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Know where your sensitive data goes,
and stop it leaving.
A data loss prevention programme that starts with understanding your data and workflows, then builds endpoint, email and cloud policies that protect what matters without burying your team in false positives.
Most DLP fails from noise, not from attackers.
Personal information, financial records, health data, intellectual property: every organization has data that must not leave. It leaks through personal email, USB drives, cloud uploads, misconfigured sharing and, increasingly, AI tools. Most leaks are accidental. Some are not.
Many DLP deployments generate thousands of alerts, block legitimate work, and are quietly switched to audit-only. We start with discovery and classification, so policy is based on where sensitive data actually lives and how it moves. Policies run in monitor mode first and are tuned with the business before they block. Then we keep them tuned as your data and workflows change.
- Sensitive data discovery across file shares, cloud storage, email and key applications
- A classification scheme and labelling approach aligned to your regulatory obligations
- A data-flow map of how sensitive data moves between systems, people and partners
- Endpoint, email and cloud DLP policies, tuned in monitor mode before enforcement
- An incident triage and exception process with clear ownership
- Ongoing policy tuning and reporting, if you want us to run it with you
Five steps, discovery to steady state.
Discover
We scan where data lives and interview the business about how it is used, so we know what is sensitive, where it is, and who needs it.
Classify
A classification scheme is agreed and applied, with labels your staff and your DLP tools can both understand.
Design policy
Policies are written per channel: endpoint (removable media, uploads, print), email and cloud storage and collaboration, mapped to your regulatory requirements.
Monitor, then enforce
Policies log before they block. We tune out false positives with the business, then move the highest-risk rules to enforcement.
Operate
Incident review, exception management and regular tuning as new data types and workflows appear.
Two gaps a DLP tool cannot close alone.
Generative AI
Network DLP cannot see inside a prompt. HLÍN catches personal, financial and health data, secrets and confidential records as staff paste, type or upload into AI tools, and tokenizes or redacts them before the AI provider sees them.
Documents that have already left
DLP guards the exits it can see. HILDR embeds tracking markers in sensitive documents and data sets, and reports where, when and on what they are opened, copied or moved outside the network.
Who it is for
Organizations that hold regulated data under the UAE PDPL, Saudi PDPL, PCI DSS or similar rules, and those with valuable intellectual property. Also anyone who has a DLP tool in audit-only mode because blocking caused too much friction.
- HLÍN, DLP for generative AI
- HILDR, track sensitive documents wherever they go
- EIR, data classification
- Insider threat, the people side of data loss
What buyers ask.
We already have a DLP tool.
Most organizations do. The usual problem is coverage and tuning, not the product. We review what it sees, what it misses and why it is noisy, then fix the policies before recommending anything new.
DLP will get in the way of our people.
Badly tuned DLP does. We run policies in monitor mode first, tune them with the business, and block only where the risk justifies it.
What about staff using AI tools?
Traditional DLP cannot see inside the prompt. We pair the programme with HLÍN, which works at the keyboard in the browser and stops sensitive data reaching AI providers.
How do we know if data has already leaked?
DLP stops data at the exits it watches. For documents that may already be outside, HILDR reports when a tracked file is opened or moved somewhere it should not be.
Protect the data that matters, and let work continue.
Put an engineer on a call to scope a DLP programme. No charge, no commitment.