All news with #ai runtime security tag

🔒 CrowdStrike explains how widespread AI adoption expands the enterprise attack surface, exposing models, data pipelines, APIs, and autonomous agents to new adversary techniques. The post argues that legacy controls and fragmented tooling are insufficient and advocates for real-time, full‑stack protections. The Falcon platform is presented as a unified solution offering telemetry, lifecycle protection, GenAI-aware data loss prevention, and agent governance to detect, prevent, and remediate AI-related threats.

🔐 CrowdStrike explains how inadvertent 'leakage' — when dependent or correlated observations are included in training — can inflate machine learning performance and undermine threat detection. The article shows that blocked or grouped data splits and blocked cross-validation produce more realistic performance estimates than random splits. It also highlights trade-offs, such as reduced predictor-space coverage and potential underfitting, and recommends careful partitioning and continuous evaluation to improve cybersecurity ML outcomes.