AI-EDR (AI Endpoint Detection and Response) applies the principles of traditional cybersecurity EDR to AI models and agents. Instead of monitoring endpoints for malicious code, AI-EDR continuously monitors AI behavior at runtime — detecting drift, anomalies, misalignment, and unsafe decisions before they impact real-world systems.

Starseer's Runtime Assurance solution is built on this approach, focusing on behavioral signals across decision-to-action chains where real operational risk lives.

Traditional EDR monitors software processes, file activity, and network behavior on endpoints. Starseer monitors the behavioral signals of AI models and agents — what decisions they make, why, and what real-world actions result.

AI-specific failure modes like model drift, adversarial inputs, and agent misalignment are invisible to traditional EDR tools. Starseer is purpose-built to detect exactly these failure modes in autonomous, edge, and enterprise AI systems.

AI Detection Engineering applies the discipline of security detection engineering to AI behavior. It involves defining what normal and unsafe AI behavior looks like, designing and testing detections tailored to specific AI systems, deploying them at runtime, and continuously tuning them using live evidence to reduce noise and improve signal quality.

Starseer treats detection engineering as a core discipline across the AI lifecycle — not an afterthought bolted on after deployment.

Starseer's detection engineering covers: behavioral drift, data distribution shift, adversarial inputs, model misalignment, cascading failures in multi-agent systems, and model-level threats like backdoors and weight tampering.

Detections are designed, validated against real-world conditions, and continuously tuned — not static rules that become stale as models and environments evolve.

AI model validation confirms that a model is fit for its intended real-world deployment before release. This goes beyond benchmark accuracy to verify performance, latency, hardware fit, cost constraints, and behavioral stability under realistic conditions.

Starseer's Operational Readiness service provides pre-deployment validation for AI models and agents — treating readiness as a continuous discipline, not a one-time pre-launch gate.

Accuracy metrics are measured on benchmark datasets that often don't reflect real deployment conditions. A model can score well on benchmarks and still fail in production due to latency issues, hardware incompatibility, behavioral instability under edge-case inputs, or data distribution mismatch.

Starseer validates the dimensions that actually determine whether a model survives contact with the real world — performance, cost, latency, hardware fit, and behavioral stability under the conditions it will actually face.

Yes. Many customers engage Starseer after deployment to identify performance or stability issues, tune models and agents safely, and optimize tradeoffs across accuracy, latency, reliability, and cost.

Incident findings from production feed back into validation and detection tuning, making future deployments progressively more resilient. Starseer maintains runtime evidence and behavioral records that support internal reviews, executive reporting, and regulatory investigations.

Starseer serves: enterprises deploying AI agents and MCP servers, manufacturers and industrial operators running autonomous or edge AI, AI-first product companies scaling from pilot to production, healthcare and finance organizations with regulated AI systems, and teams recovering from failed pilots or unstable deployments.

Engagements range from short advisory and validation work to long-term hands-on runtime tuning and detection engineering partnerships.

Starseer helps development teams ensure AI-driven systems — vision inspection, predictive maintenance, robotics, drones, and autonomous vehicles — are operationally ready and stable in production. Starseer validates models against real conditions, monitors runtime behavior on the edge, and supports rapid investigation when AI-driven decisions impact throughput, safety, or quality.

Common scenarios include: vision systems behaving inconsistently under changing lighting, AI-driven automation causing production slowdowns or false stops, and root cause analysis after AI-related downtime or defects.

Starseer helps AI startups move faster without sacrificing reliability. Starseer supports teams transitioning from pilots to production by validating real-world readiness, reducing failed deployments, and providing runtime insight that enterprise customers increasingly demand before signing contracts.

Common scenarios: scaling from proof-of-concept to production, demonstrating operational maturity to enterprise buyers, and reducing post-deployment surprises and customer escalations.