The industry has rushed to operationalize GenAI by leaning on what it knows best: pipeline visibility. While engineering teams have successfully extended tools like Datadog, Splunk, or LangSmith (by LangChain) to cover new workflows, a dangerous contradiction remains: the pipeline is robust, but the specific GenAI attack surface is under-instrumented.
You have excellent operational visibility, including latency spikes and cost balloons, but monitoring isn’t security.
You wouldn’t deploy a web app without a DAST scan, yet many deploy GenAI agents with only “smoke alarms” (observability) and no “fire drills” (validation). Observability sees what happened but validation predicts what will break.
To truly secure the GenAI enterprise, CISOs must pair monitoring with rigorous end-to-end validation.
1. The “Active Stack” Validation
Vulnerabilities thrive in integration seams, not just model weights. Static benchmarks treat the LLM as an isolated calculator, missing “stateful” flaws where conversation history or retrieved docs alter behavior.
We validate the Active Stack by interacting via standard SDKs to test the full lifecycle:
- App Logic: Can indirect context (e.g., invisible text in a PDF resume) bypass the prompt?
- Model: Does it leak PII despite instructions?
- Vector DB: Is the retrieval layer corruptible?
This ensures your implementation of the model is secure, not just the model itself.
2. Securing the Retrieval (RAG) Layer
RAG introduces a massive attack surface: the Vector Database. Observability tools often treat Vector DBs (Milvus, Pinecone) as black boxes, seeing retrieval but not data integrity.
- The Threat: “RAG Poisoning,” where adversaries update text without modifying embeddings, tricks the AI into retrieving malicious context that looks benign to keyword filters.
- The Fix: We inject poisoned documents and mismatched vector/text pairs into the pipeline to verify your system can distinguish authoritative data from malicious noise.
3. Stress-Testing the Guardrails
Compliance demands guardrails exist. Security demands they work.
We automate adversarial stress-testing, including standard “DAN” attacks and sophisticated obfuscation, to empirically assess failure thresholds. We transform “we have guardrails” from a hopeful assumption into a verified metric.
4. Sovereignty: Validation Within Your Perimeter
For Finance, Healthcare, and Defense, data sovereignty is a requirement, not a feature.
Unlike cloud validators that require shipping logs or keys to a third party, our architecture supports Air-Gapped and Local Deployment. You run our regression suites entirely within your VPC. No data leaves your perimeter.
Conclusion: Trust, but Verify
Observability is essential for Day 2 operations. Validation is critical for Day 1 deployment.
Your observability tools are watching the building. Let us check the locks.
Attending AVAR 2025? Visit the SecureIQ Lab booth for a demo.
