Most AI security tools solve one piece of the problem. Valo Security unifies AI, SaaS, and agent security in one open-source platform with deterministic policy enforcement, AI-native risk detection, framework mapping (OWASP LLM Top 10 and MITRE ATLAS), and executive-ready reporting. Valo Security is built from the professional perspective for security teams that need visibility, governance, and enforcement, not just alerts.
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We created Valo Security because AI adoption is moving faster than enterprise security. Organizations are deploying LLMs, AI-powered SaaS, and autonomous agents without the visibility or controls needed to understand what those systems are doing, what data they're accessing, or how they're being manipulated. Traditional security tools weren't designed for this new attack surface.
Valo Security was built to change that. Our mission is to help organizations adopt AI with confidence through deterministic visibility, governance, and enforcement. By making enterprise-grade AI security available to the community, we hope to accelerate responsible AI innovation, establish practical security standards, and help organizations move from AI experimentation to trusted, secure deployment at scale.
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Love the open-source angle and the focus on real enforcement over more alerts. One thing that would help teams adopt it faster is a built-in CI pipeline template that fails builds when policy violations are detected, so devs catch issues before deploy instead of after the fact. Would save a ton of back-and-forth between security and engineering.
honestly the unified approach is what stood out to me, since most tools only handle one layer. one thing that would be really useful though is some kind of community-driven rule packs or shared policy templates, so security teams can basically swap configs for things like prompt injection rules or shadow AI detections instead of building everything from scratch. would save a ton of time
Would love to see a built-in diff view that shows exactly which AI or SaaS config changed and which policy rule caught it, so during incidents I don't have to dig through logs to understand the violation context.
The unified view across AI, SaaS, and agent security is genuinely useful instead of juggling separate dashboards. The OWASP and MITRE ATLAS mapping right out of the box saved me a lot of setup time.
Love the open-source angle and the focus on real enforcement over more alerts. One thing that would help teams adopt it faster is a built-in CI pipeline template that fails builds when policy violations are detected, so devs catch issues before deploy instead of after the fact. Would save a ton of back-and-forth between security and engineering.
@hatunnevruz Brilliant.
honestly the unified approach is what stood out to me, since most tools only handle one layer. one thing that would be really useful though is some kind of community-driven rule packs or shared policy templates, so security teams can basically swap configs for things like prompt injection rules or shadow AI detections instead of building everything from scratch. would save a ton of time
@glen1334476 Agree completely
Would love to see a built-in diff view that shows exactly which AI or SaaS config changed and which policy rule caught it, so during incidents I don't have to dig through logs to understand the violation context.
@hamzasefil3idj Exactly! Thank you for the feedback!
The unified view across AI, SaaS, and agent security is genuinely useful instead of juggling separate dashboards. The OWASP and MITRE ATLAS mapping right out of the box saved me a lot of setup time.