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AI Runtime Security & Observability
Your AI Passed Testing. Is It Still Safe?
Real-time adversarial detection, shadow AI and MCP surfacing, agentic drift alerting, and an AI kill switch — across five observability pillars and a dedicated agentic layer with separate performance and security engines.
5
Pillars + Agentic Layer
2
Dedicated Engines (Perf + Security)
4
Agentic Detections
< 5s
Kill-Switch Response
The Challenge
AI Under Attack Looks Normal to APM.
Traditional APM and SIEM tools see latency and error rates. They cannot see prompt injection, reasoning chain deviation, agent skill-usage changes, shadow AI at runtime, MCP connections, or agent drift. SIEM sees text — it does not understand model behavior.
Pre-Deployment Testing Is a Snapshot
"We tested before launch" is not a runtime security posture. AI behavior changes in production — and most teams learn about failures from user complaints, days or weeks later.
Shadow AI Proliferates Between Audits
New agents, unauthorized model deployments, and ad-hoc AI experiments appear at runtime. Inventory audits run quarterly; the attack surface changes daily.
Agentic Drift Cascades Silently
In multi-agent systems, one agent's scope creep triggers system-wide deviation. Skill-usage changes, decision-path shifts, and inter-agent communication deviations go unseen.
How It Works
Detect, Contain, Rollback
Step 01
Observe
Five pillars — security attack detection, sensitive data monitoring (PII/PHI/PCI), performance, model drift, agent drift baseline — plus a dedicated agentic layer.
Step 02
Detect
Runtime shadow AI detection, runtime MCP detection, dynamic agentic discovery, and advanced agentic drift detection — capabilities absent from APM and ML tracing tools.
Step 03
Contain
AI Kill Switch instantly shuts down a model or agent when a critical threshold is breached. Automated or manual trigger — immediate deactivation.
Step 04
Rollback
Pre-defined incident playbooks. Instant revert to a previous known-good model version. Supply-chain incident playbooks for upstream provider failures.
Key Benefits
Why Teams Choose PrismObserve
Core
Five-Pillar AI-Native Observability
Security attacks, sensitive data leakage, performance, model drift, and agent drift baseline — depth no APM, SIEM, or ML tracing platform reaches.
Dedicated Agentic Capability Layer
Runtime shadow AI detection, MCP connection detection, dynamic agentic discovery, agentic drift detection — capabilities absent from APM and vendor dashboards.
Dual-Engine Architecture
Separate performance engine (latency per agent hop, tool cost, throughput, loop detection) and security engine (adversarial signatures, permission abuse, scope violations). Neither drowns the other.
AI Kill Switch + Rollback
Instant deactivation when critical thresholds breach. Pre-defined incident playbooks. Instant revert to a previous known-good model. Not a soft alert.
Sensitive Data Leakage Caught Live
PII / PHI / PCI monitoring on inputs and outputs — caught before it becomes a reportable breach.
Continuous Compliance Evidence
Runtime evidence for ISO 42001, EU AI Act Art. 72 post-market monitoring, NIST AI RMF, CBUAE and SAMA ongoing monitoring.
Built For Your Team
Designed for Every Stakeholder
CISO
Collapse the compromise-to-detection window from weeks to minutes. Continuous runtime evidence for regulators.
Security Architect
Runtime shadow AI, MCP detection, and agentic drift — surfaced before they cascade.
AI / ML Platform Lead
Dual-engine monitoring across agents and multi-agent systems. Performance and security tuned independently.
SecOps / DevOps
Kill switch, incident playbooks, rollback, supply-chain IR — wired into existing alerting and response stacks.
Frameworks & Integrations
ISO 42001EU AI Act Art. 72NIST AI RMFCBUAESAMAOpenTelemetrySlackPagerDutySIEMMCP ServersAutonomous AgentsMulti-Agent Systems