Cribl Guard proactively ‘sieves out’ risks in hidden sensitive data

Proactive intelligence layer

This new capability adds a proactive intelligence layer to Cribl Guard, allowing software engineering team controls to identify hidden data risks before they result in exposure.

According to Cribl (pronounced crib-el, rhymes with nibble… although the name cribl is derived from the French for sieve, or industrial filtering device, which is crible), with background detection, Cribl Guard proactively finds new patterns of personally identifiable information (PII), secrets and regulated data.

Unlike external data loss protection (DLP) tools that require copying streams out of a software team’s environment, the purpose-built AI model runs entirely within Cribl Stream Workers, ensuring sensitive data analysis never leaves the customer’s own infrastructure.

“Security and IT teams don’t want to enable AI and agentic assistants on sensitive data and face costly, time-consuming cleanups. By analysing data flowing through pipelines, background detection catches sensitive information in flight before it even gets to a data store,” said Dritan Bitincka, co-founder & chief product officer of Cribl. “This helps organisations transition from static policy enforcement to continuous, AI-driven risk discovery and mitigation.”

Background detection is powered by Cribl’s telemetry AI models that identify new, unknown sensitive data, which immediately surfaces the finding in the Cribl interface.

DevSecObvsOps

Security and observability professionals (a team layer that we can perhaps define as SecObvs (inside DevOps perhaps, so maybe DevSecObvsOps) in modern environments) can investigate the sampled events with full event context, dismiss them if appropriate, or instantly convert findings into new Guard rules with a single action. This shortens the path from AI-driven detection to enforced protection before sensitive data reaches downstream destinations such as SIEMs, data lakes, and observability platforms.

“In today’s complex, data-rich environments, security teams can’t afford to wait for sensitive data to land in a SIEM before they act. Cribl Guard’s background detection, powered by purpose-built AI, fundamentally shifts the security paradigm from reactive cleanup to proactive, in-flight risk mitigation,” said Stuart Bowell, global head of observability, security and telemetry at NETbuilder. “It directly addresses the challenges of shadow IT, giving our shared customers the confidence to accelerate their data initiatives while remaining compliant and secure.”

Key functions here in Cribl Guard background detection include the power to uncover hidden risk before exposure i.e teams can automatically detect new PII, secrets and regulated data that existing static rules may have missed, reducing the likelihood of audit fines, breach notifications, and expensive remediation efforts.

The moment a risk is identified, a security admin can efficiently turn it into an active Guard rule, saving time and leading to faster, more confident security decisions. The technology provides defensible evidence of ongoing monitoring and documented mitigation, replacing reliance on rulesets that haven’t been revisited in months.

Cribl Stream Workers, never shirkers

The custom AI model runs within a team’s own Cribl Stream Workers (part of the platform that looks after jobs to process, transform, and route high-volume telemetry data between sources and destinations), so sensitive data is never processed outside your environment — a critical distinction from external DLP tools.

By keeping a custom AI model in the Worker, a node where the data is being emitted and constantly analysing data streams in the background, Cribl helps prevent unexpected sensitive data exposures before they become incidents, minimising financial and operational impacts for the enterprise.

View from co-founder & CPO

CWDN: How does the AI model inside Cribl Stream Workers detect sensitive data without ever leaving the customer’s own infrastructure?

Bitincka: Cribl Guard’s background detection runs a compact, purpose-built model directly on Cribl Stream Workers, inside the customer’s environment. Stream Workers periodically sample telemetry data and run it through the model to identify new patterns of PII, secrets, and regulated data in flight. Only model artefacts and minimal usage analytics are sent to the admin console – no raw events or PII –  and high-level findings are presented to the user to become Guard rules.

CWDN: What happens when background detection flags a false positive — can developers fine-tune the AI rules themselves?

Bitincka: When background detection flags something, it shows up in a centralised view with sampled events and full context so teams can inspect it and decide whether it’s real risk or just noise. If it’s a false positive, they can dismiss the finding and/or refine Guard rules using out-of-the-box or custom rules to narrow scope, add exceptions, or change actions, which reduces future false positives. Cribl manages the underlying AI model and constantly updates it with every new release. Customers tune policy via rules, not by directly retraining the model.

CWDN: How does Cribl Guard handle regulated data differently from general PII when scanning live telemetry pipelines in flight?

Bitincka: Cribl Guard’s background detection uses the same in-flight AI pipeline to spot PII, secrets, and regulated data, but it tags regulated patterns (e.g., payment cards, government IDs, health-related fields) as a higher-risk class in its findings and applies appropriate actions. Cribl Guard lets teams mask sensitive fields (obscure values), drop them entirely so they never reach downstream tools, or reroute them to safer destinations. Because every detection and mitigation step is logged, customers can show auditors a clear record.