Realm Security Debuts Detection Integrity for Reliable SIEM Optimization

Realm Security, the SOC-aware Security Data Pipeline Platform (SDPP), today announced two new capabilities ahead of Black Hat USA 2026. Detection Integrity proves that reducing SIEM log volume never breaks a threat detection. New search inside Realm Data Haven, the platform’s searchable retention layer, makes the data you keep out of the SIEM directly queryable when an analyst needs it. Together, they let teams cut ingestion cost and avoid vendor lock-in without gambling on coverage.

Also Read: Infrastructure as Code vs Configuration Management: What’s the Difference?

AI has changed how fast attacks move. Attackers now use agents to find vulnerabilities, build exploits, and run multi-stage intrusions faster than any human team can respond. Security teams are answering with agents of their own, but those agents run on a data foundation that years of cost-cutting have weakened. Rising SIEM ingestion costs pushed most teams into a bad trade: shrink the telemetry going in and hope they can still detect threats. Industry analysis has found that half of detection rule failures trace back to log collection gaps, and as a result, organizations are only detecting 1 in 7 attacks. The agents that teams are now deploying to defend run on that same foundation.

“As too much data became a security problem, CISOs focused on cutting SIEM ingestion costs, and in doing so many of them quietly starved their detections,” said Pete Martin, CEO and co-founder of Realm Security. “Every data pipeline can cut volume. Only Realm proves the cut didn’t cost you a detection. And Data Haven keeps what you didn’t send to the SIEM searchable when you need it, not locked behind a lengthy rebuild or a per-query bill.”

Detection Integrity: cut ingestion volume without gambling coverage

Detection Integrity uses Realm Clarity AI, the platform’s intelligence layer, to understand every source and destination and the best way to optimize the flow between them. It reads every detection running in a team’s SIEM, maps each one to the log sources and fields it depends on, and builds the pipeline protection rules needed to preserve coverage as volume drops. It supports Sigma, KQL, and SPL, and extends to other detection formats. When Realm applies a reduction, it produces a shareable report showing what was reduced, what was protected, projected savings, and MITRE ATT&CK coverage before and after. When a reduction can’t be applied safely, it flags the finding instead of cutting.

Also Read: Infrastructure as Code Governance: Managing Risk in Enterprise Cloud Environments

The premise: the platform that reduces your data should be the one that proves the cut is safe. Legacy pipeline tools reduce by configured rules and leave the team to find the coverage gap later. Detection Integrity is the first time a pipeline platform reasons about both sides of the cost-versus-coverage equation. In one deployment, a 40,000-employee global manufacturer used it to translate more than 1,000 KQL rules and their Data Collection Rule logic into protection rules in 72 hours, work that would otherwise take weeks by hand.

Data Haven Search: search your full security history directly, no rebuild

Data Haven is the searchable retention layer of the Realm Platform. It keeps an immutable raw copy of a team’s security data with OCSF-normalized observables attached to every event. With search, teams can now query their full history directly on those observables and resupply the exact events an investigation needs to any destination.

This happens without the restore-and-dig step that defines SIEM archive tiers, and without the schemas, transforms, and infrastructure a general-purpose data lake needs to be queryable. Data Haven is priced on what a team ingests, not on how much they query or retrieve, so analysts never have to decide mid-investigation whether a search is worth the cost. Search launches with unmetered queries.

Write to us [⁠wasim.a@demandmediaagency.com] to learn more about our exclusive editorial packages and programmes.