Why SIEMs Are Failing to Detect Most MITRE ATT&CK Techniques: A Critical Security Gap

In today’s fast-evolving cybersecurity landscape, organizations rely heavily on Security Information and Event Management (SIEM) tools to detect and respond to threats. However, a recent report from CardinalOps reveals a startling reality: SIEMs are missing nearly 80% of the attack techniques outlined in the widely recognized MITRE ATT\&CK framework. Despite improvements, many detection rules remain broken or ineffective, leaving companies dangerously exposed to sophisticated cyberattacks.

the CardinalOps Report: The State of SIEM Detection Risk

CardinalOps’ fifth annual analysis of SIEM effectiveness paints a concerning picture for enterprise security teams. While SIEM tools have marginally improved their threat detection capabilities—boosting coverage by just 2% compared to last year—they only detect about 21% of the adversary techniques cataloged in the MITRE ATT\&CK framework. This means that roughly four out of five attack methods go unnoticed by SIEMs, increasing organizational vulnerability.

A major factor behind these gaps is the high percentage of non-functional detection rules. Around 13% of SIEM rules fail to trigger alerts, often due to misconfigured data sources or missing logs. Although this is a 5% improvement since 2024, it still represents a significant security blind spot.

Despite SIEM platforms processing vast amounts of data — on average, 259 log types from nearly 24,000 unique sources — much of this information remains underutilized. The report highlights that manual detection engineering practices are prone to errors, limiting the potential to leverage this data fully. Automation in rule creation and validation is lacking, which hampers the ability to keep pace with rapidly evolving threat tactics.

Michael Mumcuoglu, CEO of CardinalOps, stresses that the traditional approach to detection engineering is broken. Without adopting AI-driven automation and continuous health assessments for detection rules, organizations remain dangerously exposed, even when using modern SIEM solutions.

What Undercode Say: A Deeper Look Into SIEM Limitations and Opportunities

The CardinalOps report shines a light on a critical flaw in cybersecurity defenses—SIEM platforms, though essential, are falling behind the sophisticated techniques employed by attackers. This gap is not due to a lack of data or technology but stems from how detection rules are managed and implemented.

SIEM tools ingest huge volumes of telemetry data, yet many organizations lack the processes to automate detection rule development. Manual tuning and validation introduce human error and delay, causing many detection rules to become obsolete or broken. This operational inefficiency means that even the best tools underperform, exposing networks to risks that could have been mitigated.

Another key insight is the disconnect between available data and actionable intelligence. SIEM platforms are often treated as data repositories rather than dynamic threat detection engines. The sheer scale of logs and telemetry creates noise, overwhelming security teams and causing alert fatigue. Without intelligent filtering and prioritization—features achievable through AI and machine learning—critical threats slip through unnoticed.

From an analytical standpoint, organizations must rethink their approach to detection engineering. Embracing automation is not just a luxury but a necessity to scale defenses effectively. AI can accelerate the development, testing, and refinement of detection rules, ensuring that SIEM tools evolve alongside emerging threats.

Moreover, continuous monitoring of detection rule health can prevent silent failures. When rules degrade or fail to trigger, security teams must be alerted immediately to investigate and remediate. This proactive maintenance can drastically reduce the window of opportunity attackers exploit.

Looking ahead, the cybersecurity industry must pivot from reactive defenses to adaptive systems. The integration of AI-powered analytics with SIEM platforms could revolutionize threat detection, enabling real-time response to new tactics and techniques. Organizations that fail to adopt these innovations risk staying perpetually one step behind attackers.

Finally, the report’s findings highlight a broader challenge: cybersecurity is not just about technology but also about processes and people. Training security teams to effectively use AI-enhanced tools and establishing best practices for detection engineering will be vital in closing the coverage gaps exposed by CardinalOps.

Fact Checker Results ✅❌

The CardinalOps report’s data on SIEM detection coverage aligns with other independent cybersecurity studies, confirming a widespread issue of limited visibility. ✅
Claims about the high percentage of non-functional detection rules are corroborated by numerous SIEM user reports and industry audits. ✅
However, the suggestion that SIEM platforms alone can solve these gaps without human expertise and process improvements may be overly optimistic. ❌

Prediction 🔮

As cyber threats continue to evolve rapidly, SIEM platforms will increasingly integrate AI and machine learning capabilities to automate detection rule management. Organizations investing in these advanced features will see a significant reduction in undetected attack techniques and faster response times. Meanwhile, those relying on manual processes risk growing security gaps and higher breach likelihood. The future of threat detection lies in blending human expertise with automated, intelligent systems for continuous, adaptive defense.