24/7 SOC. AI observability.
Threat hunting that doesn’t sleep.
Detection in seconds, not days.

The first step in AI security is knowing what you have. Armorstack SENTRY performs comprehensive AI asset discovery across your enterprise — identifying every deployed model, API integration, AI-powered SaaS application, embedded agent, and developer-deployed tool that exists in your environment. This discovery process reaches across cloud environments, SaaS platforms, internal development repositories, and end-user devices to surface the full breadth of your AI attack surface.

Once discovered, every AI asset is classified by risk tier, data sensitivity, and governance status. Armorstack establishes formal Shadow AI governance policies, maintains a living AI asset inventory, and enforces acceptable use policies for generative AI tools across your workforce. Business units that have deployed unauthorized AI tools are brought into a managed governance framework — eliminating blind spots without disrupting legitimate productivity use cases.

AI systems require a fundamentally different observability model than traditional applications. SENTRY deploys purpose-built AI observability tooling that monitors LLM input and output streams in real time, tracking token usage analytics, prompt pattern analysis, response anomaly detection, and latency deviations that may indicate adversarial interference or model degradation. Every interaction with your AI infrastructure is logged, scored, and correlated against established behavioral baselines.

Critically, SENTRY integrates AI telemetry directly into your existing SOC workflows and SIEM platforms — ensuring that AI-specific threat signals are correlated with traditional cybersecurity data rather than siloed in a separate tool. When a prompt injection attempt occurs alongside an unusual network connection, SENTRY’s integrated platform surfaces the correlated alert as a single incident, enabling faster investigation and response than disparate tooling allows.

Armorstack maps every AI deployment in your environment to the NIST AI Risk Management Framework’s four core functions: Govern, Map, Measure, and Manage. The Govern function establishes organizational policies, roles, and accountability structures for AI risk. Map identifies AI use cases and their associated risk categories. Measure provides ongoing assessment of AI system performance, bias, and security posture. Manage implements risk responses and maintains continuous monitoring across the full AI lifecycle.

Beyond NIST AI RMF, SENTRY’s AI risk management practice addresses EU AI Act compliance readiness for organizations with European operations, generating the documentation, audit trails, and conformity assessments required for high-risk AI system deployment. Board-level AI risk reporting is delivered monthly — translating technical AI risk metrics into business impact language that enables executive decision-making and investor transparency.

Armorstack conducts structured adversarial testing of AI systems using the same red teaming methodology applied to traditional infrastructure — adapted for the unique attack surface of LLMs, AI agents, and RAG systems. Our AI penetration testing engagements include prompt injection testing across all identified input vectors, jailbreak attempt cataloging, model extraction and inversion simulations, data poisoning exercises for training pipeline security, and supply chain compromise scenarios targeting model registries and API trust boundaries.

Every AI red team engagement concludes with a comprehensive findings report and actionable remediation roadmap. Unlike generic penetration testing reports, Armorstack’s AI security assessments provide prioritized findings mapped to NIST AI RMF risk categories, specific technical mitigations for each identified vulnerability, and a 90-day remediation roadmap with clear ownership assignments. Our team returns after remediation to validate that identified vulnerabilities have been fully addressed.