DeepTempo is a threat detection platform that uses a deep learning foundation model called LogLM to identify cyberattacks in real-time. The system analyzes operational behavior across enterprise environments to detect malicious activity, including AI-driven attacks, reconnaissance, command and control communications, credential access attempts, and other threats that may evade traditional security tools. The platform operates by learning normal system behavior patterns and identifying deviations that indicate malicious intent. It processes network flow data and security telemetry to detect threats early in the attack lifecycle, before systems are exploited. The foundation model continuously adapts to new attack patterns and behaviors without requiring manual rule updates or signature maintenance. DeepTempo deploys across hybrid environments including cloud infrastructure, on-premises systems, and operational technology networks. The platform integrates with existing security stacks and data pipelines, working alongside SIEM, EDR, and other security tools. It provides detection context to support investigation and incident response activities. The system includes flow preprocessing capabilities to reduce SIEM data storage requirements. Detection intelligence is shared across protected environments to maintain current awareness of evolving attacker tactics. The platform is designed for security operations center teams to reduce false positives and operational overhead associated with detection engineering.