Pen-Testing AI Agents: What a CISO should ask for — and how to read the report
PRISM Observe — AI Runtime Security & Observability
Monitor Large Language Model (LLM) applications in production and gain real-time visibility into model behavior, security risks, and performance issues. Capture every interaction and know not just that an attack was attempted, but whether the model resisted or succumbed.
Why it matters
AI systems in production create new blind spots that traditional monitoring tools cannot detect, leading to gaps in security, performance, and compliance.
No Post-Deployment Monitoring
Teams treat pre-deployment testing as the finish line. AI behavior changes in production — and most teams learn about failures from user complaints, days or weeks later. A fraud or credit model can silently drift, or quietly succumb to a prompt injection, with no signal at all.
General APM / Infrastructure Monitoring
General APM 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.
Log Aggregation / SIEM Without AI-Native Analysis
Piping model logs into SIEM. Raw prompt/response logs do not surface adversarial patterns, reasoning drift, or agent scope violations without AI- native analysis.
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.
Our Approach
PRISM Observe applies a continuous, multi-layered 4-step AI observability framework to keep AI systems secure, performant, and compliant in production
Observe
Five pillars — security attack detection, sensitive data monitoring (PII/PHI/PCI), performance, model drift, agent drift baseline — plus a dedicated agentic layer. Captures every interaction via SDK, a shareable endpoint for third-party apps, or a no-code gateway (OpenTelemetry) — so even hosted and vendor AI is monitored.
Detect
Runtime MCP detection, dynamic agentic discovery, and advanced agentic drift detection. Confirms whether a detected attack was resisted or succumbed to, and applies per-use-case fine-tuned drift models — not a blunt global threshold — with hallucination flagging.
Alert & Trace
Every finding raises a risk-scored alert, reviewed in the UI, with the full agent decision trace — every tool and MCP call — so a wrong action can be reconstructed and explained.
Feed the Loop
Production signals — drift, cost, hallucination and attack incidents — flow into Govern to keep governance live, while newly seen AI systems surface back into Discover and telemetry feeds your existing SIEM/SOC.
Service Offerings
PRISM Observe operates across five pillars plus a dedicated agentic capability layer to detect, alert, and trace AI-specific threats in production.
Security attack detection, sensitive data monitoring, performance monitoring, model drift detection, and agent drift detection baseline. Includes attack detection with succumb/resist confirmation, per-use-case fine-tuned drift models with hallucination flagging, and cardholder-data (PAN) detection in both prompts and responses.
A distinct agentic observability layer for autonomous agent and multi-agent deployments that includes shadow AI detection at runtime, MCP connection detection, dynamic agentic discovery, and advanced agentic drift detection.
Identifies new Model Context Protocol server connections as agents establish them in production. Trust chains, data access scope, and inter-agent MCP dependencies are catalogued continuously.
The performance engine tracks operational metrics of agents and multi-agent systems. The security engine tracks adversarial signatures, permission abuse, scope violations, suspicious MCP connections, and drift from approved behavior.
Monitors autonomous agent behavior for deviation from approved baselines: skill usage pattern changes, decision paths that differ from established patterns, scope creep beyond approved use cases, inter-agent communication deviations.
Captures every interaction via SDK, a shareable endpoint for third-party apps, or a no-code AI gateway — routed over OpenTelemetry — so even hosted and vendor AI apps that can’t take custom logging are finally monitored.
Attributes latency and spend down from application to model to specific use case, turning an opaque, growing AI bill into something you can manage.
Production drift, cost, hallucination and attack incidents flow into PRISM Govern to keep governance live; newly seen AI systems surface back into PRISM Discover.
BENEFITS
PRISM Observe helps you run AI systems with greater confidence, visibility, and control.
Collapse the gap between compromise and detection from weeks to minutes.
Real-time adversarial attack detection, shadow AI surfacing, and agentic drift alerting.
Monitor agentic systems with dual-engine precision.
Performance engine tracks operational health. Security engine tracks threat signatures. Neither drowns the other.
Know whether an attack actually worked, not just that it was tried.
Succumb/resist confirmation on every detected attack turns a noisy alert into a triage-worthy incident.
Continuous runtime evidence for regulatory compliance.
ISO 42001, EU AI Act post-market monitoring, NIST AI RMF continuous monitoring,
CBUAE/SAMA ongoing monitoring expectations, plus PCI DSS 4.0.1 and the PCI SSC principle that AI actions be logged, monitored and traceable to an accountable human (RBI and MAS ongoing monitoring).
Detect new MCP connections at runtime.
Catalogues MCP trust chains as they form and expand dynamically in agentic systems.
Catch silent drift in your decisioning models.
Per-use-case fine-tuned drift models — a fraud model tuned differently from a support copilot — flag silent behavioural change and likely hallucination before it becomes a business problem.
Monitor even hosted and vendor AI.
OpenTelemetry capture via SDK, push endpoint, or no-code gateway means hosted GenAI and vendor apps you cannot instrument are finally in view.
Governance that stays live.
Production drift, cost, hallucination and attack incidents flow into PRISM Govern automatically, and shadow AI surfaces back into PRISM Discover, so governance reflects live reality, not a quarterly
snapshot.
Why Organizations Choose PRISM Observe from SISA
PRISM Observe connects AI observability with threat detection, governance evidence, and forensic response for regulated enterprises.
AI-Specific Observability Design
Built specifically for AI systems, not adapted from traditional monitoring tools.
Content-Aware Threat Detection
Detects risks within prompts and responses, not just system-level signals.
Integrated Security and Performance View
Combines threat detection and performance monitoring into a unified platform.
Enterprise-Grade Architecture
Designed for scalability, multi-tenancy, and high-volume AI workloads.
Configurable and Flexible Framework
Supports environment-specific thresholds, policies, and operational tuning.
Aligned with AI Governance and Risk Needs
Supports compliance, audit, and governance requirements for AI deployments.
Coverage You Can Prove
Because PRISM Observe is wired to PRISM Discover, you can confirm every discovered AI system is actually being monitored — one pane of glass, no blind spots.
Forensics DNA and a Managed-SOC Ecosystem
AI incidents can be escalated to SISA’s DFIR team and investigated the way a payment breach is, and PRISM Observe integrates with SISA’s ProACT managed-detection ecosystem and Agentic SOC — backed by 18+ years of PCI QSA heritage.
The Closed Loop No Point Tool Replicates
PRISM Observe does not just watch: it feeds production reality into PRISM Govern and shadow AI into PRISM Discover, so governance is evidenced by what AI actually did, mapped to PCI DSS 4.0.1, SAMA, CBUAE, RBI and MAS.
Runtime Security for Agentic AI.
