Detection Engineering: Implementing Detection-as-Code in an Existing Environment: Part 3 – Validation and Future Possibilities
Consolidating detection data is only the first step. In Part 3 of our Detection-as-Code series, we explain how we validate schemas and shared fields, resolve inconsistencies, and integrate checks into GitHub CI pipelines. Tiered validation blocks invalid changes while allowing teams to fix non-urgent issues over time.
Breaking the Semantic Cache: A LiteLLM Case Study (CVE-2026-89032)
Semantic caching can reduce AI inference costs, but isolation failures can expose sensitive data and enable malicious responses or tool…
Detection Engineering: Implementing Detection-as-Code in an Existing Environment: Part 2 – Execution
Moving to Detection-as-Code starts with making existing detection data reliable and machine-readable. In Part 2, we explain how we validated…
Detection Engineering: Implementing Detection-as-Code in an Existing Environment: Part 1 – Introduction and Concept
How do you introduce Detection-as-Code into an environment where detections are already being developed and deployed? This customer case study…
PHP2Shell Operational Guidance
This public operational guidance for PHP2Shell is provided to customers and published for the benefit of the wider community.
Verborgene Tastenanschläge: Forensische Analyse eines Digispark HID-Geräts
Ein Digispark mag wie ein harmloses USB-Entwicklungsboard aussehen, lässt sich jedoch leicht in ein Gerät zur Tastenanschlag-Injektion verwandeln, das sich…
Keystrokes in Disguise: Forensic Analysis of a Digispark HID Device
A Digispark may look like a harmless USB development board, but it can easily be turned into a keystroke injection…
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