What Is LLM Security Observability?
LLM Security Observability is the Armorstack program that closes the monitoring gap between AI deployment and AI security operations. Organizations deploy LLMs — customer service chatbots, internal knowledge assistants, clinical decision support, financial analysis copilots, code generation tools — without the telemetry infrastructure to detect prompt-injection attacks, jailbreak attempts, or sensitive-data exfiltration. The consequences range from data breaches to regulatory violations to reputational damage. Armorstack LLM Security Observability instruments every covered LLM deployment at the inference boundary, establishes behavioral baselines for normal interaction patterns, and generates security events for prompt-injection chains, jailbreak attempts, and sensitive-data leakage (PII, PHI, PCI, IP). Behavioral-drift and anomalous-output alerting builds on that same baseline data and is on our near-term roadmap as those detection models mature. All events feed the Armorstack SENTRY 24/7 SOC through the same response workflow as any other security event. No separate console, no separate team.
What You Get
- Inference-boundary instrumentation for each covered LLM deployment
- Behavioral baseline establishment (normal interaction patterns)
- Prompt-injection detection and alerting with severity classification
- Jailbreak attempt detection and response playbook integration
- Sensitive-data exfiltration prevention (PII, PHI, PCI, IP detection in outputs)
- Model-behavior drift monitoring — phased rollout as the anomaly-detection layer matures (roadmap)
- Monthly LLM security posture report
- SOC incident response for AI-specific events
Who This Is For
CISO / Security Team
Responsible for LLM deployments that process sensitive data and need security telemetry parity with traditional application monitoring.
AI / ML Engineering Teams
Deploying customer-facing or internal LLM applications that require security guardrails beyond the model provider’s built-in safety filters.
Compliance & GRC
Needing documented evidence of LLM monitoring for HIPAA, SOC 2, PCI-DSS, or cyber insurance auditors.
How It Works
Deployment Inventory & Scoping
Catalog every LLM deployment in scope: provider (OpenAI, Anthropic, Google, Azure OpenAI, open-source), access pattern (API, plugin, copilot), data classification, and user population.
Instrumentation Deployment
Install inference-boundary monitoring at the API gateway or proxy layer. No model-side changes required. Behavioral baseline established over 30 days.
SOC Integration
LLM security events routed into Armorstack SENTRY SOC with defined severity levels, response playbooks, and escalation paths. AI events handled alongside all other security events.
Ongoing Operations
Continuous monitoring with tuning as model behavior evolves, monthly posture report, quarterly policy review, and playbook updates for new attack patterns.
Investment
Multi-deployment volume discounts available.
Timeline: Instrumentation deployment 2–4 weeks; ongoing monthly subscription.
Every engagement begins with a scoping call and a written proposal. Work begins only after the engagement agreement is executed.Request a Consulting Proposal
Why Armorstack
SOC-Integrated, Not Siloed
Every AI security event goes through the same Armorstack SENTRY 24/7 SOC workflow as every other security event. One response process, one team, one escalation chain.
Inference-Boundary Approach
Monitoring at the API boundary — not requiring model-side access or changes. Works with any LLM provider (OpenAI, Anthropic, Google, Azure OpenAI, Bedrock, Mistral, open-source).
Behavioral Baseline Accuracy
Baselines calibrated to your actual use-case interaction patterns, not generic LLM behavior — sharper signal for prompt-injection and jailbreak detection today, with behavioral-drift alerting expanding on that same foundation as the capability rolls out.
Compliance Evidence
Monthly reports provide auditor-ready evidence of continuous LLM monitoring for SOC 2, HIPAA, PCI-DSS, and cyber insurance purposes.
Frequently Asked Questions
Which LLM providers and deployment patterns do you support?
All major API-accessible providers: OpenAI (GPT-4, o-series), Anthropic (Claude), Google (Gemini), Azure OpenAI Service, AWS Bedrock, Cohere, Mistral, and self-hosted open-source models (Llama, Mixtral, Falcon) via inference servers. Monitoring is implemented at the API gateway or proxy layer, independent of the model provider.
What is prompt injection and why does it matter?
Prompt injection is an attack where a malicious actor embeds adversarial instructions in user input or retrieved content to manipulate LLM behavior — directing the model to ignore system instructions, exfiltrate data, or produce harmful outputs. It is the dominant attack vector against deployed LLMs and cannot be fully prevented by model-side safety filters alone. Defense requires monitoring at the inference boundary.
How does this integrate with our existing SIEM?
LLM security events can be delivered to your existing SIEM via syslog, CEF, or API integration in addition to or instead of routing through Armorstack SENTRY SOC. Integration with Splunk, Microsoft Sentinel, IBM QRadar, Elastic SIEM, and major platforms is supported.
Is this covered under a SOC 2 or HIPAA program?
Yes. Monthly LLM security monitoring reports provide evidence of continuous monitoring controls required under SOC 2 CC7 (System Operations) and HIPAA Security Rule §164.312(b) (Audit Controls). The program is designed to satisfy auditor and examiner evidence requests out of the box.
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Ready to Engage LLM Security Observability?
Every VERITY AI engagement begins with a scoping call and a written proposal. No commitments until scope, deliverables, and pricing are agreed upon.Request an Engagement Proposal