CyberIntelAI tracks how AI is changing the threat landscape in real time.
Not just new capabilities, but how attackers are actually using them: the incidents, the tools, and the shifts that matter once systems move into production.
If you're defending modern infrastructure, this is where the signal is.
In March and April 2026, AI-enabled attacks became cheaper to launch, faster to scale, and harder to stop, according to IBM X-Force, Akamai, and aggregated threat intel. What happens when the same tools defenders rely on are now driving the most damaging breaches?
Prompt injection is no longer the main risk; autonomous agents now need policy checks, tool allowlists, and human approval at runtime to prevent silent data leaks and destructive actions. If your AI can browse, write, or act, how do you stop it from chaining a poisoned prompt into a real-world incident?
The Anthropic incident made one thing clear: AI is no longer just helping defenders, it’s becoming part of the attack surface. If models can be probed, manipulated, or misused at scale, what security controls actually hold up?
Retrieval-augmented apps are now a top AI attack surface because poisoned documents can steer model answers, leak secrets, or trigger unsafe actions. This post shows the controls teams are using to sanitize sources, isolate tools, and verify retrieved context before generation.
Darktrace’s latest threat report says nearly 70% of incidents in the Americas now begin with stolen or misused accounts, not software exploits. As attackers use AI to move faster and adapt in real time, are traditional detection tools becoming too slow to catch the breach?
As more teams ship LLM-powered products, red teaming is shifting from a one-time test to a recurring control that finds prompt injection, data leakage, and unsafe tool use before attackers do. The question is no longer whether to test your model, but how to do it continuously without slowing delivery.
As LLM apps move from pilots to production, prompt injection is turning AI gateways into a practical control point for filtering malicious inputs, enforcing policy, and logging risky model calls. The real question is no longer whether to deploy one, but how to make it effective without breaking useful workflows.
Retrieval-augmented generation can leak secrets, amplify prompt injection, and surface poisoned documents if its data pipeline is not hardened end to end. This post shows the security controls practitioners need before RAG becomes their next production incident.
Prompt injection remains one of the most reliable ways to steer AI assistants into leaking data or taking unsafe actions, even when basic filters are in place. Learn why defenders are shifting from prompt-only controls to model isolation, tool permissioning, and runtime policy enforcement.
Prompt injection, data exfiltration, and tool misuse are no longer edge cases—they’re the failure modes security teams are finding first in production copilots and agentic systems. This post examines how AI red teaming catches these risks before attackers do, and which tests matter most in 2026.