Flarehawk

Flarehawk provides automated threat investigation by connecting security telemetry from your existing tools and grouping noisy signals into actionable incidents. It is a security operations platform designed for teams overwhelmed by alert volumes from WAFs, API gateways, edge networks, and application logs. The core value is transforming raw telemetry into plain-language findings with one-click remediation suggestions, powered by an ML engine that builds unique behavioral models for each environment. This allows security and engineering teams to find and fix security issues earlier, before they escalate into customer-impacting breaches. Whether you have a dedicated SOC or are building security without one, Flarehawk adapts to your normal traffic patterns.

Security teams often face a deluge of alerts from various security tools, each producing its own stream of signals. Analysts must manually correlate events across different time windows and source types, a process that is slow, error-prone, and allows attackers to move laterally while the team is still reconstructing what happened. Flarehawk solves this pain point by automatically correlating related alerts into a single incident, showing exactly which apps are affected and why it matters. The platform reduces the cognitive load of investigating hundreds of raw alerts daily, enabling faster response. Without this capability, organizations risk extended dwell time, increased investigation costs, and missed early indicators of compromise.

The Watch feature continuously ingests telemetry from connected sources such as Cloudflare Logpush, Fastly, and API gateways. It displays a live traffic window that mixes normal requests with suspicious ones, like an unusual spike of 186 requests to an admin export route. The Detect layer then analyzes this window for behavioral anomalies, flagging patterns such as admin API bursts at 18 times the baseline, WAF challenges spiking sharply, or new threat sources appearing. This two-stage approach ensures teams see not just raw logs but also context about what changed compared with normal behavior. Instead of reviewing every alert, analysts can focus on the few incidents that deviate significantly from the learned normal.

Once anomalies are flagged, the Connect step groups related signals from the same time window into a single incident, transforming four separate alerts into one reviewable item. It merges edge network data, API gateway blocks, WAF payload matches, and app log patterns into a unified timeline. Then the Understand phase rewrites the incident in plain language: it states the confidence level (e.g., 91%), which apps are affected (e.g., Admin API probed, Orders export probed, Checkout API clear), and why the situation matters. This allows both security operations and the API team to immediately grasp the impact without having to parse technical jargon or cross-reference multiple dashboards. The result is faster comprehension and accelerated decision-making.