decides on the device instead of making a round trip to the cloud, so users are never left waiting.
Response fits the risk
Clear exfiltration gets blocked, borderline actions get coached or redirected, and everything else passes.
Investigations in minutes
Bold attaches data lineage and context from the start, so investigations drop from days of log correlation to minutes.
Protection scales without headcount
Policies never need tuning, and incidents are prevented outright, not just detected faster.
Where Bold fits in your DLP program
Bold is the endpoint layer your network and cloud tools can't reach. Keep the rest of your stack; replace what you've already turned off.
Bold complements
Your email gateway and compliance reporting keep doing their job. Bold adds the device layer: local files, clipboard, USB, print, and desktop AI.
Bold replaces
The noisy legacy DLP you've turned blocking off on. Bold classifies by meaning and enforces in real time, so you can actually prevent.
One platform, three use cases
The same on-device classification that powers DLP carries two more use cases from one deployment, drawing on one shared context.
Endpoint DLP
Semantic classification and real-time enforcement for sensitive data living and moving on the device.
AI Usage Control
See and govern every AI interaction on the device, including shadow AI and desktop apps cloud tools miss.
Endpoint Insider Risk
Behavioral intelligence plus data context, so every investigation carries the full picture from the start.
