Industry News - October 17, 2025: Levo.ai unifies AI security and compliance from development to runtime
Levo.ai has launched a Unified AI Security Platform that brings security and compliance under a single control plane, from build to production. For teams stuck in pilot limbo due to risk and audit gaps, this approach targets the core blockers: fragmented visibility, uneven governance, and weak runtime controls.
Why AI pilots stall
Traditional security tools weren't built for agent chains, MCP servers coordinating tools, vector stores feeding LLMs, or dense API flows. Securing each piece in isolation multiplies blind spots and policy drift. Boards see the risk, so scale stalls and ROI sits on the shelf.
What Levo.ai unifies under one umbrella
The platform maps and governs applications, agents, MCP servers, APIs, vector stores, and LLMs from one place. It focuses on runtime visibility, policy enforcement, and active defense-so teams can ship without trading speed for risk.
- Runtime AI visibility: Automated discovery for every AI component and data path.
- AI monitoring & governance: Runtime policy that protects customer data and enforces guardrails.
- AI threat detection: Finds sensitive data exposure and over-permissioned agents before incidents land.
- AI threat protection: Real-time blocking that keeps customers and partners online, preserving revenue.
- AI red teaming: Detects prompt injection, data poisoning, and collusion attempts; provides guided remediation.
Under the hood
Levo.ai extends eBPF-based sensors to discover LLM traffic and the broader AI stack, then centralizes control in one plane. Policies follow components across environments, so governance holds at runtime and not just in design docs. "Our eBPF sensors had already begun discovering LLM traffic, and customers asked us to extend that visibility across the entire AI stack," said CEO Buchi Reddy.
Why this matters for engineering, security, and data teams
- One source of truth: Inventory, flows, and policy in a single place-less glue code, fewer exceptions.
- Runtime-first governance: Enforce least privilege for agents and tools; stop risky data egress by default.
- Fewer handoffs: Shared controls across Dev, Sec, and Data reduce friction and audit churn.
- Production confidence: Block attacks without breaking user paths or partner integrations.
Practical rollout plan
- Connect sensors in pre-prod and prod to auto-discover agents, MCP servers, tools, vector stores, and LLM endpoints.
- Map data boundaries (PII, PHI, PCI) and define allow/deny flows per application and agent chain.
- Enforce least privilege for tool access and external calls; right-size permissions for each agent.
- Run red team scenarios for prompt injection, exfiltration, and tool collusion; fix findings before scale-up.
- Integrate alerts and telemetry with your SIEM/SOAR and compliance evidence store.
For additional context on common AI risks and controls, see the OWASP Top 10 for LLM Applications (OWASP) and the NIST AI Risk Management Framework (NIST).
Outcome
Levo.ai's platform aims to move AI from pilot to production by automating security and compliance end-to-end. Teams ship faster, keep sensitive data protected, and turn AI gains into durable profit instead of overhead.
If your team needs to upskill on AI security, governance, and deployment practices, explore current certification paths here: Complete AI Training - Certifications.
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