What is API Discovery: Examples, Tools & Best Practices

APIs now function as the operational backbone of modern enterprises, shaping every transaction, workflow and digital service. The 2025 Postman report shows that 69% of developers spend more than 10 hours a week on API work, and more than 25% spend over 20 hours. 

API driven businesses are turning that investment into revenue, with 43% of API first organisations generating more than 25% of their revenue from APIs and 20% generating more than 75%.

But despite this dependency, visibility has not kept pace. 55% of enterprises now manage more than 500 APIs, yet nearly 40% admit they cannot maintain an accurate inventory. 

API ecosystems continue to expand with weekly releases and hundreds of distributed microservices, creating a widening gap between what is deployed and what security leaders can actually see. The result is measurable risk: 48% of organisations cite API sprawl as a top security concern and 39% struggle to track what is running in production.

For CISOs, the mandate is clear. You cannot secure what you cannot see. API visibility provides that clarity by revealing every API in use, mapping its behaviour and identifying the data it handles. It transforms a fragmented, fast moving surface into a knowable and governable system that supports both resilience and growth.

What is API Discovery?

API discovery is the discipline of identifying, mapping and cataloguing every API operating across an organisation’s digital environment. It is not limited to listing a few documented REST endpoints. It produces a complete, continuously updated inventory that reflects real usage, real behaviour and real risk for all API types and frameworks: REST, GraphQL, gRPC and SOAP. For modern enterprises running microservices, hybrid cloud and third party integrations, API discovery is the foundation of API visibility.

Different Techniques for API Discovery

Effective API discovery combines several complementary techniques:

• Specification and code analysis: Parsing source code, OpenAPI and Swagger specs, WSDL files and Protobuf definitions to uncover the interfaces developers intend to expose.
• Traffic inspection: Analysing live traffic flowing through gateways, service meshes and host machines. Kernel level eBPF sensors capture API calls directly at the OS layer, exposing undocumented or shadow endpoints without requiring inline proxies.
• Cloud and OSINT scanning: Reviewing cloud flow logs, DNS records and public certificate data to detect externally reachable, forgotten or misconfigured APIs.
• Runtime metadata collection: Recording behavioural attributes such as parameters, methods, authentication schemes, rate limits and error responses, enriching each endpoint with operational context.

By layering these capabilities, organisations can surface every API in their environment, internal or external, partner or third party, open source or custom built, and classify them as active, shadow, zombie, deprecated or sensitive data handling. Continuous discovery ensures that inventory updates automatically as developers push new code or deploy new services, closing the visibility gap that manual surveys and quarterly audits inevitably create.

Why is API Discovery important?

APIs now form the backbone of modern digital systems, yet most organizations are flying blind. As API ecosystems scale into the hundreds or thousands, visibility has failed to keep pace, leaving gaps that compound across security, compliance, operations, and AI governance.

1. Security Gaps: Shadow and zombie APIs are the easiest paths into an organization because they operate outside logging, monitoring, and policy enforcement. Without discovery, teams cannot apply authentication, rate limiting, or data protection controls, giving attackers unguarded entry points.

2. Compliance and Audit Risk: Frameworks like PCI DSS and HIPAA mandate visibility into every API touching sensitive data. Incomplete inventories make it impossible to prove compliance or detect when PII flows through forgotten endpoints, increasing audit failures and regulatory exposure.

3. Operational Friction: When teams don’t know what already exists, they rebuild it. Poor API visibility leads to duplicated services, slower integrations, delivery bottlenecks, and wasted engineering cycles, an issue cited by more than 90% of teams in recent industry surveys.

4. AI Governance Challenges: APIs now feed data into AI agents and automated systems, growing rapidly year over year. Without knowing which APIs supply which models, organizations cannot enforce data governance, restrict misuse, or validate that AI systems consume only approved sources.

In short: you can’t secure or govern what you can’t see.

Continuous API discovery is the foundation for eliminating blind spots, enforcing controls, and enabling secure, scalable innovation.

Who needs API Discovery?

API discovery isn’t a niche security function, rather it’s a cross functional necessity for every team that builds, operates, or governs digital systems. With API ecosystems expanding, each stakeholder relies on accurate, real time visibility to keep systems secure, compliant, and scalable.

Any organisation that builds, consumes, or monetises APIs needs continuous discovery, especially those operating in regulated environments or distributed engineering setups. It’s the foundation for trust, resilience, and sustainable growth.

Here are some of the key personas that need API Discovery in practice:

• Security and Risk Leaders (CISOs): They need a complete API inventory to map the attack surface, uncover shadow and zombie endpoints, and prioritise vulnerability testing based on real exposure.
• DevOps Teams: Discovery helps control microservices sprawl, eliminate duplicate services, streamline deployments, and ensure every API has clear ownership and lifecycle management.
• Compliance & Audit Teams: To meet standards like PCI DSS, HIPAA, and SOC 2, they need auditable records of every API touching regulated or sensitive data, something impossible without automated discovery.
• Product, Engineering and AI Teams: Product builders and AI teams rely on discovery to safely reuse existing APIs, speed up integration, and ensure AI agents interact only with approved, governed interfaces.‍
• ‍Executives and Technology Leaders: Visibility into the full API estate helps leaders understand digital health, quantify operational risk, and make informed investment and transformation decisions.

Risks of Incomplete API Discovery and Visibility

When organisations operate without a complete and continuously updated API inventory, blind spots accumulate and create systemic exposure across security, compliance and operations.

1. Unseen Attack Surface: Shadow and zombie APIs remain outside logging, monitoring and baseline security controls. These neglected endpoints are the first targets attackers probe, giving them easy access without triggering alerts.

2. Stale and Misconfigured APIs: New APIs are often deployed before security teams know they exist, while deprecated ones stay online, unauthenticated or misconfigured. Fragmented inventories create policy drift, inconsistent protections and lingering vulnerabilities.

3. Blind Spots Between Deployments: Manual tagging, spreadsheets and quarterly audits cannot keep pace with weekly or daily releases. With each deployment cycle, the inventory gap widens and security loses visibility into new endpoints and behaviours.

4. Sensitive Data Exposure: APIs that transfer PII, PHI or financial data must be identified and prioritised for testing. Without complete visibility, sensitive data can leak through forgotten or unmonitored endpoints, often going undetected until it is too late.

5. Missed Dependencies and Cascading Failures: In microservices architectures, the downtime of one API can impact many others. Without a real time dependency map, teams cannot trace how failures propagate or quickly understand the blast radius during incidents.

6. Compliance Gaps and Audit Failures: Regulatory frameworks such as PCI DSS, HIPAA and GDPR require full visibility into all systems that process sensitive data. Missing APIs translate into missing controls and increase the risk of audit failures, penalties and remediation costs.

7. Misgoverned AI Integrations: AI agents now call internal and external APIs autonomously. Invisible endpoints create untracked decision paths, making it difficult to govern how AI systems consume data or prevent unintended behaviours.

8. Slower Incident Response: During breaches or outages, incomplete inventories delay triage. Teams spend critical time identifying which APIs are involved, who owns them and what data or systems are at risk.

Incomplete API discovery does not just create gaps. It amplifies risks across the entire digital estate. Full visibility is essential to prevent attacks, avoid compliance issues, manage dependencies and ensure resilient operations.

Limitations of Legacy API Discovery Approaches

Traditional methods of tracking APIs were built for slower, monolithic architectures. With microservices, distributed teams and continuous delivery in place, these approaches cannot keep pace and leave organisations operating with outdated or incomplete visibility. 

Legacy API discovery was not designed for the speed, scale or complexity of modern systems. Without continuous, multi layer and behaviour aware discovery, organisations end up operating in the dark and absorbing unnecessary security, compliance and operational risks.

• Manual Inventories Cannot Scale: Spreadsheets, surveys and ticket based inventories break immediately once organisations cross a few hundred APIs. They depend on human inputs that are inconsistent, incomplete and quickly outdated.
• Static Documentation Misses Reality: OpenAPI files, Postman collections and code based specs capture intended behaviour, not actual behaviour. They do not reflect shadow endpoints, unapproved changes, incomplete deprecations or runtime drift introduced after deployment.
• Gateway Only Discovery Is Too Narrow: Many legacy tools rely solely on API gateways for visibility. This ignores internal service to service calls, direct container traffic and APIs exposed through sidecars, reverse proxies or custom routing. The result is large blind spots in internal mesh traffic.
• No Runtime Context or Behavioural Insight: Older tools stop at endpoint enumeration. They do not collect parameters, authentication patterns, rate limits, payload structures or error behaviours that are essential to assess risk, understand sensitivity or prioritise testing.
• Inability to Detect Shadow, Zombie or Rogue APIs: Legacy discovery depends on predefined locations or declared specs. Anything outside those paths goes unnoticed. Shadow APIs created during rapid iteration and zombie APIs left behind during migrations remain invisible.
• Slow, Periodic and Disconnected from CI CD: Quarterly scanning or ad hoc reviews cannot keep up with daily or weekly releases. Without continuous integration into build pipelines and runtime environments, inventories drift almost immediately after creation.
• Limited Cloud and OSINT Awareness: Older tools do not interrogate cloud flow logs, DNS records, public certificates or internet facing service metadata. As a result, externally exposed APIs, forgotten subdomains and unmonitored cloud functions remain undiscovered.‍
• Fragmented Visibility Across Teams: Documentation lives with developers, gateway configs live with platform teams and security logs live with SOC teams. Legacy discovery cannot unify this fragmented data, making it impossible to build a single source of truth.

Key Steps to build an Effective API Discovery Strategy

Building a modern API discovery program requires a shift from periodic, manual checks to continuous, intelligence driven visibility. An effective strategy aligns engineering, security and compliance needs while ensuring that every API is tracked, classified and governed in real time.

A well executed discovery strategy transforms API visibility from a static documentation exercise into a living, data rich control layer. When these steps are implemented together, organisations gain continuous, comprehensive insight that aligns engineering velocity with security and regulatory requirements.

1. Combine Code Analysis with Traffic Instrumentation: Start by parsing source code, specifications and CI pipelines to identify declared interfaces. Pair this with runtime traffic sensors such as eBPF to observe the actual API calls flowing through your infrastructure. This dual approach uncovers both intended and undocumented behaviour.

2. Discover Every API Type Across the Estate: Ensure the program covers internal, external, partner, open source and third party APIs across all protocols including REST, GraphQL, SOAP and gRPC. Modern architectures mix multiple styles and incomplete coverage creates blind spots.

3. Extend Discovery Across All Environments: Monitor APIs in development, QA, UAT, staging, SIT and production. Adversaries increasingly target non production systems where authentication and monitoring controls are weaker. Visibility must cover the entire SDLC. 

4. Enrich Each API with Deep Metadata: Capture granular details such as endpoints, methods, parameters, authentication schemes, rate limits and version lineage. Map which APIs process sensitive data so teams can prioritise risk based on exposure, compliance impact and business criticality.

5. Automate Inventory Updates and Version Control: Discovery must be continuous. The inventory should update automatically as teams commit code or deploy services, while maintaining version history for auditing and change management. Manual updates cannot keep pace with rapid releases.

6. Integrate Discovery Across the Toolchain: Connect discovery to GitHub or GitLab, CI CD pipelines, cloud platforms, gateways and service meshes. Pulling signals from code to runtime ensures the inventory reflects real world behaviour rather than static assumptions.

7. Provide Actionable Risk Context Instead of Raw Lists: Classify APIs by authentication strength, rate limiting, sensitivity, external exposure and business function. Prioritise remediation based on risk rather than volume so teams can focus on issues that meaningfully reduce attack surface.

KPIs to Measure API Discovery

A mature API discovery program requires more than an inventory; it demands measurable, repeatable outcomes that reflect true visibility across the organisation. Tracking the right KPIs helps leaders validate coverage, quantify risk reduction and justify investment in continuous discovery.

Together, these KPIs create a clear, quantifiable picture of API visibility maturity. They help CISOs and engineering leaders move beyond static documentation toward a dynamic, risk aligned discovery program that grows in accuracy and impact with every release.

• Inventory Coverage Rate: Measure the percentage of APIs discovered compared to the estimated total across all business units and environments. Break it down by internal, external, partner and third party APIs to highlight gaps hidden within specific teams or architectures.
• Shadow and Zombie Detection Rate: Track how many undocumented, deprecated or previously unknown APIs are surfaced during each discovery cycle. High detection rates signal a widening attack surface and help benchmark improvement over time as processes mature.
• Discovery Latency: Monitor the average time between an API being deployed and it appearing in the inventory. Low latency indicates real time discovery while high latency signals exposure gaps where vulnerable APIs may run without oversight.
• Metadata Completeness: Evaluate the proportion of APIs enriched with full metadata such as authentication status, rate limiting, sensitive data flags, error patterns, ownership and version history. Comprehensive metadata is essential for risk based prioritisation and governance.
• Incident Reduction Linked to Unknown APIs: Measure the decrease in security incidents, audit findings or compliance violations caused by undiscovered or misclassified APIs. As visibility improves, blind spot driven incidents should trend downward.‍
• Update Frequency: Assess how often the inventory refreshes across environments. Shorter refresh intervals indicate continuous discovery while longer intervals point to manual, high friction processes.

Best Practices for effective API Discovery

An effective API discovery program is not defined only by tooling, but by the operational habits that keep inventories accurate, contextual and resilient. These best practices help organisations maintain real time visibility even as architectures evolve and release cycles accelerate.

These practices ensure API discovery is not a one time initiative but an ongoing discipline that supports secure development, operational efficiency and enterprise wide governance.

• Automate End to End Discovery: Discovery should run continuously and silently in the background. Agentless, passive techniques must replace manual surveys, tribal knowledge and quarterly audits. Automation ensures every new or changed endpoint is captured without developer intervention.
• Use Hybrid Instrumentation: No single technique provides complete visibility. Combine static analysis of code, specifications and pipelines with traffic inspection through sensors such as eBPF to uncover both declared and undocumented APIs. This dual approach eliminates blind spots that traditional scanners miss.
• Classify and Tag Every API: Attach metadata such as purpose, ownership, environment, authentication type, rate limiting and sensitive data exposure. Tags enable governance policies, alert routing and risk based prioritisation across teams.
• Monitor All Environments Equally: Attackers often target development, QA or staging environments because controls are weaker. Apply the same discovery rigor across every environment to detect shadow or exposed APIs before they reach production.
• Integrate with Governance and Security Testing: Connect discovery outputs to API governance systems, policy engines and automated test frameworks. This ensures that every newly discovered endpoint immediately inherits standards, controls and security checks.
• Generate Living Documentation: Use runtime behaviour and traffic patterns to automatically generate and update API specifications. Living documentation eliminates drift, accelerates onboarding and reduces duplication across teams.‍
• Align Stakeholders Through Shared Visibility: Make the API inventory a shared, accessible source of truth for security, engineering, compliance and product teams. Use dashboards, reports and ownership tagging to drive accountability and coordinated remediation.

Challenges in API Discovery and Inventory

Even with automation, API discovery remains one of the most complex tasks in modern engineering and security. The challenge is not simply finding endpoints, but maintaining an accurate, high context inventory in environments where change is constant and decentralised.

• Speed of Change Outpaces Documentation: APIs are created, modified and deployed at a rate manual processes cannot match. Documentation lags behind reality. Only 3% of developers consider their APIs “very well documented,” and 39% developers cite inconsistent documentation as a major blocker. As release velocity increases, inventories drift unless discovery is continuous.
• Uncontrolled API Sprawl: Microservices, partner integrations and rapid digital expansion fuel exponential API growth. Market estimates active APIs could surpass 1 billion by 2031. Without automated visibility, organisations lose track of what exists, who uses it and whether it should still be online.
• Fragmented Ownership Across Teams: APIs are built by distributed squads, often across regions and business units. Without central governance, no single team maintains responsibility for documentation, security policies, lifecycle management or retirement. This fragmentation leads to inconsistent standards and unmanaged risk.
• Third Party and Open Source Blind Spots: Modern applications embed SaaS connectors, SDKs and open source services that generate APIs beyond an organisation’s direct control. These external interfaces are rarely documented internally but still expand the attack surface and must be discovered and assessed.
• Difficulty Identifying Sensitive Data Flows: Discovery alone is insufficient. Organisations need to know which APIs process PII, PHI or payment data to prioritise testing and safeguards. Without field level visibility, sensitive data exposures remain hidden.

Integration Gaps with Modern Toolchains: Legacy scanners and inventory tools struggle to integrate with CI/CD pipelines, container orchestration platforms, service meshes and AI driven workflows. When discovery is not embedded naturally into the engineering ecosystem, it becomes a disconnected silo instead of a continuous control.

How to choose the right API Discovery Tools

Selecting an API discovery platform is a strategic decision that directly shapes an organisation’s security posture, operational efficiency and governance maturity. The right tool must go beyond basic enumeration and deliver continuous, context rich visibility across heterogeneous, fast moving environments.

By prioritising these capabilities, organisations can select a discovery platform that delivers accurate, real time visibility and strengthens security, compliance and operational reliability.

• Comprehensive and Continuous Coverage: Prioritise platforms that automatically discover every API type internal, external, partner, third party and open source across REST, GraphQL, gRPC and SOAP. Coverage should extend across on prem systems, multi cloud deployments, containers and serverless functions to eliminate blind spots.
• Agentic and Agentless with Low Overhead Architecture: Choose solutions that use eBPF, agentic to capture traffic at the operating system layer or other agentless instrumentation at the network/ application level without code changes or proxies. This ensures accurate discovery with minimal latency and near zero operational overhead.
• Built in Sensitive Data Awareness: The platform should automatically detect APIs handling PII, PHI or payment data and rank them based on exposure and authentication strength. This allows teams to prioritise controls where business and regulatory impact is highest.
• Real Time Risk Context for Every Endpoint: Metadata such as authentication scheme, encryption status, rate limits, reachability, request patterns and status codes should be captured continuously. This context enables filtering, impact assessment and risk scoring at scale.
• Auto Update and Version Tracking: Discovery must be dynamic. Tools should detect new APIs immediately after deployment, track changes and maintain version histories so that drift is identified before it becomes a governance issue.
• Flexible and Deep Integrations: Ensure compatibility with your engineering and security ecosystems including GitHub, GitLab, Jenkins, Kubernetes, Docker, cloud providers, Postman, Burp Suite and SIEM or SOAR platforms. Discovery should plug seamlessly into existing workflows.
• Full Visibility Without Performance Tradeoffs: Traffic sensors must observe passively, ensuring production systems are never slowed by discovery processes. Any tool that degrades performance introduces operational risk.
• Support for Complex and Air Gapped Environments: Enterprises operating in regulated or disconnected networks need tools that can run fully on premises without exporting data to external clouds.
• Actionable Insights Beyond Enumeration: Look for platforms that offer dashboards, alerts and contextual insights that flow into testing, remediation and governance workflows. Discovery should drive action, not just generate lists.‍
• Proven Scalability and Efficiency: The platform must scale across thousands of microservices and high volume traffic patterns without excessive resource consumption. Scalability is essential for enterprise wide adoption.

Top API Discovery Tools (2025)

Below is a concise, research backed overview of the leading API discovery platforms for 2025. These summaries combine industry analysis with handson evaluations to highlight core strengths, gaps and ideal fit for different teams.

1. Levo.ai (Recommended)

Levo.ai provides full spectrum API security with 100% visibility across internal, external, partner and third party APIs. Its eBPF powered runtime discovery blends with code, log and traffic instrumentation to build an accurate, behaviour aware API inventory enriched with metadata such as auth types, rate limits, changelogs and sensitive data flows. 

Key advantages include:

• Comprehensive instrumentation: Automatically discovers REST, GraphQL, gRPC and SOAP across dev, QA, UAT, staging and production environments, without code changes or agent overhead.
• Integrated security: Generates living documentation, runs role aware attack simulations and offers built in remediation, ticketing and prioritisation.
• Seamless integrations: Works with GitHub, GitLab, Jenkins, Jira, AWS, GCP, Slack, Kubernetes, Postman, Burp Suite and more.
• Privacy and efficiency: Agentless sensors ensure sensitive data never leaves your environment, with pricing optimised for low egress and compute consumption.
  

Pros: Full SDLC coverage, highly accurate behaviour aware discovery, automated documentation and remediation.

Cons: A newer entrant than legacy vendors, but rapidly gaining enterprise adoption.

2. Traceable.ai

Traceable offers strong runtime first API security focused on attack detection, behavioural analytics and fraud prevention. It captures full traffic payloads to provide deep forensic visibility and integrates with AWS, Azure, GCP, Datadog, Splunk, ServiceNow, Jira and Okta.

It excels at runtime detection and investigation but offers limited discovery breadth, primarily surfacing active, externally exposed APIs. Its traffic heavy architecture can also lead to higher storage and compute consumption.

Read More: Top 10 Traceable AI Alternatives

3. Rapid7

Rapid7 provides API security as part of its broader AppSec suite, delivering DAST based point in time scans. It integrates with Jira, Jenkins, Splunk and major clouds, offering a familiar workflow for audit focused teams.

However, the scan centric architecture lacks continuous runtime visibility, leaving gaps between assessments and creating blind spots for shadow or low traffic APIs.

Read More: Top 10 Rapid7 Alternatives

4. Akto

Akto is a developer focused platform combining lightweight API discovery with pre-built test templates and traffic analysis. Integrations include GitHub, GitLab, Jenkins, Slack and Postman.

It’s simple to deploy and offers 1,000+ test payloads, but discovery coverage is narrower, primarily identifying active or frequently used endpoints, while internal or low traffic APIs may go undetected.

Read More: Top 10 Akto Alternatives

5. Other notable tools

Platforms like Salt Security, Qualys, StackHawk and Akamai offer varying blends of scanning, monitoring and runtime protection. Their strengths differ across compliance, detection and testing workflows.

When comparing these solutions, align your evaluation with the earlier selection criteria, especially coverage depth, automation maturity, scalability, integration fit and support for hybrid or cloud native environments.

Read More: Top 10 API Discovery Tools in 2025

Why Levo.ai is the right API Discovery Platform for 2025

Levo.ai distinguishes itself by pairing complete API visibility with deep behavioural context and automation across the SDLC. Its eBPF powered sensors and 24+ agentless techniques continuously discover every API, i.e. internal, external, shadow, zombie and third party, across all environments. 

Unlike platforms dependent solely on traffic or specifications, Levo ingests code artefacts, logs and runtime signals to construct a behaviour aware inventory enriched with metadata such as authentication status, rate limits, version history, changelogs and error responses. 

This dual instrumentation approach helps in sensitive data discovery, detection of broken access controls, sensitive data exposure and in real time, allowing teams to prioritise remediation intelligently.

Where many tools stop at runtime or scanning, Levo extends discovery across the entire SDLC. It integrates directly with GitHub, GitLab, Jenkins, service meshes, cloud providers, CI/CD pipelines and testing frameworks so discovery begins at code and continues through staging, UAT and production. 

Out of line sensors ensure zero performance impact, while privacy safe deployment guarantees that no payloads or sensitive data leave the customer environment. Additional features like automated documentation, automated API security testing, monitoring, detection and protection workflows streamline collaboration between security, engineering, compliance and platform teams.

Levo.ai provides the visibility backbone required for modern API security: continuous discovery, enriched behavioural context, precise metadata, automated testing and integrated remediation. 

For organisations aiming to reduce risk, accelerate release velocity and enable AI driven governance, Levo.ai stands out as the strategic API discovery platform for 2025.

How to achieve total API Discovery & Inventory with Levo

Implementing Levo to achieve complete API visibility is simple and designed to fit directly into modern engineering workflows. Its multi-layered instrumentation ensures that every API, whether documented or not, is discovered, classified and monitored without friction.

By following these steps, organisations gain total, real time API visibility, from code to runtime, enabling proactive security, automated governance and accelerated innovation.

1. Deploy Levo’s eBPF sensor: Install Levo’s lightweight, kernel sensor across cloud and on prem hosts. It passively captures API traffic for REST, GraphQL, gRPC and SOAP without modifying code, inserting proxies or impacting performance. This forms the backbone of continuous, real time discovery.

2. Connect code and CI/CD: Integrate Levo with GitHub, GitLab and your CI/CD pipelines so it can ingest API specifications, code artefacts and build metadata. This enables early stage discovery, allowing Levo to identify APIs at commit, build and deploy time, not only at runtime.

3. Ingest traffic from gateways and service meshes: Configure Levo to pull structured telemetry from API gateways, service meshes and cloud logs. This ensures that APIs flowing through your infrastructure, including partner and third party traffic are automatically detected and added to the inventory.

4. Classify and tag APIs: Use Levo’s portal to categorise APIs by type, protocol, environment and ownership. Levo autonomously identifies shadow, zombie and deprecated endpoints, and highlights sensitive data flows across environments such as dev, QA, staging and production.

5. Enrich with metadata and risk scoring: Levo augments each endpoint with authentication context, rate limits, error responses, version history, changelogs and reachability. This enriched metadata powers precise filtering, prioritisation and automated risk scoring across large API estates.

6. Integrate testing and monitoring: Connect Levo’s discovery engine to its integrated testing, detection, protection and monitoring modules. Newly discovered APIs are automatically scanned for vulnerabilities, validated for compliance and protected  continuously in production, closing visibility and security gaps instantly.

7. Generate living documentation and reports: Levo auto generates human readable, always up to date API documentation based on runtime behaviour. Dashboards and reports help engineering, security and compliance teams share inventory status, track trends and drive accountability.

Conclusion

APIs are expanding faster than organisations can keep up with. With microservices accelerating delivery, AI systems generating dynamic workloads and regulations demanding stronger oversight, discovery and visibility have become the core of API security. You cannot govern what you cannot see, and incomplete inventories remain one of the biggest sources of risk.

Traditional methods like manual catalogues, static documentation and periodic scans cannot match the speed and complexity of modern architectures. What today’s environment requires is continuous, automated discovery enriched with runtime behaviour, metadata and sensitive data insight, built directly into the SDLC instead of added after deployment.

The trend lines are unmistakable. API estates are exploding, blind spots are increasing and undiscovered endpoints continue to fuel breaches and compliance failures. At enterprise scale, only automated visibility can provide the accuracy, coverage and freshness teams need to stay ahead.

This is where Levo transforms the equation. By always turning discovery, metadata enrichment, documentation, testing and monitoring into a unified control plane, Levo shifts teams from only reacting to incidents to both:  preventing them and protecting against them. 

Every API is identified, governed and protected in real time, empowering stronger security, faster development and better operational resilience.

Whether you are scaling microservices, migrating legacy systems or enabling AI driven architectures, Levo gives you the visibility foundation needed to move quickly without compromising safety.

See everything. Understand everything. Secure everything.

Levo delivers full spectrum API discovery and visibility for modern organisations. Book your demo today to implement API security seamlessly.