# Detection engineering Detection engineering is a cybersecurity discipline for designing, evaluating, and validating detection analytics using telemetry, behavioral signals, and contextual evidence. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Benchmaxxing: When the Benchmark Becomes the Target DevFeed: [Benchmaxxing: When the Benchmark Becomes the Target]() Original publisher: [Read original article]() Author: Nathan Danneman Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [Benchmark](), [Artificial Intelligence](), [Cybersecurity](), [Ground truth / benchmark quality](), [Detection engineering]() Tags: [ai](), [ai-and-cybersecurity](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [cybersecurity](), [leaderboards](), [securing-ai](), [security](), [validation]() ### AI overview The article explains how public AI and cybersecurity benchmarks can become targets for optimization, a practice it calls "benchmaxxing." It argues that gaming, ceiling effects, data leakage, binary scoring, omitted costs, and aggregate scores can make benchmark results poor proxies for real-world defensive capability. The article proposes task-coupled internal benchmarks intended to evaluate end-to-end cyber agents and support rigorous science rather than visibility-driven score optimization. ### Source excerpt The more attention a benchmark receives, the stronger the incentive to optimize for it. In AI and cybersecurity, this can have significant consequences. ## Black Hat USA 2026: Building the Agentic SOC, One Live Event at a Time DevFeed: [Black Hat USA 2026: Building the Agentic SOC, One Live Event at a Time]() Original publisher: [Read original article]() Author: Jessica (Bair) Oppenheimer Published: 2026-09-07T15:00:58Z Content type: article Language: en Sources: [Security @ Cisco Blogs]() Topics: [Detection engineering](), [SIEM, Security, Observability](), [Threat Hunting & Intel](), [telemetry](), [NOC](), [Malware]() Tags: [agentic-soc](), [black-hat](), [cisco-secure-access](), [cisco-talos](), [cybersecurity](), [duo](), [firewall](), [malware](), [network-operations-center](), [noc](), [security](), [security-operations-center](), [soc](), [splunk-cloud](), [splunk-enterprise-security](), [telemetry](), [thousandeyes]() ### AI overview Cisco describes its work protecting the Black Hat USA 2026 network alongside NOC leaders and technology partners. The team combined security telemetry and workflows to support visibility, detection engineering, threat hunting, malware analysis, AI protection, and Agentic SOC development. ### Source excerpt Cisco is the Security Cloud Provider for the Black Hat conferences. Learn about the latest innovations for the Agentic SOC. ## How to start the AI-accelerated defense DevFeed: [How to start the AI-accelerated defense]() Original publisher: [Read original article]() Author: info@1password.com (Wade Wells) Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Artificial Intelligence](), [Detection engineering](), [Prompt Engineering](), [Large Language Model](), [log management](), [Security]() Tags: [ai](), [ai-adoption](), [documentation](), [engineering](), [llm](), [logging](), [onboarding](), [security](), [validation](), [workflow]() ### AI overview This article explains how a security team began adopting AI for detection engineering by supplying the documentation and workflow context that AI systems lack. It covers an AI Detection Engineering stack involving logging pipelines, log onboarding, detection validation, threat modeling, and detection logic, and shows why better context produces more reliable results than relying on prompts or increasingly powerful models. ### Source excerpt Early on in the AI adoption boom, I gained a reputation for just throwing everything at it to see what would stick. That wasn't the most effective strategy, and my token usage was crazy high. There are a ton of talks and posts on all the cool ways you can use AI for detection engineering, but I didn't see any that showed you where to begin. So, this isn't another blog about why you need to use AI in your defensive workflows. It seems most people understand why we need that. My focus is to show how our team got started and realized that providing AI with the necessary context is key to detection engineering successfully adopting AI. This is not just about building detection logic, but that is one of the goals. This foundation helps create the AI Detection Engineering stack: logging pipelines, log onboarding, detection validation, threat modeling, and more. An LLM does not know your stack, so out of the box it has limited value in a security review. In our experience, reliable results depend less on the fanciest model and more on the documentation and context around the workflow. If a human reads your log inventory and still has to ask three people what the ingestion method is, your agent does too. Why cold prompting fails When we first started using AI tooling, we realized prompts alone could get stuff done, but the output was inconsistent. Fields were missed, assumptions were made, and some detection logic was wrong. We saw it write queries that would not work in our SIEM. Usually these were around wildcards. The playbooks it wrote were generic, the tuning was poor, and some detections were just bad. With enough re-prompting, the output would improve, but it always required some massaging. The effort invested in the agent inputs had a noticeable impact on the quality of the outputs. TL;DR: garbage in, garbage out. Where our context came from At the start, this was just internal documentation we built to make our own lives easier. It started with new-hire materials a ## Detecting multi-stage attacks on AWS: A guide to cross-service signal correlation DevFeed: [Detecting multi-stage attacks on AWS: A guide to cross-service signal correlation]() Original publisher: [Read original article]() Author: Nisha Kashyap Published: 2026-08-26T17:39:19Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services (AWS)](), [Security](), [Detection engineering](), [Amazon CloudWatch Logs](), [AWS CloudTrail](), [VPC Flow Logs](), [Amazon S3](), [data]() Tags: [advanced-300](), [amazon-cloudwatch](), [amazon-cloudwatch-logs](), [amazon-guardduty](), [amazon-route-53](), [amazon-web-services-aws](), [analysis](), [aws-cloudtrail](), [aws-security-hub](), [best-practices](), [cybersecurity](), [guide](), [incident-response](), [logs](), [s3](), [security](), [security-blog](), [security-identity-compliance](), [shared-responsibility-model](), [technical-how-to](), [threat-detection](), [vpc-flow-logs]() ### AI overview This article explains how security engineers can detect multi-stage attacks on AWS by correlating signals across services with business context. It presents examples using CloudWatch Logs Insights and discusses expanding the correlations into an automated pipeline. ### Source excerpt A single alert from one security service tells you something happened. Read that signal alongside activity from other services and your own business context, and you will know whether what happened is part of a multi-stage attack. Consider a short sequence. An identity calls GetCallerIdentity from a source address it hasn't previously used. Within minutes, [...] ## From Exploit Code to Production Detection: Building a CVE-2026-31431 (Copy Fail) detection with Agents DevFeed: [From Exploit Code to Production Detection: Building a CVE-2026-31431 (Copy Fail) detection with Agents]() Original publisher: [Read original article]() Author: Ryan Simon Published: 2026-05-28T00:00:00Z Content type: article Language: en Sources: [Datadog Security Labs]() Topics: [Vulnerabilities](), [Linux](), [Detection engineering](), [kernels](), [Security](), [AI-assisted coding](), [Cryptography]() Tags: [agents](), [analysis](), [audit-trail](), [code](), [coding](), [engineering](), [kernel](), [kernels](), [linux](), [operations](), [security](), [vulnerabilities](), [vulnerability]() ### AI overview CVE-2026-31431, known as Copy Fail, allows an unprivileged local user to corrupt Linux page caches through AF_ALG sockets and escalate privileges to execute code as root. The article explains the exploit's kernel mechanisms and describes how Datadog Security Research used coding agents to develop and ship a detection in a single session. ### Source excerpt CVE-2026-31431 (Copy Fail) lets any unprivileged user corrupt the Linux page cache via AF_ALG sockets to escalate privileges. This post covers the exploit mechanics and how Datadog Security Research used coding agents to ship a detection content pack in a single session. ## Pathfinding Labs: Deploy, test, and learn from 100+ intentionally vulnerable AWS environments DevFeed: [Pathfinding Labs: Deploy, test, and learn from 100+ intentionally vulnerable AWS environments]() Original publisher: [Read original article]() Author: Seth Art Published: 2026-05-18T00:00:00Z Content type: article Language: en Sources: [Datadog Security Labs]() Topics: [Amazon Web Services](), [AWS IAM](), [Terraform](), [Go Language](), [Command-line interface](), [Text-based user interface](), [ctf](), [Detection engineering](), [Security & compliance, Cloud security](), [Authorization]() Tags: [aws](), [aws-iam](), [cli](), [cloud](), [cloud-security](), [ctf](), [go](), [iam](), [sandbox](), [security](), [techniques](), [terraform](), [tool](), [tools](), [validation]() ### AI overview Pathfinding Labs is a collection of more than 100 intentionally vulnerable AWS environments for practicing and validating detection of IAM privilege-escalation and other cloud security misconfigurations. The project includes a web catalog with CTF-style hints and solutions, Terraform-based labs, and plabs, a Go CLI with an interactive terminal interface for deploying and exploiting the labs. ### Source excerpt Introducing Pathfinding Labs, a collection of intentionally vulnerable AWS environments for red teamers and blue teamers to deploy, exploit, and use for detection validation. ## Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber DevFeed: [Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber]() Original publisher: [Read original article]() Published: 2026-05-07T13:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [Cybersecurity](), [Critical Infrastructure](), [vulnerability](), [Detection engineering](), [Reverse Engineering](), [Threat Research]() Tags: [ai](), [critical-infrastructure](), [cybersecurity](), [developers](), [engineering](), [openai](), [research](), [reverse-engineering](), [security](), [validation](), [vulnerability]() ### AI overview OpenAI describes Trusted Access for Cyber, an identity- and trust-based framework that provides verified defenders with enhanced GPT-5.5 capabilities for authorized cybersecurity workflows. GPT-5.5-Cyber is being introduced in limited preview for defenders securing critical infrastructure, with safeguards against activities such as credential theft, persistence, malware deployment, and exploitation of third-party systems. ### Source excerpt OpenAI expands Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber, helping verified defenders accelerate vulnerability research and protect critical infrastructure. ## OCSF Achieves ITU Support: Powering AI-Ready Security Operations DevFeed: [OCSF Achieves ITU Support: Powering AI-Ready Security Operations]() Original publisher: [Read original article]() Author: Rod Wallace Published: 2026-03-24T16:48:50Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [Security](), [AI-Ready Data](), [Open Source](), [Cybersecurity](), [Artificial Intelligence](), [data](), [Detection engineering](), [Programming]() Tags: [ai](), [ai-ready-data](), [algorithms](), [black-hat](), [cybersecurity](), [data](), [logs](), [machine-learning](), [open-source](), [security]() ### AI overview OCSF is presented as a vendor-neutral, open source security-data standard supported for ratification by the ITU. By normalizing logs from data centers, cloud environments, and SaaS applications into a common language, it creates AI-ready data for correlation, pattern detection, and more effective security operations. ### Source excerpt The security industry stands at an inflection point. In November 2024, the Open Cybersecurity Schema Framework (OCSF) joined the Linux Foundation, cementing its role as a vendor-neutral, open source standard for the global security community. Last summer at Black Hat 2025, we showed you how OCSF was powering AI-driven security operations. Then in December 2025, [...] ## 2025 Year in Review - Top 10 DevFeed: [2025 Year in Review - Top 10]() Original publisher: [Read original article]() Author: Phil Venables Published: 2026-01-10T14:36:22Z Content type: opinion Language: en Sources: [Risk and Cyber]() Topics: [Cybersecurity](), [Architecture & Design](), [Detection engineering](), [Vulnerabilities]() Tags: [2025](), [benchmarking](), [cybersecurity](), [execution](), [leadership](), [risk](), [technology](), [transformation](), [vulnerabilities](), [year-in-review]() ### AI overview A 2025 year-in-review highlighting posts about cybersecurity as a business leadership, strategic design, and sustainable execution function. It emphasizes moving from reactive security practices toward scalable, proactive systems and cautions against benchmarking based only on inputs such as budgets rather than control effectiveness. ### Source excerpt The most read posts in 2025 coalesced around the concept that successful cybersecurity is fundamentally a function of business leadership, strategic design, and sustainable execution . The unifying themes across the top posts emphasize shifting security from an artisanal, reactive craft to an industrial-scale, proactive capability focused on building scalable, self-reinforcing systems (flywheels). Transformation requires leaders to manage stakeholder expectations carefully, particularly by... ## Seeking symmetry during ATT&CK® season: How to harness today's diverse analyst and tester landscape to paint a security masterpiece DevFeed: [Seeking symmetry during ATT&CK® season: How to harness today's diverse analyst and tester landscape to paint a security masterpiece]() Original publisher: [Read original article]() Author: Márk Szabó James Shepperd Ben Tudor Published: 2025-12-10T15:03:51Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [Endpoint Security & XDR](), [Cybersecurity](), [Resilience](), [Detection engineering](), [Threat Research]() Tags: [2025](), [analysts](), [article](), [attacks](), [business-security](), [ciso](), [cybersecurity](), [endpoint-security](), [enterprise](), [incident](), [industry](), [infrastructure](), [practitioner](), [report](), [resilience](), [security](), [testing]() ### AI overview The article explains how security practitioners can interpret and connect cybersecurity reports and tests from analyst firms and independent testing labs. It focuses on endpoint security, including product evaluations, feature testing, broader market analyses, and assessments against known advanced adversary attacks, to support more informed protection-stack and purchasing decisions. ### Source excerpt Interpreting the vast cybersecurity vendor landscape through the lens of industry analysts and testing authorities can immensely enhance your cyber-resilience. ## Minimizing False Positives: Enhancing Security Efficiency DevFeed: [Minimizing False Positives: Enhancing Security Efficiency]() Original publisher: [Read original article]() Author: Tiago Mendo Published: 2025-07-01T23:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [threat detection](), [Cybersecurity](), [Detection engineering](), [Vulnerabilities](), [data]() Tags: [2025](), [application-security](), [awareness](), [blog](), [cybersecurity](), [data](), [developer](), [devops](), [efficiency](), [engineering](), [false-positives](), [organizational](), [reduce](), [security](), [security-tools](), [threat-detection](), [vulnerabilities]() ### AI overview The article explains how false-positive security alerts consume investigation time, contribute to alert fatigue, and delay responses to genuine cyberattacks. It contrasts false positives and false negatives with accurate true-positive detection, emphasizing the value of security tools that improve accuracy and help organizations respond to real threats efficiently. ### Source excerpt Discover how enhanced security tools reduce false positives and streamline threat detection for more effective cybersecurity management. ## Know your tools: The full range of Elastic Security's detection engineering capabilities DevFeed: [Know your tools: The full range of Elastic Security's detection engineering capabilities]() Original publisher: [Read original article]() Author: Kseniia Ignatovych Published: 2024-11-12T05:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Detection engineering](), [Security](), [SIEM, Security](), [threat detection](), [alert triage](), [Tooling]() Tags: [alert-fatigue](), [alert-triage](), [automated](), [automated-threat-protection-cybersecurity-defense-security-compliance](), [blog](), [detection-engineering](), [elastic](), [features](), [latest-features](), [quality](), [security](), [security-siem](), [siem](), [threat-detection](), [tools](), [workflows]() ### AI overview This Elastic Security blog provides an overview of detection engineering capabilities, including customizable prebuilt rules, alert suppression, manual rule runs, automated case creation, and machine learning jobs. ### Source excerpt This blog provides a comprehensive overview of the detection capabilities available in Elastic Security. Learn about the latest features and get useful tips and tricks for your detection practice! ## Rolling your own Detections as Code with Elastic Security DevFeed: [Rolling your own Detections as Code with Elastic Security]() Original publisher: [Read original article]() Author: Mika Ayenson,Kseniia Ignatovych,Justin Ibarra Published: 2024-05-14T00:00:00Z Content type: tutorial Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [Detection engineering](), [Security](), [Code](), [CI/CD](), [SIEM, Security](), [Tooling]() Tags: [automated-threat-protection-cybersecurity-devops-open-security](), [ci-cd](), [devops](), [research](), [review](), [security](), [siem-security](), [testing](), [tooling]() ### AI overview Elastic describes how its detection-rules repository supports Detections as Code, including rule-management tooling, tests, CLI commands, and automation. The article explains how teams can adapt these capabilities for custom detection-rule management using peer review, testing, and CI/CD practices. ### Source excerpt Detections as Code (DaC) is transforming security rule management. Learn about Elastic's latest enhancements in the detection-rules repo, how to leverage it for custom rule management, and our comprehensive guide for adopting DaC.