AiStrike cuts alert noise with Continuous Detection Engineering

AiStrike has launched Continuous Detection Engineering, a capability that transforms how security operations teams manage detections, shifting from reactive alert triage to proactive, intelligence-driven optimization.

The detection quality gap

Security teams today are overwhelmed by alerts, but the root cause is not volume, it’s detection quality. AiStrike’s analysis across enterprise environments revealed that:

• More than 80% of alerts lead to dead ends
• Fewer than 20% of detection rules ever trigger alerts
• Under 5% of rules generate most of the alert noise
• Over 70% of detection gaps can be addressed using existing data in SIEM
• More than 50% of SIEM data is never used for detection

These inefficiencies lead to alert fatigue, detection blind spots, higher SOC costs, and underused SIEM investments.

AiStrike addresses this challenge by introducing a continuous, closed-loop model for detection engineering.

A New model for security operations

AiStrike’s Continuous Detection Engineering replaces static detection management with a continuously improving system aligned to real-world threats. Inspired by software engineering practices, AiStrike brings detections-as-code, automated validation, and feedback-driven optimization into a closed-loop detection model.

Key capabilities include:

• Detection coverage & gap analysis: Maps detection coverage against frameworks like MITRE ATT&CK and real-world threat intelligence to identify coverage gaps and auto-generate detections to close them
• Intelligent noise reduction: Continuously optimizes high-volume, low-value detections to reduce false positives without sacrificing visibility
• Detection validation & readiness: Ensures every detection is functional, relevant, and actionable, eliminating inactive or misconfigured rules before incidents occur
• Data & SIEM efficiency optimization: Identifies high-impact telemetry to improve coverage while reducing ingestion and storage costs

By integrating feedback from real investigations and incident outcomes, AiStrike ensures detection logic continuously evolves alongside each organization’s environment and threat landscape.

From reactive SOC to continuous improvement

“Security teams don’t have an alert problem – they have a detection engineering problem,” said Nitin Agale, CEO of AiStrike. “Most organizations are operating with noisy, misaligned, or incomplete detections. We built AiStrike to continuously improve detection quality, reduce noise, and align security operations to real threats – without requiring teams to rip and replace their existing stack.”

“AiStrike reduced our alert noise by over 90%, but more importantly, it gave us clear visibility into which detections are actually effective,” said Robert Vaile, CISO, SUBSCRIBE. “Instead of chasing alerts, we’re now continuously improving our coverage against real threats.”

Built for the security stack

Many organizations struggle to stand up a dedicated detection engineering team or modernize workflows around Git, CI/CD, and data engineering. AiStrike delivers these best practices as a product capability, enabling customers to achieve mature detection engineering outcomes without restructuring their SecOps organization.

AiStrike integrates with existing SIEM, XDR, and cloud security platforms, enabling organizations to improve outcomes without replacing existing tools. CISOs gain greater confidence that their SIEM and XDR investments are tuned to their actual risk, while SOC leaders see improved time-to-detect and time-to-contain without adding headcount.

The result:

• Up to 90% reduction in alert noise
• Improved detection coverage aligned to real threats
• Lower SOC and SIEM costs
• Faster, more effective investigation cycles