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.
As teams pull pretrained models, adapters, and embeddings from public hubs, the supply chain around AI is becoming a quiet entry point for malware, backdoors, and data leakage. This post breaks down the checks that matter most—artifact signing, dependency scanning, provenance verification, and safe model serving—before a trusted model becomes an untrusted package.
Foresiet’s March–April incident roundup shows attackers using synthetic voice and video to impersonate suppliers, rush invoice changes, and bypass approval chains in minutes. Which verification steps still hold up when the caller sounds right, looks right, and moves faster than the finance team?
As assistants start persisting preferences, plans, and credentials across sessions, their memory stores become a high-value target for poisoning and silent data exfiltration. This post looks at the controls practitioners need—state scoping, write validation, and memory review—to keep long-lived agents from carrying yesterday’s attack into tomorrow’s workflow.
Foresiet’s March–April incident roundup suggests AI is now compressing the full business-email-compromise loop: research, impersonation, and persuasion into minutes. Which controls still work when a fake executive can be spun up, tailored, and deployed at machine speed?
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.