Data Loss Prevention (DLP) is the set of tools, techniques, and people that keep sensitive data from being leaked, stolen, or lost. It covers data on-premises and in the cloud, at rest and in transit, and it works by comparing what is happening in your business against the rules you set.

A DLP strategy tells you where sensitive data lives, who can reach it, and how large your exposure is. That was the job for years, and it is still worth doing. The question has grown since: which AI tools your people use, which agents reach your data, and what those agents do once they get there.

Businesses run sensitive data through more places than one team can watch, and the reasons to control it stack up:

1. Customer records and your people's personal data are among the most valuable things you hold, and among the easiest to lose. One over-shared link is usually all it takes. Protecting them keeps the people who trust you out of harm's way, and keeps you out of an incident report.
2. Compliance with regulations such as GDPR, HIPAA, and CCPA is hard when you cannot say where your data lives. Data discovery, sharing limits, and retention periods give you an answer you can evidence rather than assert, which is what an auditor or an enterprise buyer asks for.
3. Trade secrets leak the same way personal data does, through a shared link or a pasted document. Source code, pricing models, and unannounced plans are exactly the material that ends up in the wrong window, and a competitor gets your thinking for free.
4. Breaches cost money, time, and reputation. Holding less data and finding exposure early shrinks the surface an attacker can reach, the fine that follows, and the number of customers who ask you to explain yourself.

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Data loss prevention is usually split by where the data sits, and most security teams end up running more than one type:

1. Cloud DLP: Protects data held in cloud applications such as Microsoft 365, Google Workspace, and AWS. Your security team sees who can reach data in cloud environments, and can encrypt it for another layer of protection.
2. SaaS DLP: A subset of cloud DLP, SaaS DLP covers data held in apps such as Slack, Google Drive, Microsoft Teams, and more. It detects sensitive data and shows you where it sits across files, assets, and chats, including the shared links and channels nobody has looked at in two years.
3. Network DLP: Controls data as it moves across the network, including email and cloud traffic. It blocks unauthorized uploads and shows your security team where sensitive data lives and who can reach it.
4. Endpoint DLP: Installed on laptops and phones, endpoint DLP watches where data is stored and whether it has moved. It classifies sensitive data so the right protection policies apply to the right files, and it is the type most organizations push back on, because it means another agent on the device.

DLP works by comparing the rules you set against what is happening across your organization.

Say you have decided that credit card numbers do not belong in cloud storage. The software scans the content held in your cloud apps, finds the card numbers, and weighs the context they sit in against that rule before it does anything about them. Context is the part that matters, because the same string of digits is fine in one place and a problem in another.

Metomic shipped classifiers for email addresses, phone numbers, social security numbers, and more, plus custom classifiers for the data types specific to your business. That classification engine still does the work. It now runs on prompts and agent requests as well as files.

Once a rule is broken, what happens next is yours to set: alert your security team, coach the person involved, redact the sensitive part, block the action, or hold it while someone approves or denies. Metomic scans content in flight and does not store the source content.

Metomic also goes to the person, or to the agent's owner, in Slack or Teams, so the fix happens where the work happens rather than in a ticket queue nobody reads. Most exposure gets cleaned up by the person who created it, within minutes, without your team touching it.

All of it leaves a record. Your security team gets the reports and audit logs to investigate an incident, keep compliance records, and hand something concrete to an auditor.

Data ends up in the wrong place in most companies. Most hold more of it than any one person or team can track, so exposure builds quietly until something surfaces it, and by then the data has usually been somewhere it should not have been for months. Manual review does not scale to that, and neither does a written policy people agree to once at onboarding.

DLP software is worth the investment if you want to:

• Protect sensitive data and cut your risk of a breach or a leak
• Evidence compliance with regulations like GDPR, HIPAA, and CCPA
• Keep intellectual property and trade secrets inside the business
• Win customer trust by showing how you handle their data
• Shrink your attack surface by holding less data in the first place
• Enforce your security policy instead of publishing it and hoping
• Spot insider threats and unusual behavior before they become incidents
• Teach your team good habits without running a training program

Metomic built its business classifying and protecting sensitive data in SaaS apps: Slack, Google Drive, Jira, Microsoft 365, and more. That held while the risk was a person sharing a file with the wrong audience.

Then the risk moved. Your people paste customer records into chat tools on personal accounts, and your agents inherit the permissions their owner holds. Metomic is the AI data security layer for everything your people and their agents do with sensitive data. It sits in the path of your agents, and in the browser where your people use AI directly.

Book a personalized demo and we will show you what is already happening in your environment.

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