Keep sensitive data under your control.
Protect the information your business depends on, wherever your people work. Bring visibility, practical policies and real-time controls to sensitive data moving through web, cloud and AI services.
Illustrative example only, not live data
Your data moves. Your protection should too.
A spreadsheet uploaded to personal storage. Customer records pasted into an AI prompt. A confidential document shared with the wrong recipient. Everyday actions can expose valuable information, even when nobody intends to cause harm.
Data loss prevention (DLP) identifies sensitive information and applies rules to how it can be used or transferred. An effective programme combines content inspection, identity and destination context with clear ownership, proportionate enforcement and a response process your people understand.
Protection that follows your data
Understand exposure
See which sensitive data is moving, who is using it and which destinations create risk.
Control risky transfers
Apply consistent policy to inspected traffic, with exceptions for approved business needs.
Make incidents actionable
Give investigators the context to prioritise events and improve controls.
Six capabilities, working as one
Select a capability around the ring.
Content-aware detection
Identify personal information, payment data, business records and intellectual property using appropriate content rules. Combine patterns, keywords and contextual checks to distinguish meaningful exposure from routine activity.
Ask about thisClassification and labels
Use existing sensitivity labels alongside content inspection. A missing label should prompt closer analysis, while a confidential label should inform the permitted destination and handling rules.
Ask about thisWeb and cloud controls
Apply policy to supported uploads and transfers through the inspection service. Define permitted destinations and activities so teams can collaborate while reducing exposure through unsanctioned services.
Ask about thisGenerative AI data protection
Set rules for sensitive information submitted to supported AI services. Assess prompts and file uploads where inspection is available, and distinguish approved business use from high-risk sharing.
Ask about thisIdentity and destination context
Scope policies by user, group, destination and available device context. Give departments the access they need while keeping sensitive workflows subject to appropriate controls.
Ask about thisIncident visibility and reporting
Record policy matches and enforcement decisions with the context needed for investigation. Agree who can view evidence, how long it is retained and when incidents should enter existing response workflows.
Ask about thisWhat we help protect
Personal and customer data
Protect identifiers, contact details and customer records according to your organisation’s handling requirements.
Financial and regulated records
Apply controls to payment information, financial exports and other records requiring restricted handling.
Intellectual property
Reduce exposure of source material, product plans, pricing, research and confidential client work.
Inspection boundaries
Document encrypted or unsupported content, bypass routes and unmanaged activity. Add complementary controls where inline inspection cannot provide coverage.
Pick the data. Pick the destination. See the decision.
Explore an illustrative policy decision. These examples show how controls can support different business workflows; they are not live monitoring data.
Illustrative example policy. Real policies are agreed with your data owners and tuned against legitimate workflows.
Visibility first. Blocking when it is right.
Discover
Map priority data, owners, users and destinations. Establish which traffic can be inspected and identify the gaps.
Design
Agree classification, policy scope, exceptions and incident responsibilities. Define what success will look like.
Validate
Start with visibility and representative testing. Tune rules against legitimate workflows before introducing blocking.
Improve
Review incidents, false positives and exceptions. Extend coverage as applications, data and working practices change.
Built around how you work
Hybrid working
Apply policy to supported traffic from connected users working in the office or remotely. Assess unmanaged devices and offline activity separately.
Cloud collaboration
Make approved sharing routes clear and restrict sensitive transfers to personal accounts or unapproved services where controls support it.
Responsible AI adoption
Help teams use AI with clear rules for acceptable information, approved tools and escalation when a sensitive prompt is detected.
Accidental and deliberate exposure
Investigate risky activity using evidence and business context. A policy match is a signal for review, not proof of malicious intent.
Is DLP the same as backup?
No. DLP aims to reduce inappropriate disclosure or transfer of information. Backup supports recovery after deletion, corruption or disruption. A resilient data protection strategy needs both.
Can DLP inspect encrypted traffic?
Inspection of HTTPS traffic typically depends on approved TLS inspection and compatible applications. End-to-end encrypted content, certificate pinning and encrypted files can limit visibility. These boundaries should be assessed during design.
Will it interrupt legitimate work?
Controls should be introduced gradually. Monitoring, representative testing, contextual rules and documented exceptions help reduce unnecessary blocking. Some workflows may need an approved alternative route.
Does this cover endpoints, email and data at rest?
Coverage depends on the deployed controls. Inline web and cloud DLP should not be assumed to cover removable media, offline files, all email channels or stored data. We scope these needs and identify complementary controls.
Can it support compliance?
DLP can support data handling policies, audit evidence and information security controls. Its value depends on configuration, coverage and operational processes; it does not establish compliance by itself.
How do we start?
Begin with the data that would cause the greatest harm if exposed. Identify its owners and likely transfer routes, then pilot a small set of policies with agreed measures and response responsibilities.
Put practical controls around your sensitive data.
Talk through your data flows, current controls and priority risks. Build a clear starting point for a DLP programme that supports your business.
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