
Microsoft Project Perception adds Guardian Agents to limit AI permissions
Microsoft introduces adaptive security controls for AI agents within Project Perception to contain risks and enforce policy boundaries.
Everything Vector Bulletin has reported about AI security: 11 stories, newest first. Part of our AI coverage.

Microsoft introduces adaptive security controls for AI agents within Project Perception to contain risks and enforce policy boundaries.

Detecting malicious code injected via software development utilities requires monitoring build outputs rather than examining user prompts.

A new joint whitepaper from Microsoft and NVIDIA provides enterprises with a shared taxonomy and controls to manage risks in open-weight AI deployments.

Generative systems create novel outputs from patterns, while traditional systems classify existing data, creating distinct attack surfaces for each.

The physical cooling requirements of AI clusters dictate the network topology and create unique attack surfaces that traditional server farms do not possess.

Treating AI models as untrusted insiders prevents latent data leakage and logic manipulation that standard perimeter controls miss entirely.

Model theft often leaves no malware traces, only anomalous inference patterns and resource spikes that standard security tools ignore entirely.

Trusting open-source weights as safe is a false premise because model parameters can embed backdoors that survive standard integrity checks and retraining.

Most teams assume output filtering prevents leaks, but the real exposure occurs when context windows ingest unredacted source material before processing begins.

Synthetic media is not just about deepfakes; it is a structural shift in how digital evidence is generated, verified and trusted across your infrastructure.

AI agents inherit systemic vulnerabilities from their underlying models, creating attack surfaces that traditional perimeter defenses cannot detect or block effectively.