AI-EDR (AI Endpoint Detection and Response) is the AI-native equivalent of traditional EDR, purpose-built for AI infrastructure. Where traditional EDR monitors processes, files, and network activity on endpoints, AI-EDR monitors the behavior of AI models and agents at runtime — detecting tampering, misuse, anomalous behavior, and policy violations based on how models act, not just what they output.

What does Starseer AI-EDR monitor at runtime?

Starseer AI-EDR monitors active AI models and agents across your environment, including their behavioral baselines, decision-to-action chains, activation patterns, output distributions, and policy compliance. It identifies unauthorized or tampered models, detects anomalous behavior as it occurs, and maintains a continuous record of AI activity for forensic investigation and audit.

Does AI-EDR work for autonomous and edge AI systems?

Yes. Starseer AI-EDR is specifically designed for autonomous and edge AI deployments — including drones, robotics, manufacturing automation, and medical devices — where AI failures manifest as physical or operational outcomes rather than traditional security events. It provides runtime visibility and detection in environments where traditional security tooling has no coverage.

Can Starseer AI-EDR detect shadow AI and unauthorized models?

Yes. Starseer discovers and catalogs active AI models and agents across endpoints, servers, and edge infrastructure — including models deployed without formal security review. It verifies runtime model identity against approved baselines, flagging unauthorized, tampered, or substituted models as they execute.

What happens when AI-EDR detects a threat?

When a detection fires, Starseer AI-EDR triggers automated or guided response workflows to limit impact and stabilize operations. It captures decision-level timelines and behavioral context to support rapid investigation, and feeds incident findings back into detection tuning and model validation so that each incident makes the system more resilient.

How does AI-EDR support compliance and audit requirements?

Starseer AI-EDR maintains continuous, audit-ready behavioral records of all AI model activity. These records map to major frameworks including NIST AI RMF, the EU AI Act, ISO standards, and OWASP AI Top 10, providing the evidence needed for internal reviews, regulatory investigations, and customer assurance.

AI Runtime Monitoring & Response for AI Systems

Starseer AI-EDR — app.starseer.ai/edr

Endpoints monitored

Active detections

94%

Baseline compliance

Threats contained

Behavioral Baselines Live

agent-orch

0.3σ

chatbot-prod

0.1σ

finance-agent

1.8σ

code-assist

2.7σ

claims-proc

0.2σ

search-api

0.1σ

Recent Detections

prompt-injection-v3 → agent-orch 2m

data-exfil-pattern → code-assist 14m

baseline-drift → finance-agent 38m

deployed: jailbreak-bypass-v4 1h

llmjacking-resource → prod-east-1 2h

recalibrated: chatbot-prod 3h

Endpoint Health

agent-orch

Claude 3.5

chatbot-prod

GPT-4o

finance-agent

Claude 3.5

code-assist

DeepSeek-V3

claims-proc

GPT-4o

search-api

Cohere-R+

summarizer

Llama-3.1

rag-pipeline

Mistral-7B

ATLAS Coverage — 87%

Covered

Partial

Gap

— What Is AI-EDR

Detection engineering and runtime response,
unified for how AI actually operates.

Every deployed model and agent is an endpoint. Unlike servers or workstations, AI endpoints operate probabilistically, reasoning, deciding, and acting through inference chains no traditional EDR can instrument.

AI-EDR closes the full loop: design versioned behavioral detections, test them against adversarial fixtures, deploy through CI/CD with quality gates, then run them continuously at runtime against live AI behavior. When a detection fires, AI-EDR contains the threat, preserves forensic state, and feeds findings back into detection tuning.

AI threats don't produce OS-level signals. A backdoored model activates only when triggered. A prompt injection arrives as a normal message. Behavioral drift is gradual and silent. Catching these threats requires detection at the model level, designed with engineering rigor and executed in real time with full inference-chain visibility.

— Detection Engineering Built In

Treat your detections
like production code.

AI-EDR includes a full detection engineering workflow. Design versioned detections, validate them against real attack patterns, and ship through CI/CD with the same rigor you apply to any production code.

Design

Write versioned YARA detection artifacts

Express behavioral patterns, threshold logic, and contextual conditions using Starseer's AI-native schema.

Test

Validate against adversarial fixtures

Measure true positive rate, false positive rate, and latency before any detection ships.

Deploy

Ship through CI/CD with quality gates

Automated checks block under-tested rules from production. Canary rollout supported.

Tune

Manage false positives without degrading coverage

Every suppression is versioned, scoped, and auditable. No silent coverage loss.

Retire

Flag stale rules before they become blind spots

Starseer surfaces coverage gaps as models change and attack patterns shift.

— Why Traditional EDR Falls Short

Traditional security tools can't see inside the model.

Blind to prompt flows

Traditional EDR sees file I/O and network calls — not what a model was asked, why it took an action, or how it reasoned to that decision. The entire inference chain is invisible.

No inference chain visibility

Cannot inspect reasoning paths, tool invocations, or agentic decision sequences. Multi-step agent workflows are a complete black box from a detection standpoint.

Semantic drift is invisible

Behavioral baselines are built on binary process behavior, not probabilistic AI output patterns. Gradual semantic degradation never registers as an anomaly in any OS-level signal.

Can't detect prompt injection

Adversarial prompt manipulation doesn't trigger OS-level signals. It arrives as a normal user message — completely invisible to any tool that isn't inspecting model inputs at runtime.

— The Results

What teams using AI-EDR are achieving.

94%

AI endpoint coverage rate across deployed models and agents

300%

Increase in threat detection rate versus traditional EDR tooling

47%

Reduction in mean time to contain AI security incidents

"We had no way to see what our agents were actually doing between request and response. AI-EDR gave us the inference-chain visibility that closed a gap nothing else could touch."

— Enterprise AI Security Team

— Full Capabilities

Unlock powerful AI runtime assurance
across our all-in-one platform.

Prompt Lineage

Traces every prompt from origin through transformation to final model input, capturing the full chain of custody across agents, orchestrators, and retrieval pipelines so you can reconstruct exactly what a model received and why.

Decision Path Monitoring

Tracks agent reasoning chains, prompt flows, and tool invocations to identify abnormal or manipulated execution sequences before they cause impact.

Behavioral Baseline Modeling

Establishes normal operating patterns for models and agents to detect drift, misuse, and emerging risks against statistically meaningful AI-native baselines.

Adversarial Activity Detection

Identifies agent hijacking, prompt injection, LLMjacking, and covert automation workflows in real time, before business or security impact occurs.

Unauthorized Access & Data Flow Monitoring

Detects unsafe data access, exfiltration attempts, and policy violations across AI pipelines and agentic workflows in real time.

Risk Scoring & Prioritization

Continuously assesses operational and security risk to focus response on the highest-impact threats, reducing alert fatigue for AI security teams.

Automated Containment Actions

Initiates throttling, isolation, suspension, or rollback within milliseconds when unsafe behavior is detected, without disrupting adjacent systems.

Incident Response & Recovery

Orchestrates remediation, retraining, and validation processes to restore safe operations quickly, with full audit trails for every response action taken.

Forensic Logging & Audit Evidence

Preserves detailed execution records to support investigations, compliance, and regulatory reporting. Aligned to MITRE ATLAS, NIST AI RMF, and ISO 42001.

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.

Build the Detections.
Run Them at Runtime.

Detection engineering and runtime response,
unified for how AI actually operates.