AI Security, Monitoring & Optimization
Keep AI systems reliable after they
go live.
Test and monitor production AI systems for prompt injection, data leakage, hallucinations, unsafe actions, cost, latency, and quality — then improve them over time.
Overview
What we deliver.
AI systems need ongoing visibility. We test AI before launch and monitor production behavior after launch — tracking quality and cost, identifying failure modes, and improving the system over time. Model behavior changes with real users, new data, new prompts, and edge cases, so we help teams detect issues early and maintain trust.
Why PROSYS
Outcome-Focused
Every deliverable tied to a business outcome
Enterprise Security
Built to SOC 2 control objectives, encrypted by default
Predictable Delivery
Iterative releases with transparent reporting
Production-Grade
Tested, documented, deployed to production
Methodology
How we deliver.
Security Testing
Prompt injection, data leakage, and agent action abuse testing before users see the system.
Quality Review
Hallucination and output-quality evaluation against real cases.
Guardrails
Implement guardrails and fallback behavior against the failure modes found.
Monitoring
Track cost, latency, failures, and escalation rates in production.
Optimization
Improve prompts, retrieval, and model routing to control cost and raise quality.
Reporting
Monthly improvement reporting with a prioritized enhancement roadmap.
Technology Stack
Our AI Security, Monitoring & Optimization toolkit
Hand-picked tools and frameworks we use to ship production-grade ai security, monitoring & optimization projects.
Business Outcomes
What you get with every engagement.
Beyond the deliverable — measurable business impact, clean handoffs, and a partnership built to scale with you.
Case Study
AI Red-Team Review and Production Monitoring
The Challenge
A team was about to launch a live AI system with no testing for prompt injection or data leakage and no visibility into cost or quality after launch.
The Result
Ran a red-team review, implemented guardrails, and stood up cost, latency, failure, and quality monitoring with a monthly optimization plan to keep the system reliable.
FAQ
Common questions.
What do you test for before launch?
Prompt injection, data leakage, agent action abuse, hallucination risk, tool misuse, and guardrail weaknesses — with remediation recommendations and implementation support.
What do you monitor after launch?
Quality, cost, latency, failures, user feedback, escalation rates, and retrieval performance, with a monthly improvement roadmap.
Can you reduce our LLM costs?
Often, yes — through model routing, caching, prompt and retrieval optimization, and fallback design, while keeping quality visible.
Next Steps
Ready to start your ai security, monitoring & optimization project?
Let's discuss your requirements and build a detailed proposal.