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.
AI now writes spear-phishing that looks tailored, timely, and almost indistinguishable from real internal mail, which is why legacy email filters are missing attacks that exploit context instead of keywords. This post shows what behavioral analysis and LLM-based detection can catch—and where human defenders still outperform the model.
An autonomous AI agent is only as safe as its weakest surface: the model, tools, memory, messages, and the human interface each create distinct paths for prompt injection, data exfiltration, and unauthorized action. This post maps those attack vectors end to end—and shows where defenders should place controls before the agent acts on its own.
Every AI SaaS app can quietly become a supply-chain risk if it sees your prompts, files, or customer data. Does your vendor questionnaire cover data processing agreements, model-training opt-outs, breach-notification SLAs, and the full subprocessor chain?
If an AI agent can fetch your data, can you prove which user, model, or workflow authorized it—and revoke that authority instantly? This post compares wallet-based identity, short-lived JWT delegation, and MCP session tokens, and shows why bearer tokens alone can’t answer the attribution problem.
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?