RapDev Webinar

AI is moving fast and so is the attack surface it creates. Most organizations are already using LLMs in production or rolling out AI coding assistants to their developer teams, but few have visibility into what those tools are actually doing. Meanwhile, the volume and complexity of alerts has outpaced what any team can triage manually. RapDev has built a structured, three-phase approach to help security and engineering teams get ahead of it within Datadog.

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We'll deep dive into our three-phase approach:

• Phase 1 — LLM Observability: Instrument and monitor your LLM-powered applications in Datadog with full visibility into token usage, latency, prompt/response tracing, and model cost.
• Phase 2 — AI Coding Assistant Security Monitoring: Monitor coding agent activity, cost, and output via Datadog's Agent Console. Use Code Security and PR Gates to catch bad code, ingest DLP logs for defense-in-depth, and leverage SIEM to correlate threats from developer endpoints. Track governance effectiveness through Dashboards and Reporting.
• Phase 3 — Proactive AI-Powered Monitoring: Leverage Datadog's native AI capabilities like Watchdog, anomaly detection, and intelligent alerting to shift your team from reactive firefighting to proactive observability.