OpsMx Delivery Shield embeds Garak’s adversarial testing engine to continuously probe, monitor, and guard live AI and LLM workloads against jailbreaks, data leaks, and policy violations.
Dynamic & Runtime AI Security Key Features
Real-time adversarial fuzzing
Garak auto-generates attack prompts and payloads against every model endpoint.
Policy-aware guardrails
Blocks or sanitizes responses that violate security, privacy, or compliance rules.
Context-graph correlation
Links runtime findings to the underlying code, model version, and data lineage for root-cause clarity.
Drift & jailbreak detection
Alerts on unexpected behavior changes or successful exploit attempts.
Automated remediation playbooks
Quarantine, rate-limit, or roll back compromised models with one click.
Seamless CI/CD & runtime integration
Works in staging tests and production inference without code changes.
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Dynamic & Runtime AI Security Key Benefits
Blocks prompt attacks in real time
Stops jailbreaks and adversarial inputs before they can leak data or violate trust.
Accelerates incident response
Provides immediate alerts with full context—reducing time to triage, root-cause, and fix.
Ensures continuous compliance
Enforces security and privacy rules automatically, even as regulations evolve.
Eliminates manual red-teaming cycles
Detects exploits and anomalies proactively—without needing constant human testing.
Unifies runtime and CI/CD visibility
Correlates live threats with model versions, pipelines, and code in a single dashboard.
Resources for Dynamic & Run-time AI Security
Datasheet: Dynamic & Run-time AI
Download NowBlog: Dynamic & Run-time AI Security by OpsMx
Read NowCapabilities of OpsMx in Securing AI/ML Models
OpsMx Delivery Shield safeguards LLMs, models, and AI agents across the pipeline with continuous scanning and policy enforcement. It detects risks early and blocks unsafe deployments before reaching production.
AI Discovery & Shadow AI Detection
Automatically detects every model, dataset, and prompt across source, registry, and runtime, building complete MBOM/PBOM/DBOM lineage.
AI Security Posture Management (AISPM)
Visualize and assess the risk of all AI assets. Gain contextual insights from model metadata, usage, and lineage.
Supports AI Red Teaming
Simulates adversarial prompts and jailbreak attempts to validate model robustness and uncover potential exposures.
Runtime Defense
Detects and prevents misuse at runtime using dynamic policies, behavior analysis, and LLM firewalls.
Agentic AI Security
Support proactive, reactive, and detective security controls and policies to safeguard agentic AI systems, LLMs, and ML models.
Automated Remediation
Respond to violations with AI-assisted remediation, using risk-informed decisions and GenAI-generated runbooks.