Fleak joins the Databricks startup accelerator. See the announcement

Alert fatigue isn't a volume problem.

Your analysts aren't drowning because there are too many alerts. They're drowning because too many alerts carry no meaning. Fix the data before it reaches your tools.

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Upstream normalization
Any schema
AI agent ready
Vendor neutral
SOC 2 Type II

63%

Of alerts go uninvestigated

not because teams are lazy — because the signal-to-noise ratio is broken

T1 → T3

Detection fidelity uplift

same AI agent — OCSF-normalized inputs, zero model changes

40%

LLM token reduction

less noise in context means fewer tokens, sharper reasoning

Tuning rules doesn't fix
a data quality problem.

Every SOC team tunes. Alert volume goes down temporarily. Then it climbs again. Because the problem isn't the rules — it's what's feeding them.

Raw, mixed-schema data reaches your detection tools

When the same event looks different depending on whether it came from Okta, Azure AD, or Zscaler, your detection logic has to account for every variant — or miss half of them.

AI agents waste cycles parsing before they can reason

LLM-powered SOC tools spend 40% of their compute on "logic grinding" — figuring out what the data means before they can assess whether it's a threat. That's your token budget and your latency.

Real threats hide inside the noise your team learned to ignore

Alert fatigue isn't just an operational problem. It's a security risk. Attackers deliberately generate alert storms to exhaust SOC capacity before executing. Your analysts have been conditioned to look away.

Is your alert worthy?

Not every event that reaches your detection tools deserves to be there. Fleak normalizes and qualifies upstream — so what arrives is signal, not noise.

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Your intention.
Fleak's execution.

Your detection tools don't generate bad alerts. They generate alerts based on what they receive. When the data arriving upstream is normalized to a consistent schema — with the right fields, the right context, the right structure — the same detection logic produces dramatically better signal. Same model. Different inputs. Different results.

works with any detection stack

Splunk SIEM
Microsoft Sentinel
XSIAM
CrowdStrike
Google SecOps
Any AI SOC agent

Brute force detection.
Same logic. Three identity providers. One normalization.

Okta

Schema-specific

Raw: system.login.failed with nested context objects. Detection rule has to parse Okta's schema specifically — breaks if Okta updates their log format.

Azure AD

Schema-specific

Raw: Sign-in activity with different field names and severity model. Second rule required — same threat, different parser.

After Fleak

One rule → all sources

Both normalized to OCSF Authentication class. One detection rule catches brute force across all identity providers — Okta, Azure AD, Zscaler, anything. Schema-agnostic detection.

Schema-specific detection rules are the hidden tax inside every SOC. Every new identity provider means a new rule. Every vendor update breaks an existing one. Fleak eliminates the maintenance — and the missed detections that come with drift.

"With Fleak-normalized data, our AI agent stopped grinding through parsing and moved straight to high-fidelity analysis. Same model. Tier 3 detection fidelity. No extra cost."

Read the full story →

Arif Shaikh, Head of AI Innovations · Gruve.ai