What is AI Visibility: Examples, Tools, & Best Practices

AI systems are rapidly becoming the operational core of modern enterprises, powering customer interactions, internal workflows, and autonomous decision making. Their adoption curve is accelerating sharply. Recent industry surveys show that 79% of senior executives report active adoption of AI agents, and 51% of organizations have already deployed agentic systems, with another 35% planning deployment within the next two years. Yet visibility into how these systems actually behave in production remains dangerously limited.

This visibility gap creates material business, security, and governance risk. 37% of enterprises cite security and compliance as the number one barrier to AI adoption, and 32% of AI initiatives stall at proof of concept due to the inability to demonstrate safe operation. As a result, integration pipelines freeze, ROI remains trapped in pilots, and competitors with secure AI foundations scale faster and capture market share. In fast moving industries such as finance, retail, and telecom, safe AI adoption is no longer optional, it directly determines competitive relevance.

The consequences extend beyond stalled adoption. 82% of enterprises report AI agents accessing sensitive data, with 58% saying this occurs daily, and the average AI related breach now costs $4.8M, exceeding traditional breach costs. Without runtime visibility, these incidents can remain undetected for 200+ days, compounding regulatory penalties and customer churn. At the same time, tightening regulations such as the EU AI Act, India’s DPDP Act, and healthcare extensions are driving up compliance overhead, often wiping out the very ROI AI was meant to deliver.

For CISOs and executive leadership, this is no longer just a technology concern. It is a strategic risk. Lack of AI visibility leads to stalled integrations, rising manual oversight costs, eroding customer trust, and ultimately board level irrelevance. Effective AI visibility, understanding which agents exist, what data they access, how they make decisions, and where they can fail, is now the prerequisite for secure adoption, regulatory confidence, and sustained competitive advantage. Without it, scaling AI safely becomes impossible.

What is AI Visibility?

AI Visibility is the unified ability to understand what AI systems exist, how they behave, and what data, tools, and decisions they touch across your organization. 

It brings together three foundational pillars: 

• a complete inventory of AI agents and models (what AI exists), 
• behavioral and decision transparency (what each agent does and why), and
• sensitive data and permission awareness (what information, tools, or actions an AI system can access or trigger). 

Together, these pillars create an authoritative, real time view of your AI estate, eliminating blind spots and replacing assumptions with evidence.

In large scale, agent driven environments, achieving AI visibility is both essential and difficult. Modern enterprises run dozens of models, autonomous agents, MCP servers, and LLM powered workflows across clouds, internal systems, and third party tools. These components evolve continuously, with prompts changing, tools added, permissions expanding, and agents interacting in unexpected ways. Without continuous discovery and runtime observation, organizations lose track of shadow agents, transitive trust chains, privilege aggregation, and high risk data flows that only surface in production.

Effective AI visibility observes AI systems at runtime, correlating agent actions, tool usage, data access, and outcomes in real time. The result is a living, always current blueprint of how AI behaves across your business, not just how it was designed to behave. This foundation enables stronger security, faster incident response, confident compliance, and safe scaling of AI from isolated pilots to mission critical operations.

Pillars of AI Visibility

AI visibility rests on 3 fundamentals: knowing what exists, understanding how it behaves, and seeing what it accesses. Together, these pillars provide a real time, accurate view of your AI estate, enabling safe scale, governance, and control.

1. AI Inventory: Knowing What Exists. 

Continuously discover all AI assets, including LLMs, agents, MCP servers, prompts, fine tuned models, and third party AI integrations across dev and production. Rapid experimentation creates shadow and unmanaged agents; a live inventory removes blind spots and ensures ownership and accountability.

2. Runtime Behavior Visibility: Understanding What It Does. 

Capture how AI systems behave in production, including prompt execution, agent interactions, tool usage, and model outputs. This exposes hallucinations, unsafe actions, and deviations from expected behavior, which design time reviews and offline evaluations miss.

3. Data, Tool, and Access Visibility: Knowing What It Handles.

Track what data AI systems access, what tools they invoke, and what effective permissions they accumulate across agent chains. This prevents transitive trust leaks, privilege escalation, and sensitive data exposure, enabling least privilege enforcement and compliance by design.

Why is Runtime AI Visibility Important?

Runtime AI visibility is the control layer that keeps security, compliance, and engineering aligned as AI systems move from experimentation to production. Without it, organizations deploy powerful, autonomous systems without knowing how they behave, what they access, or where risk accumulates.

1. Preventing Breaches and Unsafe Agent Behavior: AI agents operate autonomously, chaining tools, models, and data sources in real time. Without runtime visibility, unsafe actions like hallucinated commands, unintended API calls, or excessive privilege use go unnoticed. These blind spots enable data exfiltration, destructive actions, and business logic abuse. If agent behavior is invisible in production, it cannot be controlled or corrected.

2. Reducing Sensitive Data Exposure and Compliance Risk: AI systems increasingly access PII, financial records, and regulated data. Regulations such as the EU AI Act, DPDP Act, HIPAA, and sector specific mandates require provable controls over how AI handles sensitive information. Yet over 80% of AI enterprises report agents accessing sensitive data, often daily. Runtime visibility is essential to track data access paths, enforce least privilege, and produce audit ready evidence.

3. Enabling Effective Governance and Policy Enforcement: Design time reviews and offline evaluations capture intent, not reality. In production, prompts evolve, tools change, and agents accumulate transitive trust. Without runtime visibility, governance policies remain theoretical. Continuous observation enables real enforcement of guardrails such as allowed tools, data boundaries, and usage policies across live agent workflows.

4. Accelerating Safe AI Adoption Without Friction: Lack of visibility forces organizations to slow or halt AI rollouts due to security uncertainty. This is why 37% of enterprises cite security as the top barrier to AI adoption. Runtime visibility removes this bottleneck by giving CISOs confidence and developers clear feedback, allowing teams to scale AI safely without manual reviews or stalled integrations.

Who Needs AI Visibility?

AI visibility is not limited to security teams. It is a cross functional capability required by every group responsible for building, operating, and governing AI systems in production.

• Engineering and AI Platform Teams: Engineering teams need visibility into how AI agents, models, and tools behave at runtime. It helps them understand agent decision paths, tool usage, failure modes, and dependency chains, reducing debugging time and preventing unsafe or inefficient workflows from reaching users.
• Security and CISO Teams: Security leaders rely on AI visibility to understand the real attack surface created by AI agents. Runtime insight exposes privilege aggregation, unsafe tool calls, data access violations, and transitive trust risks that are invisible in design reviews. Without this, security teams cannot enforce policy or respond to incidents in time.
• Compliance and Risk Teams: Compliance teams require provable evidence of how AI systems access, process, and share sensitive data. AI visibility provides continuous records needed for regulations such as the EU AI Act, DPDP Act, HIPAA, and internal risk frameworks, replacing manual audits with real time assurance.
• Product and Business Leaders: Product teams depend on AI visibility to ensure features behave predictably and safely in customer facing workflows. It enables faster iteration, controlled rollouts, and confidence that AI driven experiences will not damage trust, brand, or revenue.‍
• Enterprises Scaling AI Adoption: Organizations deploying multiple AI agents across customer support, finance, HR, and operations cannot rely on manual reviews or offline evaluations. As AI usage scales rapidly across teams and environments, continuous AI visibility becomes essential to maintain control, prevent risk accumulation, and enable safe, competitive adoption at scale.

Risks of Incomplete AI Discovery & Visibility

Incomplete AI discovery creates systemic blind spots that amplify risk across security, compliance, and operations. When organisations lack a unified, real time view of their AI systems, agents, and data flows, risks multiply quickly and often go unnoticed until impact occurs. 

Key risks include:

1. Hidden AI Agents and Uncontrolled Actions
   Undiscovered AI agents or embedded models can operate outside governance boundaries, invoking tools, APIs, or workflows without oversight. These hidden execution paths create unmonitored entry points for abuse, privilege escalation, or unintended system actions.
2. Unsafe Behaviour and Policy Drift
   AI systems evolve rapidly through prompt changes, model updates, and tool additions. Without continuous visibility, policies applied at design time drift in production. This leads to inconsistent behaviour, unsafe outputs, and violations of access or data handling rules that were never approved.
3. Delayed Detection of AI Driven Incidents
   You cannot detect misuse you cannot observe. Incomplete visibility prevents effective monitoring of agent decisions, tool calls, and data access patterns. As a result, prompt injection, data leakage, or abusive automation can persist undetected, increasing blast radius and recovery time.
4. Sensitive Data Exposure and Compliance Failures
   Regulations increasingly require proof of how AI systems access and process sensitive data. Without visibility into model inputs, outputs, and downstream tool interactions, organisations cannot demonstrate compliance, identify risky data flows, or enforce controls across internal and third party AI integrations.‍
5. Operational Instability and Slowed AI Adoption
   Lack of discovery forces teams to halt deployments to manually audit AI behaviour or respond to unexpected failures. Confidence in AI systems erodes, slowing adoption and innovation. When AI visibility is incomplete, organisations trade speed for safety and often lose both.

Limitations of Legacy AI Discovery Approaches

Traditional AI discovery methods were adapted from static application and API tooling. In fast moving, agent driven environments, they break down quickly. Legacy approaches create a false sense of control while leaving critical AI behaviour invisible in production.

1. Manual Tracking Cannot Keep Up with AI Velocity: Relying on teams to document models, prompts, agents or tools does not scale. AI components change daily through prompt updates, model swaps and new tool bindings. Human maintained inventories become outdated almost immediately.
2. Design Time Focus Misses Runtime Behaviour: Most legacy approaches document what an AI system is supposed to do, not what it actually does in production. They lack visibility into real prompt inputs, agent decisions, tool calls and downstream actions, where most risk emerges.
3. Static Scans Ignore Agent Interactions: Traditional scanners analyze code, configs or model metadata. They cannot observe multi-step agent workflows, cross service tool chaining or emergent behaviour that only appears at runtime, leaving large execution paths unmonitored.
4. No Context on Data Usage and Exposure: Legacy discovery tools struggle to trace how AI systems access, transform and propagate sensitive data. Without field level and interaction level insight, data leakage and policy violations go undetected until after impact.
5. Inability to Track Drift Across Environments: AI behaviour differs across development, staging and production due to prompt tuning, model versioning and live data inputs. Static inventories cannot detect drift, creating gaps between approved behaviour and real world execution.

Legacy AI discovery fails because it is static in a dynamic system. Modern AI ecosystems require continuous, runtime aware and context rich visibility to remain secure, compliant and reliable at scale.

Read More: Common AI Security Concerns

Key Steps to Build an Effective AI Visibility Strategy

An effective AI visibility strategy must be continuous, runtime aware and deeply contextual. The objective is to eliminate blind spots across models, agents and tools while keeping security, engineering and compliance aligned on a single, trusted view of AI behaviour.

A modern AI visibility strategy unifies discovery, interaction analysis, data tracking and governance into an operational framework that scales with rapid AI adoption.

1. Continuously Discover AI Systems and Agents Across Environments: Automatically identify models, agents, prompts, tools and integrations across development, staging and production. Continuous runtime discovery ensures shadow agents, experimental deployments and deprecated components do not operate outside governance.
2. Capture Real Runtime Interactions, Not Just Configurations: Observe live prompts, responses, agent decisions and tool calls to understand how AI systems actually behave in production. Runtime visibility exposes execution paths and risks that design time documentation cannot capture.
3. Track Sensitive Data Usage Across AI Workflows: Detect and classify PII, PCI and other regulated data flowing through prompts, embeddings, tool calls and outputs. Interaction level data tracking ensures sensitive information is protected throughout multi step AI workflows.
4. Contextualise AI Behaviour with Risk Signals: Enrich every model and agent interaction with context such as user identity, access scope, data sensitivity, policy violations and anomaly patterns. Context transforms raw AI telemetry into actionable risk insights.
5. Monitor Drift in Models, Prompts and Behaviour: Continuously detect changes in model versions, prompt logic and response patterns across environments. Drift detection ensures approved AI behaviour does not diverge silently in production.
6. Integrate Visibility with Testing, Monitoring and Guardrails: Connect AI visibility to evaluation pipelines, runtime monitoring and policy enforcement. Ensure new agents or prompt changes automatically trigger validation, risk checks and ongoing monitoring.
7. Establish Ownership and Governance Accountability: Map AI systems and agents to responsible teams, define lifecycle controls and retire unused or non-compliant components. Clear ownership ensures AI evolution remains controlled, auditable and secure.

Effective AI visibility turns AI systems from opaque black boxes into observable, governable platforms, enabling organisations to scale AI safely, confidently and compliantly.

Read More: Zero Trust Architecture for AI-Driven Market Leadership

KPIs to Measure AI Visibility

Clear metrics are essential to assess the maturity of an AI visibility program and ensure it scales with rapid AI adoption. The following KPIs help security, engineering and compliance leaders quantify coverage, detect gaps and demonstrate control over AI systems in production. Together, they provide an objective framework for measuring risk reduction and operational readiness across the AI landscape.

• AI System and Agent Coverage: Measure the percentage of AI models, agents and tools discovered versus the estimated total across all environments. The benchmark is complete visibility, with a target of near 100 percent coverage across development, staging and production.
• Shadow and Rogue AI Identified: Track the number of undocumented, experimental or unauthorised AI agents and models surfaced and governed. A downward trend indicates stronger controls and reduced unmanaged AI risk.
• Runtime Interaction Coverage: Evaluate what proportion of AI prompts, responses and tool calls are captured and analysed at runtime. High coverage ensures visibility into real behaviour, not just registered configurations.
• Sensitive Data Exposure in AI Workflows: Monitor how many AI interactions process PII, PCI or regulated data without approved safeguards. A consistent reduction reflects improved data classification, policy enforcement and regulatory alignment.
• Time to Discover New or Changed AI Assets (MTTI): Measure how quickly new models, prompt changes or agent deployments appear in the visibility layer. The target is near real time discovery to prevent blind spots during rapid iteration.
• Mean Time to Detect and Respond to AI Risk (MTTD / MTTR): Track the time taken to detect risky behaviour such as policy violations, hallucinations or unauthorised data access, and the time to remediate. Mature visibility programs significantly reduce both.‍
• Compliance and Audit Readiness: Assess whether AI inventories, interaction logs and data flow records provide audit ready evidence for frameworks such as SOC 2, ISO 27001, GDPR, DPDP or the EU AI Act. Strong programs demonstrate continuous, automated reporting with minimal manual effort.

Best Practices for Effective AI Visibility

Achieving effective AI visibility requires more than registering models or approving tools. It demands continuous runtime awareness, behavioural context and tight integration across the AI lifecycle. These best practices help organisations move from reactive oversight to proactive control as AI systems scale.

• Continuous and Automated Discovery: Continuously discover AI models, agents, tools and integrations across development and production. AI environments evolve rapidly, and periodic reviews or self reported inventories become outdated almost immediately.
• Runtime Aware Visibility: Capture prompts, responses, tool calls and decision paths at runtime. Static model listings cannot reveal how AI systems actually behave, what data they access or how risks manifest in production.
• Sensitive Data Detection and Flow Mapping: Automatically identify and classify sensitive data used by AI systems, including PII, PCI and regulated information. Track how data flows through prompts, memory and downstream tools to enforce controls where risk is highest.
• Contextual Risk Enrichment: Enrich each AI asset with context such as access permissions, data sensitivity, usage patterns and anomaly signals. Context transforms raw inventories into actionable risk intelligence.
• Centralised Visibility for Cross Functional Teams: Maintain a single, shared visibility layer for security, engineering, compliance and risk teams. A unified view eliminates fragmented oversight and ensures consistent governance decisions.
• Integration Across the AI Lifecycle: Embed visibility into model onboarding, prompt updates, agent deployments and runtime operations. Every change should automatically update inventories, policies and monitoring coverage.
• Risk Based Prioritisation and Response: Prioritise AI systems based on business impact, data exposure and behavioural risk. Focus controls and remediation on the AI workflows that matter most to security, compliance and customer trust.

Read More: What is AI Governance: Examples, Tools & Best Practices

Challenges in Achieving AI Visibility

Even with growing awareness and investment, achieving end to end AI visibility remains difficult for most enterprises. The challenges are structural, operational and amplified by the speed and autonomy of modern AI systems. These barriers explain why many organisations struggle to maintain consistent oversight as AI adoption accelerates.

• Scale and Proliferation of AI Systems: AI models, agents, prompts and tools are being deployed across teams at unprecedented speed. New assistants, copilots and integrations appear weekly, often outside central governance. Manual tracking and periodic reviews cannot keep pace with this rate of expansion.
• Runtime Opacity of AI Behaviour: Unlike traditional systems, AI behaviour emerges at runtime. Prompt chaining, agent tool use and dynamic decision paths are not visible through static inventories or design time reviews, leaving critical blind spots in production.
• Fragmented Ownership and Tooling: AI assets are spread across data science teams, product groups, cloud platforms and third party providers. Each maintains partial context, but no single system captures how models, data and tools interact end to end.
• Hidden Data Flows and Sensitive Exposure: Sensitive data can enter AI systems through prompts, memory, retrieval pipelines or downstream tools. Without deep runtime inspection, organisations cannot reliably identify where regulated data is accessed, stored or leaked.
• Resource and Skill Constraints: AI visibility requires specialised expertise across security, ML and compliance. Many organisations face budget and staffing limitations, slowing the adoption of continuous monitoring and advanced observability.
• Rapidly Evolving Regulatory Expectations: Regulations governing AI, data usage and automated decision making are evolving quickly. Visibility programs must continuously adapt to demonstrate control, traceability and accountability across all AI driven workflows.‍
• Autonomous and Agentic AI Complexity: AI agents act independently, trigger actions without human approval and interact with multiple systems at machine speed. Traditional monitoring was not designed for autonomous behaviour, making visibility especially challenging without AI native approaches.

How to Choose the Right AI Visibility Tools

Selecting the right AI visibility platform is critical to sustaining secure, compliant and scalable AI adoption. Not all tools are built for the runtime complexity, autonomy and data sensitivity of modern AI systems. The following criteria help leaders evaluate solutions that deliver real control instead of surface level reporting.

• Runtime First Visibility: Choose tools that observe AI systems in production, not just design time configurations. The platform should capture live model interactions, agent actions, prompt flows and tool invocations as they occur, without relying solely on logs or developer input.
• Comprehensive AI Asset Discovery: The tool must automatically discover models, agents, prompts, integrations and third party AI services across all environments. Partial inventories create false confidence and leave material blind spots.
• Deep Context and Behavioural Insight: Visibility should extend beyond “what exists” to “how it behaves.” Look for tools that enrich AI activity with context such as data accessed, actions taken, permissions used, frequency, anomalies and downstream impact.
• Sensitive Data Detection and Tracing: Field level detection of PII, PHI and regulated data is essential. The tool should trace how sensitive data enters prompts, moves through models and reaches tools or outputs, enabling precise risk prioritisation and compliance enforcement.
• Support for Agentic and Autonomous Workflows: As AI agents act independently, the platform must monitor non linear execution paths, chained decisions and automated actions. Static or request based tools are insufficient for agent driven systems.
• Low Friction Deployment: Prefer agentless or lightweight instrumentation that does not slow performance or disrupt workflows. High overhead deployments reduce adoption and limit coverage.
• Integration with Security and Governance Workflows: AI visibility should feed directly into monitoring, alerting, policy enforcement and audit reporting. Look for seamless integration with existing security, compliance and DevOps tooling.
• Scalability and Future Readiness: The platform must scale with rapid AI adoption and adapt to evolving regulations and architectures. Tools built for experimentation will not hold up under enterprise wide AI deployment.

The right AI visibility tool becomes a control layer for trust, not just an observability dashboard. It enables organisations to scale AI confidently, with clear insight into behaviour, risk and impact across the entire AI lifecycle.

Top AI Visibility Tools for 2026

AI visibility platforms are rapidly evolving from basic model tracking into fullstack, runtime aware systems that observe agents, prompts, data flows and autonomous actions in production. The strongest tools combine live telemetry, behavioural context and sensitive data intelligence to provide continuous visibility across complex AI deployments.

Below are the leading AI visibility tools for 2026, ranked by depth of runtime insight, automation and enterprise readiness. While Levo.ai leads due to its runtime first architecture, organisations should evaluate options based on deployment scale, AI maturity and governance needs.

1. Levo.ai
2. Arize AI
3. Fiddler AI
4. WhyLabs
5. Robust Intelligence
6. Protect AI
7. HiddenLayer
8. CalypsoAI
9. Arthur AI
10. Datadog AI Observability

AI visibility platforms provide end to end insight into models, agents, prompts and integrations across development and production. They automatically surface shadow AI usage, trace sensitive data exposure, monitor agent behaviour and detect drift, misuse or policy violations in real time. 

By maintaining a continuously updated view of how AI systems actually behave, these tools enable governance, security and operational control as AI adoption scales across the enterprise.

Why Levo.ai is the Right AI Visibility Platform for 2026

With AI systems moving from experimentation to autonomous, production critical execution, visibility must operate at runtime, not after the fact. In 2026, AI risk will no longer come only from models, but from agents, prompts, integrations, and machine driven decisions executing at scale. Levo.ai is purpose built for this shift.

Levo.ai delivers runtime native AI visibility by observing AI activity directly at the execution layer. Instead of relying on logs, SDKs, or post hoc sampling, Levo captures live agent interactions, data flows, and AI driven actions as they occur across internal systems, third party services, and production environments. This ensures no blind spots, even for autonomous or ephemeral AI workloads.

Unlike legacy observability tools that focus on metrics or model outputs in isolation, Levo correlates who the AI is, what it accessed, what decisions it made, and what data it touched, all in real time. Every AI interaction is enriched with context such as identity, permissions, data sensitivity, execution path, and downstream impact, enabling precise risk detection and faster response.

Levo.ai continuously surfaces high risk behaviors such as unauthorized data access, prompt injection paths, excessive permissions, hidden agent dependencies, and sensitive data exposure. Each finding is mapped to business impact and enriched with evidence, allowing teams to reduce detection and remediation times by over 60%.

This runtime first approach transforms AI visibility into an operational control layer:

• Security: Detects unsafe agent behavior, data exfiltration, and misuse in real time. 
• Reliability: Monitors AI execution paths, failures, and cascading dependencies continuously. 
• Compliance: Maintains audit ready visibility aligned with AI regulations and data protection mandates.

Operations: Eliminates blind spots, reduces manual oversight, and scales safely with autonomous AI.

How to Achieve Complete Runtime AI Visibility with Levo

Levo.ai moves AI visibility from passive monitoring to active assurance. As enterprises scale agentic and AI driven systems in 2026, Levo provides the clarity, control, and confidence required to innovate without losing trust, compliance, or resilience.

Achieving full runtime AI visibility requires more than model dashboards or offline evaluations. It demands continuous, execution-level intelligence that understands how AI agents behave, what they access, and how their actions propagate across systems in real time. Levo.ai delivers this through a unified, runtime-first approach designed for autonomous, distributed AI environments.

Levo’s Four Pillar Methodology for Runtime AI Visibility

1. Discover Every AI Agent and Interaction Automatically 

Levo continuously identifies AI agents, model endpoints, tools, and integrations across development, staging, and production. Using agentless, runtime instrumentation, it surfaces first party, third party, embedded, and shadow AI agents without relying on developer declarations or static inventories. This ensures no autonomous workflow or hidden integration remains invisible.

2. See What Data AI Systems Access and Produce

Levo inspects live AI interactions to classify sensitive data at the field and prompt level, including PII, financial data, health data, and proprietary information. Teams gain immediate clarity into which agents access regulated or high risk data, enabling targeted controls before leakage or misuse occurs.

3. Expose Hidden Behaviors and Risky Execution Paths

AI agents often trigger non-linear execution flows, invoke downstream services, or act with excessive permissions. Levo maps these runtime behaviors end to end, exposing prompt injection paths, over privileged agents, unauthorized access, and unintended data propagation. This eliminates blind spots that static reviews and model only tools cannot detect.

4. Enforce Continuous Governance and Compliance at Runtime

By maintaining a continuously updated view of AI agents, data flows, and behaviors, Levo generates audit ready evidence aligned with emerging AI regulations and data protection laws. Governance becomes continuous and automated, without manual reviews, sampling, or retroactive investigations.

Beyond these pillars, Levo’s architecture: runtime native visibility, agentless deployment, zero performance impact, and deep integration with cloud, data, and application layers, makes it uniquely suited for enterprise scale AI adoption. Levo transforms AI visibility from a reactive audit function into an active control plane, enabling organizations to scale AI safely, confidently, and at speed.

Conclusion: Implementing Runtime AI Visibility for Complete AI Protection

AI systems are scaling faster than most organisations can understand or control. Autonomous agents, LLM powered workflows and machine driven integrations now execute decisions, access sensitive data and trigger actions across core business systems. In this environment, visibility is no longer optional. You cannot secure AI systems you cannot observe at runtime.

Manual reviews, offline evaluations and model centric dashboards fail the moment AI moves into production. They cannot track real execution paths, transitive trust, dynamic tool usage or data exposure as it happens. Effective protection requires continuous, runtime first visibility that captures how AI actually behaves, not how it was designed to behave.

The reality is clear. AI adoption is accelerating, breach costs are rising, and regulators are demanding provable governance. Most enterprises still lack a reliable way to see which agents exist, what data they access and how decisions propagate across systems. At this scale, static controls collapse. Runtime visibility becomes the only defensible foundation for security, compliance and operational trust.

This is where Levo.ai changes the equation. By transforming runtime AI visibility into an always on control plane, Levo enables organisations to detect risk as it emerges, enforce governance continuously and prevent incidents before impact. Every AI agent, interaction and data flow is discovered, contextualised and governed in real time, then connected directly to monitoring, policy enforcement and response.

Whether you are deploying AI copilots, orchestrating multi-agent workflows or embedding AI into customer facing systems, Levo gives you the clarity and confidence to scale safely without slowing innovation.

Levo is more than just runtime AI Visibility, it also offers real time AI monitoring & governance along with runtime AI threat detection and AI attack protection. Moreover, AI Red Teaming ensures all the possible vulnerabilities are duly checked in production, offering a 360 degrees complete AI Security and compliance. 

See every agent. Understand every action. Control every risk. That is the Levo.ai approach to complete runtime AI protection.

Implement real time AI visibility with Levo. Book your demo today and secure AI at runtime by design.