OptIQ is now Matters, read about it here
Background
Insider Data Risk Management
Predict risk before insiders become incidents.Matters doesn’t wait for DLP rules to fail. It detects and responds to suspicious data movement in real time using behavioral analysis and content awareness.
Predict risk before insiders become incidents.
Why it MattersMatters uses a predictive behavior model that continuously learns how users interact with sensitive data. Instead of relying on static rules, it flags abnormal behavior based on intent, giving you early signals of misuse, compromise, or negligence across employees, contractors, and privileged accounts.

You can’t secure what you can’t see. 83% of organizations faced insider attacks in 2024. Insider risk isn’t just a threat. It’s a blind spot. Matters gives you the visibility to detect intent, not just incidents.
Why it Matters
ADVANCED DETECTION
Too Many Access Points. Too Little Visibility. Insider Risks Thrive in the Gaps.
Your data isn’t confined to a single place anymore.

It’s scattered across SaaS apps, cloud platforms, endpoints, and internal tools, accessed by employees, contractors, vendors, and even AI agents. This sprawl makes it nearly impossible to track who is doing what with sensitive information.
Misuse doesn’t happen in one placeAn engineer copies production data into a spreadsheet.

A vendor shares a drive folder without clearance.

A junior analyst exports files to a personal device.

These actions don’t usually trigger alarms, not because they’re harmless, but because traditional tools weren’t built to see across all these environments, let alone connect the dots.
Access Isn’t Static, and Intent Isn’t Always ClearPeople take on new roles. Vendors get temporary access. AI agents interact with systems in ways we’re still learning to monitor. What’s normal for one person might look suspicious for another, and without context, it’s hard to tell the difference.
By the Time It’s Flagged, the Damage Is DoneMost insider threats aren’t discovered until after something has gone wrong.

That’s because older tools generate isolated alerts without deeper insight, leaving security teams piecing together a puzzle after the fact.
Background
How We Solve Insider Risk Before It SpreadsLet’s say a backend developer with access to GitHub starts cloning private repositories at 11 PM, then uploads files to a personal OneDrive. No red flags are raised because they technically had access. But it’s still an insider threat.

Matters is built to catch this before it turns into a breach.

Here’s how:
Background
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Identity-Aware BaseliningWe monitor user behavior down to each asset. In this case, the developer had never accessed multiple repos in one session or worked outside business hours. Our engine picks up the deviation from their usual access pattern, flagging the activity as unusual even before data leaves the environment.
Identity-Aware Baselining
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Contextual Anomaly DetectionIt’s not just what they did, it’s how and when. Our system detects that the data being accessed is sensitive (codebase), that the timing is off (after-hours), and that the action (bulk clone) doesn’t match past behavior. That combination is what elevates this from normal activity to a credible risk.
Contextual Anomaly Detection
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Role-Based Risk ScoringWe dynamically score access events based on the user’s role, privileges, and deviation from expected behavior, so misuse by higher-risk profiles gets surfaced faster. Cloning repositories triggers a higher risk score than routine code pushes, even if both actions are done by backend developers. In this case, the score spikes, highlighting privilege misuse and placing the user under real-time scrutiny.
Role-Based Risk Scoring
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Intent Prediction EngineAccidental or malicious? Our system models intent by comparing this action to semantic and behavioral patterns from similar roles. The combination of late-night access, data movement to an unsanctioned location, and volume indicates more than carelessness; it’s flagged as likely malicious.
Intent Prediction Engine
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Intent is the real threat surface.
Users don’t need to break rules to put data at risk. Don’t just monitor insiders. Understand them.

Matters understands how users behave around data and why their actions may signal risk. Instead of static rules, we surface threats based on patterns, pressure points, and intent.