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.
NIST’s AI Risk Management Framework is easier to apply when you treat it as four operational questions: who owns the model, what can go wrong, how do you prove it’s behaving, and how do you respond when it doesn’t? For a deployed LLM, "Measure" means more than accuracy—it means tracking jailbreak success rates, hallucination frequency, policy violations, latency, drift, and abuse signals against real production traffic.
When employees paste contracts, customer records, or source code into AI tools, vendors may retain prompts, outputs, and metadata far longer than your team expects—and opt-outs from training rarely stop all retention. This post explains what GDPR and CCPA actually require, how to verify a vendor’s data-use controls, and the deployment steps that reduce exposure before your next AI rollout.
AI agents can now automate recon, suggest exploit paths, and even chain steps with alarming speed—but they still struggle with context, novel defenses, and anything that requires real-world judgment. This post asks where PentestGPT, AutoAttacker, and similar tools are actually useful, and where ethics and authorization must draw a hard line.
A hallucinated answer is more than embarrassing when it tells an engineer to patch the wrong service, cites a fabricated CVE, or gives false confidence that a system is safe. This post breaks down the failure modes and the guardrails that can keep AI from turning bad security advice into real risk.
If employees are already pasting sensitive data into AI tools, what is your governance model doing to stop it? CISOs need a practical framework now: classify inputs, codify acceptable use, rehearse AI-specific incident response, and vet AI vendors before a breach starts with a prompt.
An attacker can turn an LLM into an exfiltration relay by hiding secrets in generated text patterns or by forcing tool calls that send data out through webhooks. This post shows the attack patterns, the telemetry that exposes them, and the controls that block leakage before the model becomes a silent data hose.
A structured red team should test four things in order: threat model, adversarial prompts, tool-abuse paths, and output validation gaps. This post shows a repeatable methodology for finding the failure modes attackers are most likely to exploit in 2026.
When the “user” is an AI agent, zero trust means every prompt, tool call, and data request must be verified, scoped, and logged in real time. This post shows how microsegmentation, just-in-time privilege, continuous identity checks, and tamper-evident audit trails stop agents from becoming an enterprise-wide blast radius.
A HuggingFace model can be more dangerous than it looks: malicious weights, unsafe deserialization (like PyTorch pickle CVEs), and tampered LoRA adapters can all turn a download into code execution or silent backdoors. The real question is: how do you verify provenance before an AI model reaches production?
A single malicious document in your vector store can steer answers, leak hidden instructions, or even exfiltrate sensitive data through a carefully crafted query. This post breaks down where RAG breaks first—and the concrete controls that stop poisoned retrieval, indirect prompt injection, and unauthorized data leakage.