Your team sets the strategy. The agents handle the rest. That's the whole idea.
Your team sets the strategy. The agents handle the rest. That's the whole idea.
Most DLP tools promised less work. What they delivered was a new job description. Someone has to build the classifiers, chase the alerts the tool generates, keep policies tuned, clear an exception queue that refills overnight and remediate whatever the last audit turned up. That someone is usually your best security engineer. This work is most of their week.
MIND AI DLP Agents are our answer, a team of autonomous agents built into the MIND platform that takes on the most time-consuming work in data security. The Custom Classifier and Issue Investigator agents are already on the job. Today the Policy Producer, Rapid Response and Reason Reviewer agents join them, and the team will keep growing. You direct them all in plain language from the AI clients your team already uses. They run on autopilot.
Why does running a DLP program still consume your team?
Because the tooling automated detection and left the program to humans. Detection was never the hard part. The hard part is deciding what counts as sensitive in your business, figuring out whether an alert matters, translating intent into policy syntax and reviewing the override requests that pile up faster than anyone can clear them.
Security teams have felt this for years. The result is a program where the tool generates work and the people absorb it. Analysts burn hours per incident on manual digging. Policy engineering becomes a specialized skill someone has to maintain. Exception reviews get rubber-stamped because nobody has time to read them, which quietly defeats the point of having policies at all.
It doesn't have to be this way. The work is real. Almost none of it requires a human.
What do MIND AI DLP Agents actually do?
Each agent owns a distinct part of the program and they work together as one team. These first five cover the jobs that consume the most hours.
The Custom Classifier Agent builds business-specific data classifiers at both the document and data level. It learns what sensitive means in your environment rather than applying someone else's template.
The Issue Investigator Agent analyzes incidents, uncovers patterns across your environment and explains the risk in plain language. It tells you what happened, what to do about it and can take that action itself.
The Policy Producer Agent creates and continuously refines policies from what it observes in your environment and from instructions you give it directly. No policy engineering, no rules to maintain by hand.
The Rapid Response Agent executes approved remediation actions immediately and escalates when human approval is required. Nothing sits in a queue waiting for someone to get back from a meeting.
The Reason Reviewer Agent evaluates policy override justifications against your organizational guidance in real time, so exceptions get a real review instead of a rubber stamp.
MIND isn't just automating DLP tasks. It's minding the whole program, so the judgment calls that genuinely need a human are the only ones that reach one.
How do you direct the agents in plain language?
Through MIND's MCP interface, from any MCP-compatible AI client your team already works in. Assign an investigation or draft a policy the same way you'd ask a colleague. Trigger a remediation without leaving the thread. There's no syntax to learn and no console to switch to. Every action runs through MIND in the background while your team stays in one place.
This matters more than it sounds. The biggest hidden cost of most security tooling is the context switch, the separate console and learned query language that exist apart from where the team actually thinks. When directing your DLP program feels like sending a message, it stops being another place your team has to go.
What results are security teams seeing?
MIND customers report an 80% reduction in DLP program effort, near-zero false positives and 25 to 50% less time per incident investigation. Numbers like these are the reason we built the agents.
Behind each number is a team that got its week back.
“The team is spending 80% less resources managing their DLP program than before.”
Yaron Blachman
CISO at OpenWeb
“MIND saved us 1-2 hours per incident and in some cases 3-4 hours, which translated to 25-50% time savings per incident per day.”
Mike Morrato
CISO & Global Head of IT at Noname Security
Al Faiella, Sr. Director of Security Engineering at ThoughtSpot, put it more simply. The value his team gets from MIND is peace of mind. We didn't plan the pun either.
What changes for your team on day one?
Deployment takes minutes and insights land the same day, so the shift is less a rollout than a handoff. The classifiers start learning your environment. Incidents arrive with the investigation already done, and policies refine themselves against what's actually happening in your data. Your team still decides what the strategy is, where the lines are and which remediations run without approval. The agents keep everything else.
And this is the starting roster, not the finished one. As the work of data security changes, new agents will join the team.
That's DLP at AI speed. It's the version of the job your team was promised in the first place.
How do you see MIND AI DLP Agents in your environment?
Book a demo and watch the agents work on your data, not a canned dataset. Deploy in minutes, get insights the same day and decide from there.
Let's mind what matters.
