AI Runtime Monitoring

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Behavioral Baseline Profiling

Builds AI-native baselines on output distributions, latency patterns, data access, and tool invocation frequency. Alerts on statistically meaningful deviations, not just rule-based thresholds.

Adversarial Activity Detection

Identifies prompt injection, agent hijacking, LLMjacking, unauthorized tool use, and covert agentic workflows as they occur, before business impact.

Automated Containment

Initiates throttling, isolation, or suspension within milliseconds of a confirmed threat. Preserves forensic state for investigation without disrupting adjacent systems.

Automated Response & Remediation

When unsafe behavior is detected, Starseer triggers automated containment, rollback, and retraining workflows, preserving system integrity while generating audit-ready evidence for governance and compliance.

Incident Response Root Cause Analysis

Deep, model-aware root cause analysis, tracing AI incidents back to the exact model, data, or decision pathway that caused them.

Forensic Logging & Compliance Evidence

Generates continuous audit trails, behavioral reports, and risk assessments aligned to MITRE ATLAS, NIST AI RMF, ISO 42001, and sector regulations. Every detected incident produces machine-readable evidence for investigators, regulators, and auditors.

Decision Path Monitoring

Tracks agent reasoning chains, prompt flows, and tool invocations to identify abnormal or manipulated execution.

Behavioral Baseline Modeling

Establishes normal operating patterns for models and agents to detect drift, misuse, and emerging risks.

Adversarial Activity Detection

Identifies agent hijacking, prompt injection, LLMjacking, and covert automation workflows in real time.

Unauthorized Access & Data Flow Monitoring

Detects unsafe data access, exfiltration attempts, and policy violations across AI pipelines.

Risk Scoring & Prioritization

Continuously assesses operational and security risk to focus response on the highest-impact threats.

Automated Containment Actions

Initiates throttling, isolation, suspension, or rollback when unsafe behavior is detected.

Incident Response & Recovery

Orchestrates remediation, retraining, and validation processes to restore safe operations quickly.

Forensic Logging & Audit Evidence

Preserves detailed execution records to support investigations, compliance, and regulatory reporting.

How is AI-EDR different from traditional EDR or SIEM?

Traditional tools are blind to inference chains, prompt flows, and semantic behavioral drift. AI-EDR instruments these natively, giving you the same visibility you have over OS processes, but for AI decision paths.

Can we detect when attackers or insiders are abusing our AI systems?

Yes. Starseer deploys runtime detectors that identify agent hijacking, prompt injection, LLMjacking, unauthorized integrations, and covert workflows, enabling early intervention before business or security impact occurs.

What does "enrolling an AI endpoint" look like in practice?

Once a deployed model, agent, or orchestration pipeline is enrolled, Starseer begins profiling behavior immediately, no manual instrumentation required for supported frameworks.

Can we threat-hunt across historical AI activity?

Yes. The threat hunting interface lets you query across prompt history, tool invocation logs, and output records retroactively, so you can investigate whether a threat existed before you noticed it.

How do we optimize performance and cost without increasing risk?

Starseer monitors latency, throughput, and resource consumption alongside behavioral risk, enabling teams to safely tune models, manage token usage, and optimize infrastructure without compromising security or reliability.