This is like putting a smart security guard in your cloud data center who never sleeps, learns what “normal” looks like, and automatically flags or blocks suspicious behavior before it turns into a breach.
Traditional cloud security tools generate too many alerts, miss novel attack patterns, and require heavy manual tuning. This approach uses AI to continuously monitor cloud activity and detect threats (misconfigurations, intrusions, anomalous access) faster and more accurately, reducing breach risk and security operations workload.
Defensible advantage would come from proprietary labeled security telemetry (logs, network traces, access patterns) across many cloud tenants, combined with tuned detection models and tight integration into cloud provider APIs and SOC workflows.
Classical-ML (Scikit/XGBoost)
Vector Search
High (Custom Models/Infra)
High-volume cloud telemetry ingestion and processing (logs, metrics, network flows) and the associated storage and compute cost for real-time anomaly detection at scale.
Early Majority
Focus specifically on AI-driven behavioral/anomaly-based monitoring for cloud-native infrastructures, potentially combining multiple signals (network, IAM, configuration, workload behavior) in a unified detection layer rather than just rule-based or signature-based alerts.