Sweet is excited to announce the release of its LLM Cloud Detection Engine, which harnesses the power of LLMs to spot anomalies within cloud environments.
In cloud security, the ability of LLMs to analyze complex cloud environments and highlight deviations from expected behavior makes them invaluable. This advanced detection capability enhances security operations by uncovering hidden risks and providing precise, actionable insights into cloud activity.
By leveraging Sweet’s LLM Cloud Detection Engine, organizations can move beyond static rules to dynamically identify "unknown unknowns"— emerging threats that have never been observed or documented.
How It Works
Traditional, rule-based detection methods are notoriously noisy and generate a multitude of false positives, often resulting in missed threats and time-consuming investigations of up to 10 days or more.
Sweet’s advanced LLM processes and organizes cloud activity into unified sessions, evaluating anomalies in real-time and adapting findings to the nuances of each unique environment. This innovation allows security teams to identify zero-day attacks and other unknown threats with unprecedented accuracy.
Here’s a step-by-step breakdown of the process:
- Unified Sessions: Events are aggregated into cohesive sessions based on shared identifiers.
- Real-Time Anomaly Detection: The LLM evaluates session metadata and sequences, distinguishing between benign irregularities and genuine threats.
- Clear Incident Labels: Each anomaly is classified as "malicious," "suspicious," or "bad practice," enabling teams to focus on actionable findings.
- Actionable Visualizations: Heatmaps highlight unusual session metadata, such as access patterns or activity details, helping analysts quickly identify outliers. It also tracks event sequences within a session, visually pinpointing specific actions or parameters that may indicate malicious intent.

Enhancing Sweet’s Unified Detection and Response Across Cloud, Workloads, and Applications
Sweet’s LLM detection engine seamlessly integrates into our runtime-based detection and response platform, building on the unified detection capabilities already in place. While our existing layers - including CDR, ADR, and CWPP - provide robust and holistic security coverage, the LLM-powered detection engine takes it further by pinpointing the exact anomalous "smoking guns" within complex datasets.
With features like severity scoring to prioritize risk levels and granular heatmap visualizations for rapid decision-making, Sweet empowers analysts to operate with unparalleled clarity and efficiency, streamlining investigations and reinforcing comprehensive protection.
Advantages of Sweet’s LLM Cloud Detection Engine
- Unmatched Precision: Reduces cloud detection noise to 0.04%, enabling teams to focus on real threats.
- Future-Proof Security: Identifies unknown threats, ensuring organizations are prepared for emerging attack vectors.
- Simplified Investigations: Intuitive visualizations and clear labeling streamline the detection process.
- Future-Ready Detection: Detects unknown and emerging threats, empowering organizations to stay ahead of attackers.
- Faster Response: Simplifies investigations with heatmaps and labeling, reducing MTTR to 2–5 minutes.

Use Case: Scaling Application Detection and Response (ADR)
Sweet Security’s LLM-powered detection engine revolutionizes Application Detection and Response (ADR) in dynamic cloud environments where traditional rule-based methods fall short. By cross-correlating potential attack patterns with extensive application data, the engine identifies the elusive "smoking gun" signals indicative of an attack. With Sweet’s ADR, organizations can pinpoint high-risk anomalies and address them with confidence, reducing investigative efforts and enhancing overall security effectiveness.
Sweet’s LLM detection engine represents a significant leap in cloud security, delivering precise, actionable, and future-proof detection of stealth cloud attacks. Ready to experience the next generation of detection and response? Book a demo today!