Guides, best practices, and technical references on application security, AI-generated code risks, and vulnerability management.
How SAST, DAST, SCA, and exploit validation fit together in a real engineering workflow.
Three scanning approaches, three attack surfaces. Here is what each covers and how to layer them without tripling your alert volume.
A practical SDL structure that reduces friction while catching real issues before they reach production.
A continuous process that tracks risk, not just finding counts. How to build one that actually closes vulnerabilities.
Source code, dependencies, containers, infrastructure, and runtime. How each scanning method works and what it misses.
ASPM aggregates findings from multiple security tools into a single risk view. What it does, where it fits, and when you need it.
Supply chain attacks target the tools you trust, not your own code. How to cover open source, build pipelines, and third-party integrations.
AI code review catches style issues and common bugs fast. But it has consistent blind spots around security. Here is where it fails.
AI coding assistants accelerate development but introduce new security risks. How to evaluate them with security as a primary criterion.
What prompt injection, insecure output handling, and the other eight LLM risks actually look like in production AI systems.
API vulnerabilities are the most common source of modern breaches. How to test REST and GraphQL APIs for the flaws static scanners overlook.
The gap between flagging a potential vulnerability and confirming it is exploitable is where most security programs waste the most time.
The highest-impact security rules for Python, JavaScript, Go, and Java, with examples of what vulnerable and safe code looks like.
SAST scans source code without running it. DAST tests a running application. Here is how to use both without doubling your noise.
API keys, tokens, and passwords committed to code are among the most exploited attack surfaces. How to detect and remediate them before they are used.
Threat modeling finds security problems before you write a line of code. How to run it without turning every design meeting into a security theatre exercise.
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Kira runs autonomously on your codebase and delivers verified, exploitable findings with proof. Not alerts. Not maybes.