Rethinking Detection Engineering: A Practical, Threat-Informed Path Forward for Modern Security Teams

Rethinking Detection Engineering: A Practical, Threat-Informed Path Forward for Modern Security Teams, published by SANS Institute in February 2026, argues that detection engineering must be treated as a life cycle engineering discipline rather than a one-time rule-writing exercise. The paper examines why detection content decays after deployment, where Detection-as-Code (DaC) helps and where it struggles, what SIEM platforms need to provide natively to support sustainable detection engineering, and how threat-informed practices and AI can be applied without sacrificing engineering discipline.

Key takeaways:

• Modern security teams typically don't fail because they lack detections — they fail because deployed detections quietly decay as log formats, infrastructure, and adversary behavior change over time
• The real operational cost of detection engineering is life cycle maintenance, not the initial writing of detection rules
• Most SIEM platforms still lack native testing capabilities such as log replay, unit tests, and regression tests, forcing teams to build this tooling themselves
• Detection-as-Code (DaC) brings version control, peer review, and CI/CD discipline to detection content, reducing teams' fear of making changes
• DaC introduces its own engineering overhead and is not realistic for every team, particularly smaller teams without dedicated DevOps support
• Alerts can be technically correct yet still not actionable if they lack identity, business, or technical context needed for analysts to make decisions
• Risk-based, entity-centric detection can reduce noise, but only scales when its correlation logic is transparent and tunable rather than an opaque black box
• Sigma provides a shared, structured way to express detection intent across teams and tools, but it is not a guarantee of easy portability into a production-grade SIEM query
• MITRE ATT&CK coverage scores are a useful directional signal but not proof of detection effectiveness, since absolute coverage is neither measurable nor achievable
• AI can accelerate detection engineering by drafting documentation and identifying noise patterns, and agentic AI may eventually coordinate testing workflows, but only within an engineering-disciplined process with humans in the approval loop
• The most effective detection engineering programs combine platforms that remove life cycle friction, shared threat-informed abstractions, and skilled analysts who adapt and validate detections in their own environment

The paper's central argument is that sustainable detection engineering depends on making change safe, quality measurable, and decay visible — not on writing more rules or chasing a perfect coverage score. As SIEM platforms absorb more of the testing, observability, and governance burden, detection teams can shift their effort from infrastructure plumbing toward threat-informed research and high-fidelity analytics, using shared patterns rather than shared, ready-made detections to compound learning across the industry. The analysis draws on the authors' hands-on experience in purple teaming, detection engineering, and SOC operations, and was published as part of SANS's Research Program with sponsorship from Splunk, a Cisco company.