Loading...
Loading...
Threats and defenses explained for IT teams.
Why it matters: Automate detection and response to match AI-driven reconnaissance and phishing and avoid being outpaced.
Why it matters: Model metadata is an attack surface—treat downloaded models like executable code and restrict sources or require vetted artifacts.
Why it matters: Inventory and lock down reverse proxies and API gateways now — misconfigurations are being actively scanned and abused.
Why it matters: Assume residual risk: design agents with sandboxed runtimes and least-privilege connectors; do not rely on a single global filter to stop prompt injection.
Why it matters: High-quality voice cloning defeats voice biometrics and single-factor voice authentication — enforce stronger MFA or require out-of-band transaction verification.
Why it matters: Inventory genAI data flows now: the average organization sees 223 sensitive-data uploads to AI apps per month — unmanaged apps are a major vector.
Why it matters: Reduces SOC alert noise by ranking incidents with ML, letting analysts focus on higher-risk investigations.
Why it matters: Centralizes agent visibility: ABA applies UEBA baselines to non-human agents, enabling detection of anomalous actions such as data exfiltration or unusual access patterns.
Why it matters: Treat LLM-enabled malware as capable of runtime payload changes; deprioritize static signatures and prioritize behavior-based and anomaly detection.
Why it matters: Treat agents as privileged accounts: protect agent credentials, tool bindings, and API keys with the same controls you apply to service accounts.
Why it matters: Update LangChain immediately and apply vendor-supplied patches to close the secret-extraction vector.
Why it matters: Agentic assistants can be weaponized without user interaction — treat deployed agents as an active attack surface and assume breach.
Why it matters: Inventory shadow AI and agent deployments now — unidentified agents are a fast path to data and IP leakage.
Why it matters: Centralized asset visibility across IT, OT, and medical devices reduces blind spots and helps SOCs verify where automated agents acted.
Why it matters: Assume faster attacker cycles: automation lets attackers iterate exploits and campaigns in minutes, so shorten detection and response SLAs and increase monitoring cadence.
Why it matters: Deserializing agent-generated or user-supplied objects can expose API keys, tokens, and other secrets from running systems.
Why it matters: Treat deepfakes and voice cloning as direct threats to identity controls—enforce multi‑factor and risk‑based authentication for sensitive workflows.
Why it matters: Adversarially trained checkpoints and runtime filters reduce the likelihood of successful prompt injections but do not eliminate the attack class — expect iterative updates and continued monitoring.