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.
A production LLM stack should log prompts, responses, model/version metadata, latency, token usage, refusals, and safety events so teams can detect drift, prompt injection, and cost spikes before users do. This post compares where Langfuse, Helicone, and Arize fit in the pipeline—and which signals each one surfaces best for alerting and anomaly detection.
When an AI system is compromised, the first question is no longer just “what data was stolen?”—it’s “what model behavior was altered, and where did it spread?” This piece maps the missing IR steps for model integrity checks, prompt-log forensics, and training-data contamination before the next incident becomes an organizational blind spot.
A single poisoned dataset can plant a hidden backdoor, flip labels at scale, or shift the feature space just enough to make a model fail only when it matters. This post shows the detection signals and monitoring controls that can catch contamination before a training run turns hostile.
AI APIs are being scraped, overused, and resold faster than many teams can notice, and the wrong auth choice can make every call a costly liability. This piece compares API keys, JWTs, and OAuth, then shows how to rate-limit and spot abuse without punishing legitimate users.
SOC teams are being promised fewer alerts, faster investigations, and less burnout—but which AI features are actually cutting time to triage, correlating logs reliably, and accelerating threat hunts? This post separates measurable ROI from common failure modes like false confidence, noisy automation, and hallucinated context.
Attackers are already using AI to mass-generate convincing phishing lures, mutate payloads between campaigns, and speed up vulnerability discovery—turning low-skill operators into far more effective threats. The hard question for defenders: when malware can rewrite itself and its social engineering in real time, which detections still work?
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.