Deep dives, guides, and strategic playbooks on automating agency operations, client onboarding, Friday KPI reporting, and building reliable production AI systems.
Phishing is just the beginning. Here are all 7 social engineering attack types with real examples from 2025–2026 that show exactly how each one works.
Social engineering is responsible for 60% of all data breaches. This complete guide covers every technique, 2025–2026 case studies, and how to defend your team.
Security testing an AI-powered application is not the same as testing a traditional web app. It starts the same way then it goes somewhere entirely new. Here is the full methodology, the tools that actually work, and what a proper AI security assessment looks like.
Three years ago 'OWASP Top 10 for LLM Applications' would have been a meaningless phrase. Today it's the most important document in AI application security. Here is what every item means in the language of someone who builds and ships products, not a security academic.
You built a RAG system to make your AI accurate and grounded. You fed it your documentation, your product data, your knowledge base. But did you consider what happens when an attacker plants something in that knowledge base before your users query it? Five documents in a database of millions is all it takes.
A startup's OpenAI bill jumped from $400 to $67,000 in a month. Their API key had been sitting in a public GitHub repository for 11 days. Automated bots found it within minutes. This is LLMjacking and Microsoft has already filed lawsuits over it.
The attacker never touched the chat interface. They sent an email. The AI read it, followed the hidden instructions inside, and silently exfiltrated data from files the attacker had no access to. This is indirect prompt injection and it's the most dangerous AI vulnerability most organisations haven't heard of.
Prompt injection is to AI applications what SQL injection was to web apps in the early 2000s a fundamental confusion between instructions and data that attackers know exactly how to exploit. Here's how it works, why it's so hard to fix, and what real-world breaches look like.
Your team shipped an AI feature last sprint. You tested it for bugs. You tested it for accuracy. Did anyone test it for security? AI features introduce a class of vulnerabilities that traditional security tools simply don't look for.
