Realm.Security
@Realm_Security
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Security Data Engineering For All
Joined September 2024
Check out the full guide on connecting @pydantic AI schemas to OTEL observability:
realm.security
In this blog, we show union-type structured output allows AI agents to handle uncertain outcomes, critical for auditable and accurate vulnerability triage.
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⏺️ The Audit: This creates a clear dashboard for CISOs and engineers, separating "Auto-Triaged" from "Needs Review". This approach transforms agent uncertainty from a liability into a specific request for expert review, ensuring no threats are silently dropped.
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The Workflow: ⏺️The Fork: The agent analyzes a vulnerability. If data is missing, it selects the UnableToAssess schema. ⏺️The Trace: Using OTEL (via Logfire), we capture a specific span tagged with uncertainty_event=True and the specific justification.
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Auditability is a core requirement for deploying agents in high-stakes environments like vulnerability management. At Realm, we are using Union-Type outputs combined with OpenTelemetry (OTEL) to turn agent uncertainty into a documented workflow.
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If your AI agent triages a vulnerability as "Safe," can you prove why? 🧵
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🏆 𝗧𝗼𝗽 𝟱 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 (🌟 Specter Rank) 1. @Realm_Security (#2)—Investors:@accomplice,+ 2. @Solvelyai (#14)—Investors:@canaan,+ 3. Vooma/@jessebucks (#65)—Investors:@craftventures,+ 4. @e6data (#68)—Investors:@accel,+ 5. @polydotapp (#70)—Investors:@bloombergbeta,+
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⚔️Blog from Realm Data Science: Security Monitoring for AIAgents and MCP 🔗 https://t.co/jSWDpdyeyf
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