DLP is an investigation problem, not just a policy problem
Most organizations have DLP tooling. They also have thousands of unresolved alerts, policies too broad to enforce, and no operational answer to what actually happened when data moved. Daylight adds the investigation and response layer that turns DLP signals into closed cases.
1,000s
68%
3–5×
0
Why DLP alerts go dark - and data still leaves
DLP tools generate signals. What they don't provide is the investigation infrastructure to determine intent, establish context, and take a proportionate response. That gap is where incidents happen.
Alert volume makes triage impossible
DLP policies written to catch everything catch too much. Security teams inherit thousands of daily alerts with no capacity to investigate – so the real incidents hide in the queue alongside the benign ones.
The alert has no context
A DLP alert tells you a file was uploaded. It doesn't tell you whether the user had a legitimate reason, whether this is a pattern, or whether anything sensitive actually left. Without context, every alert requires manual work.
AI data movement is invisible
Employees routinely paste proprietary data into Claude, ChatGPT, and other AI tools. Traditional DLP has no visibility into AI prompt streams – a growing blind spot as AI adoption accelerates across the org.
Investigation and response across every data movement channel
Daylight connects your DLP tool's signals to a full investigation workflow – adding user context, behavioral baselines, business intent, and cross-channel correlation. Every alert is assessed. Genuine incidents get a response.
DLP Alert Triage & Investigation
Every DLP alert is triaged against user identity, role, and behavioral baseline – filtering benign noise and escalating genuine risk for full investigation.
Automated triage against baseline activity
Role and business context applied to each alert
Pattern detection across alert history
Cloud Storage & SaaS Transfers
Detect sensitive data moved to personal cloud storage, unmanaged SaaS, or external file shares – and determine whether the transfer was authorized, accidental, or deliberate.
Personal Dropbox, Drive, or OneDrive uploads
Bulk file transfers to external SaaS
Sharing permissions opened to external domains
Source Code & IP Exfiltration
Identify source code, product designs, and proprietary documents leaving through personal email, unapproved repos, or AI tools – with full investigation into the user's intent and access pattern.
Code pushed to personal GitHub accounts
IP documents attached to personal email
Design files exported to unmanaged destinations
AI Prompt Stream Monitoring
Detect proprietary data, customer PII, and credentials entering AI tool sessions – coverage traditional DLP cannot provide, built on direct AI telemetry from Claude Enterprise and partner signals.
PII or customer data in Claude prompt streams
Confidential documents pasted into AI chat
Source code or credentials submitted to AI models
Endpoint & Removable Media
Investigate endpoint DLP alerts for sensitive files copied to USB drives, printed, or transferred to personal devices – with behavioral context to distinguish policy violations from genuine data theft.
Sensitive file copy to removable media
Print activity on restricted document classes
Sync client activity to unmanaged devices
Departing Employee Risk
Apply elevated monitoring for users in offboarding, recently resigned, or under active HR proceedings – identifying bulk export, data staging, and unusual access before their last day.
Bulk download activity preceding resignation
Access to projects outside normal scope
Data staging in personal cloud prior to departure
From Alert to Closed Case, Inside Your MDR Workflow
DLP investigation runs through the same operational pipeline as every other Daylight detection. Your existing DLP tooling keeps generating signals. Daylight provides the investigation and response layer your team doesn't have capacity to staff.
DLP signals ingested and enriched
Alerts from your existing DLP tools are ingested and immediately enriched with user identity, role, device state, and access history – replacing raw alerts with actionable cases.
CASB & cloud DLP integrations
Endpoint DLP signals
AI prompt telemetry
Email & messaging DLP
Intent and context established
Every alert is assessed against the user's behavioral baseline, their role and access privileges, and whether similar activity has occurred before – distinguishing genuine risk from noise at scale.
User behavioral baseline
Cross-channel correlation
HR and offboarding context
Policy violation, insider risk, or benign
Every investigation produces a documented verdict – not a risk score. Authorized activity is closed. Policy violations are documented. Insider risk incidents are escalated with a complete evidence chain.
Documented evidence chain
Intent and context recorded
Expert review for ambiguous cases
Proportionate action taken
Where warranted, Daylight executes response: revoking access, alerting HR or legal, blocking data movement, or escalating to incident response for active insider risk handling.
Access revocation
HR & legal escalation
Incident response handoff
