Why enterprises who see and secure AI at runtime will own the next innovation frontier.
Agentic, MCP and LLM adoption are on a steady rise within enterprises and with solid reasoning: they promise transformative ROI.
Steady experimentation is being pursued over the last few quarters in the hopes of capturing this value, with Gartner reporting a 750% surge in AI-agent inquiries between Q2 and Q4 2024.
Despite a majority of enterprises having at least 1 AI project underway, about 32% of enterprises exploring AI agents stall at pilot stage and are never deployed in production.
The reason is simple: production is where stakes are highest. Real identities, sensitive data, and business-critical workflows collide.
So while everyone may acknowledge strong motivations to scale AI, most cannot prove the security and control that leadership and compliance demands.
A lack of security has thus become the pedal-brakes on what could be the biggest differentiator of this decade. Those who secure AI quickly enough to deploy it at scale will capture customers, market share and competitive edge that laggards will leave behind.
Runtime AI Visibility is the foremost and the most critical step of the process and this blog breaks down the what, why and how around it.
What is Runtime AI Visibility
Protecting AI systems starts with defining what an AI application really is and why securing them requires a different approach than past architectures.
In legacy monolithic applications, the entire system was visible upfront. All the logic, data, and interfaces were bundled into a single deployable unit. That visibility made it easier to understand the full attack surface, harden perimeters, and enforce compliance before the system ever went live.
AI applications are the opposite. Most of their moving parts only materialize at runtime. Agents call tools on demand, LLMs generate new paths on the fly, RAG pipelines fetch data dynamically, and APIs are invoked contextually. Many of these elements don’t exist to be tested or reviewed until they are actually executed in production. The result is a system whose boundaries shift with every query and whose attack surface is constantly recomposed.
Which means that unlike legacy systems where the full attack surface could be mapped ahead of time, AI applications can only be understood in motion. Security teams must observe them holistically as they run, because the real risk emerges from how these components interact at runtime, in addition to individual components.
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Core elements of an AI Application:
- AI Agents: They plan, decide, and chain tools at runtime. This enables flexible workflows but also creates opaque, emergent behaviors that can’t be pretested.
- MCP Servers: Gateways that expose enterprise systems to AI. They make agents useful but also introduce new supply-chain and permission surfaces.
- APIs: The channels where sensitive data flows and business actions execute. They are the operational endpoints of AI workflows and a primary compliance and security concern.
- LLMs: The inference engines that interpret prompts and generate responses. Probabilistic by nature, they are difficult to audit without runtime logs.
- RAG Pipelines: Retrieval-Augmented Generation injects enterprise knowledge into AI. It makes AI contextually valuable but creates a shadow data plane of retrieved documents, provenance trails, and potential injection vectors.
AI applications don’t just increase breach risk by multiplying the number of potential entry points, they also amplify the damage each breach can cause.
A single compromised connector in a fintech credit engine, for instance, doesn’t just expose one record. It can enable bulk scraping of thousands of identities, poison downstream decision models, and trigger fraudulent approvals across the entire system. What was once an isolated incident in a monolith becomes systemic fallout in AI applications.
Without visibility into what exists in production and what is happening across this runtime mesh, enterprises have no way to govern or protect it or the data flowing through it.
Why Legacy Security Fails AI-native Applications
When enterprises shifted from monoliths to microservices, the way applications communicated changed dramatically. A single user request no longer touched one codebase; it fanned out across dozens of internal service calls. East–west traffic exploded, creating a dense mesh of machine-to-machine interactions inside applications.
But security tools lagged. Born at the edge, they were designed to inspect north–south traffic, meaning user-to-application flows. That worked when the critical interaction was a login, a web request, or a transaction at the perimeter.
It fails now when the bulk of activity moves inside the system. Containers spun up and down, services talked to each other constantly, and most of the real risk lived in conversations that never touched the edge.
AI applications push this reality into hyperdrive.
It is no longer just APIs talking to APIs. Agents, LLMs, MCP servers, RAG pipelines, and partner APIs are all calling one another at runtime, often spinning up pathways that did not exist moments before.
The critical attack surface has shifted into machine-to-machine workflows, yet most security tools still watch for human-to-machine events. The result is predictable: they miss seeing most of the AI attack surface, let alone securing it.
Legacy vendors have tried to adapt by bolting on limited machine-to-machine visibility. But their architectures were never built for it. SDKs and library monitoring provide partial glimpses, but coverage is patchy, operational drag is heavy, and cloud costs skyrocket as teams drown in noisy alerts. Visibility that is added after the fact is fragile, expensive, and fundamentally incomplete.
And the gap grows even wider in multi-agent workflows. When agents collaborate, their privileges and capabilities combine. One agent may read sensitive data, another may act on it, and together they can bypass intended boundaries.
Edge logs cannot trace intent, delegation, or privilege aggregation across agent chains. Without runtime visibility built in, these cascading interactions remain invisible until they cause systemic failure.
How Missing Visibility Breaks Every Layer of AI Application Security
When runtime visibility is absent, every other security layer built on top of it inherits the same blindness. Detection becomes guesswork, protection over- or under-shoots, debugging stalls without causality, remediation turns into blind triage, and testing provides a false sense of safety.
For already outnumbered and overstressed security teams, the absence of visibility doesn’t just weaken defenses, it multiplies the work, the frustration, and the burnout.
Detection drowns teams in noise and misses what matters
When detection fails, it doesn’t just delay response, it lets risks run live in production. Teams face floods of false positives while the real exploits hide in east–west agent calls they can’t see. Without runtime visibility, AI’s non-deterministic behavior overwhelms analysts with noise, and the breaches that matter most slip by undetected.
Protection blocks progress yet fails to stop real risks
Protection is supposed to enforce guardrails, yet without visibility it often does the opposite. Overly strict rules block legitimate AI workflows and slow adoption, while blind spots in MCP schemas or delegated tokens leave destructive actions unmonitored. The same lack of visibility that blinds detection also strips protection of precision, leaving enterprises trapped between over-blocking innovation and under-securing critical systems.
Debugging without visibility leaves teams chasing shadows
How do you debug what you cannot see? In AI systems, failures cascade through agent plans, tool calls, and data retrievals that don’t appear in conventional logs. Without runtime traces, engineers cannot reconstruct causality or trace identity drift across hops. The result is prolonged outages, wasted engineering cycles, and unresolved risks that linger in production long after the initial incident.
Remediation turns into endless triage instead of resolution
With runtime visibility, remediation can be precise: vulnerabilities are located, validated, and patched with context. Without it, teams chase false positives, patch the wrong surfaces, and miss lateral exploit paths hidden in multi-agent workflows. Developers are dragged into endless triage cycles, compliance deadlines slip, and security debt compounds. Visibility is the difference between closing incidents quickly and watching them spiral into systemic failures.
Security testing creates false confidence & exposed attack vectors
Pre-production testing creates a dangerous illusion of safety when runtime visibility is absent. Traditional scanners validate deterministic paths, but AI applications behave differently each run. Prompt injections, embedding abuse, and privilege escalation chains bypass tests entirely, surfacing only in production. Enterprises believe they are covered, but the blind spots left by missing runtime insights mean testing becomes a checkbox exercise, not real assurance.
What Runtime AI Visibility Must Encompass
Effective visibility in AI applications cannot stop at surface metrics. It has to account for the full runtime reality: what assets exist, how data moves, which identities act, how systems perform, and what capabilities are exposed. Without this depth, security teams remain blind to the risks that emerge inside agentic workflows, and every downstream function inherits that blindness.
1. Asset discovery across the AI estate
Security teams first need to know what exists. That means surfacing every active agent, MCP server, LLM, and AI app, whether internal or third-party, along with details such as model or server name, usage count, activity timelines, and parent app attribution. This creates the AI equivalent of a bill of materials, catching shadow AI before it spreads and anchoring governance in reality rather than assumption.
2. Data-flow visibility
Equally important is understanding how information moves. Prompts, responses, tokens, and downstream API calls need to be traced across agents, MCP servers, and LLMs, with volume, speed, error rates, and sensitivity markers. These traces make it possible to validate appropriate use, expose shadow paths or untracked destinations, and produce the audit-grade evidence that compliance leaders demand.
3. Identity and access mapping
AI systems blur the line between authorization and execution. An agent may act with delegated tokens, non-human identities can swap privileges mid-session, and without visibility, accountability disappears. Mapping who authorized versus who executed, and capturing token scopes and privilege states, restores the ability to enforce guardrails and spot over-permissive access before it is misused.
4. Operational health and cost
Security is not just about blocking exploits; it is also about ensuring systems do not collapse under their own weight. Visibility into model latency, error rates, token consumption, agent loops, and retries helps prevent outages, rein in runaway spend, and establish the reliability metrics needed to scale AI with confidence.
5. Capability and destination mapping
Finally, AI applications need to be made governable by mapping what they can do and where they can send data. MCP servers expose tool catalogs and destinations, while agents show patterns of chain depth and model features in use. Capturing this turns opaque workflows into ones security teams can reason about, enforce policies on, and build precise allowlists around.
With assets, flows, identities, health, and capabilities visible, every downstream security function becomes stronger: detection sharpens, protection gains accuracy, debugging uncovers causality, remediation targets the right vulnerabilities, and testing models real-world risk. For teams already stretched thin, this is the difference between firefighting and governing AI at scale.
How Levo Delivers Runtime AI Visibility
Levo’s Runtime AI Security module is built to give enterprises the clarity they need to take AI from pilot to production with speed and confidence. Each capability captures the runtime truth of AI applications so security teams can govern effectively without slowing adoption.
- In-house asset discovery
Maps every internal AI component—agents, MCP servers, and homegrown LLM apps—with usage counts, activity timelines, and parent attribution. This creates an always-current inventory that eliminates shadow AI and builds the foundation for compliant scale. - Third-party asset discovery
Continuously identifies external LLMs, APIs, and SaaS AI integrations in use across the enterprise. Security teams can validate vendors and enforce policy early, reducing the compliance risks that stall rollouts. - End-to-end data flow tracing
Tracks prompts, responses, tokens, and downstream API calls across the runtime mesh. These traces provide audit-grade evidence for regulatory reviews and help security teams uncover shadow paths before they become systemic failures. - Identity and access clarity
Maps who authorized versus who executed, including non-human identities and delegated tokens. This restores accountability to machine-to-machine traffic and closes the gaps that create audit friction. - Operational health and cost metrics
Surfaces model latency, error rates, agent loops, and token spend in real time. Leaders gain assurance that AI systems can scale without spiraling into instability or runaway costs. - Capability and destination mapping
Exposes tool catalogs, destinations, and model features in use. This transparency allows precise policies and controls, replacing guesswork with enforceable governance.
How Levo Provides End-to-End Runtime AI Visibility
Levo delivers this visibility continuously and without friction. Kernel-level, eBPF-powered sensors capture runtime activity without SDKs or code changes, with negligible overhead. Processing happens locally, and only scrubbed metadata leaves the environment, ensuring compliance with strict residency and regulatory requirements. Unlike bolted-on solutions, Levo’s approach is built in from the start, making runtime visibility scalable and sustainable for enterprise adoption.
Beyond Runtime AI Visibility , Complete AI Security
Levo extends beyond visibility to cover the full spectrum of AI security. Its breadth spans MCP servers, LLM applications, AI agents, and APIs, while its depth runs from shift-left capabilities like discovery and security testing to runtime functions such as monitoring, detection, and protection. By unifying these layers, Levo enables enterprises to scale AI safely, remain compliant, and deliver business value without delay.
Book a demo through this link to see it live in action!
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