What is API Protection: Examples, Tools & Best Practices

APIs now are the business: they carry payments, identities, partner integrations, mobile experiences, and increasingly, AI driven automation. Gartner projects API demand to keep accelerating, with AI and LLM based tools contributing materially to API growth.

The problem: APIs have become the most exploited interface in modern architectures. When security related incidents happen, they aren’t “IT problems”; they become revenue, regulatory, and reputation problems. IBM’s 2025 data breach research cites $4.4 Million as the global average cost, and nearly $5.05M when breaches span multiple environments (which is increasingly the norm in hybrid/multi cloud stacks).

That’s why API Protection is no longer a feature; it’s a business capability.

What is API Protection?

API Protection refers to a class of runtime, production security controls that actively intercept and enforce security decisions on API traffic before malicious requests can impact the application. These controls operate inline with live traffic, inspecting requests and where applicable, responses in real time to block, rate limit, sanitize, or challenge calls that violate expected behavior or security policy. 

In practice, API protection is commonly delivered through Web Application and API Protection (WAAP) platforms, which extend traditional WAF capabilities with API-aware parsing, schema validation, behavioral analysis, and abuse detection, and may be complemented by Runtime Application Self Protection (RASP) mechanisms that operate inside the application for deeper execution context.

Unlike detection only tools that log suspicious activity for later review, API protection is designed to prevent attacks as they happen, including injection attempts, automated abuse, and logic based attacks that span multiple requests. At its best, effective API protection remains transparent to legitimate traffic, adding minimal latency, while intervening decisively when traffic deviates from learned or defined norms, such as unexpected data structures, excessive access patterns, or anomalous user behavior. 

Achieving this balance of accuracy, performance, and coverage is critical: protection that blocks too aggressively erodes trust and adoption, while protection that merely observes fails to meaningfully reduce risk in modern, API first environments

Examples of API Protection

API protection is not one control, rather it's a system of protections. Common examples of API Protection include:

• Object level authorization enforcement to prevent BOLA which is the #1 OWASP API risk.
• Schema and contract validation for requests and responses to stop over posting, data leakage, and unexpected fields.
• Token and session abuse detection: replay, anomalous reuse, impossible travel, privilege escalation paths.
• Behavioral rate limiting and bot/abuse controls tuned to the endpoint and user context (not global thresholds).
• Runtime attack shielding that blocks exploit patterns without breaking real users (low latency, explainable decisions).

Key API Security Risks which needs to be addressed

Most API incidents map back to a few recurring failure modes yet they keep happening because modern API abuse often looks like valid traffic.

1. Access control failures (AuthN/AuthZ)

This is the #1 category to get right because it directly governs who can do what, to which resource.

• Broken Object Level Authorization (BOLA): attackers access another user’s records by changing an ID (such as, /users/123 to /users/124).
• Broken function level authorization: users invoke admin only actions because role checks are missing or inconsistent.
• Token/session misuse: stolen tokens, replay, weak refresh flows, and over privileged service accounts.

2. Business logic abuse

These aren’t “classic vulnerabilities” , they are workflow exploits.

• Bypassing steps (such as skipping payment confirmation, coupon stacking, refund loops)
• Exploiting edge cases (race conditions, inventory holds, account linking)
• Abusing “valid” features at scale (automation, scraping, enumeration)

Business logic abuse is especially dangerous because perimeter tools often see it as normal API traffic so you need runtime context (user intent signals, behavioral baselines, object ownership, velocity, anomaly patterns).

3. Sensitive data exfiltration

APIs are data highways. If responses leak too much or logs/telemetry capture secrets attackers don’t need ransomware; they just siphon.

• Excessive data exposure (returning fields a client shouldn’t see)
• Object property authorization gaps (fields not protected even when the object is)
• PII/PHI/PCI leakage via responses, errors, misconfigured debug endpoints, or overly verbose logs
• “Shadow” and “zombie” APIs that expose legacy fields nobody monitors anymore

These three access control, business logic, and sensitive data exfiltration are the risks that most directly translate into board visible outcomes: fraud, regulatory exposure, customer trust loss, and operational disruption.

Why is API Protection important?

API protection matters because APIs have become the primary interface for revenue and regulated data. Over the last two decades, enterprises shifted from on premises monoliths released on long cycles to cloud hosted, microservices based systems shipped continuously. That shift increased speed, but it also expanded the attack surface. Instead of one front door, you now have thousands of API paths, more entry points, more internal service calls, and far more change.

This is where the business case becomes obvious. Traditional perimeter security was built to stop known malicious patterns. Modern API abuse often uses valid endpoints, valid payload formats, and valid authentication, but with malicious intent. The result is direct business impact.

1. Revenue impact from fraud, account takeover, and automated abuse of transaction flows.
2. Brand impact when customers experience disruption or public data exposure.
3. Regulatory impact when APIs leak PII, PHI, or PCI through responses, debug behavior, or misconfiguration.
4. Financial impact that escalates quickly in complex environments.

When API protection is not done well, the outcome is usually predictable. Teams lose confidence in blocking, shift controls into detect only mode, attackers slip through the gaps, and security becomes a constant operational tax.

When do you need API Protection?

You need API protection now if your architecture or operating model matches how modern attacks work.

1. You have moved to cloud hosted microservices where internal service to service traffic carries sensitive actions and data, not just edge traffic.
2. Your applications are API first by default, powering mobile, web, partners, and third party integrations.
3. Most of your traffic is encrypted end to end, including internal calls, which reduces visibility for perimeter only tools unless you deliberately design for inspection.
4. Your teams ship frequently, meaning new endpoints and schema changes can appear weekly, and documentation can drift from reality.
5. You are adopting GenAI and automation that increases API volume and change rate. Gartner expects more than 80 percent of enterprises to use GenAI APIs by 2026.
6. You cannot answer these with evidence: how many APIs exist, which are internet facing, which are internal, who owns them, and what sensitive data they expose.

A practical trigger that resonates with executives is this: if digital growth is driven by APIs but the time required to protect new endpoints is not close to zero, the business is operating with a built-in exposure window.

Why Runtime API Protection matters?

For most enterprises, the goal is not perfect security. The goal is to prevent revenue disrupting incidents without slowing delivery.

Runtime API protection matters because build time controls cannot keep up with production reality. APIs change every sprint, behavior shifts with product experimentation, and high impact abuse often looks like legitimate usage.

Runtime protection closes three gaps.

1. It stops abuse that only shows up in production context, including token misuse, abnormal sequences, and high velocity enumeration.
   
2. It reduces exposure from drift by observing what is actually running and flagging new endpoints, schema changes, and unexpected data exposure as they appear.
   
3. It makes prevention practical. When false positives disrupt customers, teams avoid blocking and accept exposure. High precision runtime enforcement keeps protection on without breaking customer journeys.

Risks of Incomplete API Protection

Incomplete API protection does not fail as a security story. It fails as a business story. It creates three predictable outcomes.

1. Revenue risk shows up first. Fraud, account takeover, and automated abuse hit the same APIs that power sign up, login, checkout, and partner transactions. If protection is inconsistent, attackers find the weakest endpoint and repeat the playbook at scale.
   
2. Compliance and brand risk follows. APIs are where customer data moves. If you do not have full coverage across every API, including internal and partner-facing APIs, sensitive data exposure becomes a matter of time, not probability.
   
3. Execution slows down. When teams do not trust production blocking, they move controls to monitor only mode. That creates recurring incident response, noisy alerts, and exceptions that have to be managed every release. The hidden cost is slower delivery and higher operating expense.

The most dangerous part is that incomplete protection looks fine in reports. You may be testing in pre production, scanning a subset of endpoints, and monitoring logs. But without full runtime coverage and prevention, the business is still exposed where the money and data actually flow.

Limitations of Legacy Production Security for API Protection

Legacy production security, particularly traditional WAFs, was designed for a very different era. It assumed a stable perimeter, predictable applications, and predominantly human driven traffic. Modern software architectures have steadily eroded these assumptions, exposing structural gaps that WAFs were never built to address.

• Monolith to Microservices: the edge is no longer the control point
  Microservices expose hundreds of APIs across REST, gRPC, and GraphQL, with most traffic now east-west inside clusters. Perimeter WAFs see only gateway traffic and miss internal service to service calls, lateral movement, and chained API behavior. Static rules cannot keep up with constantly changing endpoints, leading to blind spots, rule drift, high false negatives, and noisy false positives.

• On-prem to cloud and hybrid: dynamic and encrypted by default
  Cloud native environments are ephemeral, multi region, and zero trust by design. Traffic is encrypted end to end and identities are short lived. Legacy appliances expect fixed IPs and centralized inspection points. Forcing traffic through off box inspection increases latency and cost while still missing encrypted east west flows. The result is inconsistent enforcement, coverage gaps across clouds, and attacks hidden inside internal traffic.

• Traditional code to AI native systems: behavior is non deterministic
  AI agents and autonomous services invoke APIs and tools on behalf of users. Inputs can carry adversarial prompts and outputs vary by context. Signature based tools do not understand prompts, model responses, or agent intent. Machine to machine calls are often implicitly trusted. This enables prompt injection, data poisoning, confused deputy scenarios, and AI assisted exfiltration, while rigid controls risk blocking legitimate automation.

• The operational reality: legacy blockers force a tradeoff
  Without runtime visibility, behavioral baselines, or application context, WAFs cannot reliably distinguish attacks from normal API usage. Teams are forced to choose between aggressive blocking that breaks customer experience or log only mode that leaves APIs exposed. In practice, only about 47% of WAFs run in blocking mode. The cost is constant tuning, alert fatigue, incident noise, and limited risk reduction.

Why runtime informed API protection is required

Modern API security must be grounded in runtime behavior, not static rules. Protection needs full visibility into how APIs are used, who calls them, what data moves, and how behavior changes over time.

Levo’s API Protection Module delivers this by using eBPF based runtime visibility to automatically discover and protect every API, internal and external, without gateways or manual configuration. Enforcement is based on per endpoint behavioral baselines derived from real traffic and data exposure, enabling high precision blocking without breaking applications. Decisions are explainable, auditable, and adaptive, eliminating the false choice between security and availability.

This shift from perimeter blocking to runtime enforcement is what makes API protection viable for modern cloud and AI native systems.

Key Steps to build an effective API Protection Strategy

An effective API protection strategy is not a product rollout. It is an operating model that keeps protection accurate, low latency, and always on, even as your APIs change every sprint.

1. Start with complete API visibility. Inventory every API, including internal services, partner integrations, and legacy endpoints that still respond. You cannot protect what you cannot see, and API blind spots are where most failures begin.
   
2. Classify risk based on data and business function. Identify which APIs touch regulated data, money movement, identity, and admin functions. Those APIs deserve stricter enforcement, tighter monitoring, and faster remediation workflows.
   
3. Establish baseline behavior per endpoint. Move beyond generic rules. Define what normal looks like for each endpoint: typical methods, payload shape, caller identity patterns, and usage volume. This is how you catch abuse that looks like valid traffic.
   
4. Combine contract enforcement with abuse detection. Use schema and contract validation to stop unexpected fields and payload drift. Pair it with behavioral detection to catch enumeration, workflow abuse, and multi step attacks.
   
5. Design runtime enforcement that the business can trust. If your runtime controls break customer journeys, teams will put them in detect only mode. Your strategy must prioritize high precision blocking and fast rollback controls so enforcement stays enabled.
   
6. Extend protection beyond the perimeter. Modern risk is not only north-south traffic. Service to service calls and internal APIs carry sensitive actions. Your strategy should include visibility and control for east-west traffic where it matters most.
   
7. Integrate protection into the SDLC. Run protection logic in staging in monitor mode to surface false positives before production. Use production learnings to improve tests and policies, so every incident makes the system stronger.‍
8. Operationalize ownership and response. Define who owns each API, who approves enforcement changes, and how quickly the team can unblock false positives. Without this, protection becomes a bottleneck or gets bypassed.

KPIs to measure API Protection

The KPIs that matter most are the ones that prove your protection is both effective at stopping attacks and safe to run in blocking mode without hurting customers or velocity.

Accuracy and trust KPIs

• False Positive Block Rate (FPBR)
  Measures how often legitimate API calls are blocked. This is the fastest way to detect revenue friction caused by security. Track it per critical endpoint (login, checkout, account, partner APIs) and trend it after releases.
• False Negative Rate (FNR)
  Measures how often real attacks are not blocked. High FNR means you are paying for protection that attackers can routinely bypass, especially with logic abuse that looks like normal API traffic.
• Balanced accuracy (or equivalent combined score)
  A single score that rewards protection that catches attacks without blocking users. Useful for comparing tools and tuning profiles when FPBR and FNR trade off against each other.
• Percent of endpoints in blocking mode vs monitor mode
  This is a trust metric. If large portions of your APIs remain in monitor mode, the organization is signaling that the tool is not safe enough to enforce, so it is not delivering real protection.
• Mean Time to Unblock (MTTU)
  How quickly you can reverse a false block on a critical API. This is your ability to prevent a false positive from becoming an outage or a weekend long revenue loss.

Performance and reliability KPIs

• Average latency introduced (ms per request)
  Even single digit milliseconds matter because modern apps chain multiple API calls per user action. Track p50, p95, p99 overhead, not just averages.
• Throughput capacity at peak load (RPS)
  Measure the maximum sustained traffic before queuing, drops, or throttling. If the security layer becomes the choke point, it becomes an availability risk during spikes and events.
• Fail open incidents and duration
  Fail open keeps the app up but creates a protection gap. Track frequency, minutes exposed, and whether monitoring still continued during the event.
• Fail closed incidents and downtime minutes
  Fail closed blocks all traffic and creates a self-inflicted denial of service. Track frequency, blast radius, and rollback time because it directly maps to business impact.
• Uptime or downtime attributable to the protection module
  A simple executive KPI: how often the security control itself causes degraded performance or outages. Your protection layer should not drag down your SLA.

Operational cost and adoption KPIs

• Number of exception and bypass rules maintained
  A growing exception list is operational debt and growing risk. It often indicates the tool cannot adapt to the app, so humans keep poking holes to keep production running.
• Deployment delay frequency caused by protection
  How often releases are delayed or rolled back because the protection module blocks new intended behavior. If this is common, security is slowing delivery, and teams will eventually route around it.
• Percent of false positive blocks tied to new releases
  Shows whether CI CD is outpacing your protection tuning. A spike here is a signal to integrate better pre production validation and automated learning workflows.
• Developer hours spent debugging security blocks
  Quantifies the hidden tax on engineering time. If this grows, your protection is not invisible, and trust will deteriorate even if security leaders like the dashboards.
• SOC burden from blocks (incident volume and analyst time)
  Track how many blocks escalate to investigations and how long false positives take to triage. The best protection blocks real attacks quietly and only escalates high fidelity events.

Business impact KPIs

• Customer transaction failure rate due to security blocks
  This is the KPI CEOs care about most. Measure what percentage of key journeys fail because the protection layer blocked them, then tie it to abandoned flows and support tickets.

Shadow bypass behavior
How often teams request or implement unofficial bypasses. If people are turning off protection to ship, the control has failed culturally and operationally, even if it looks enabled on paper.

Best Practices for effective API Protection

Below are some of the best practices to follow:

• Choose protection that understands your APIs, not just web traffic. API first systems use JSON heavy payloads and newer protocols like GraphQL, gRPC, and WebSockets. Your protection layer needs to properly parse and enforce on what you actually run, otherwise it is effectively blind in the places attackers hide.
• Make encrypted traffic inspectable without turning security into a bottleneck. With HTTPS everywhere and mTLS inside the environment, protection must see requests after decryption. Do this with smart placement (where TLS terminates) or modern approaches that regain visibility inside the host without forcing complex break and inspect patterns.
• Enforce the contract using schemas and specifications. Use OpenAPI and schema validation to define what valid requests look like and block unexpected fields, types, and malformed structures. Contract enforcement is one of the fastest ways to stop abuse and reduce ambiguity across teams.
• Detect logic abuse with identity and behavior context. Many API attacks look valid per request. Effective protection correlates sequences and identity signals to catch patterns like enumeration, token misuse, and mass extraction. Prioritize controls that track by user and token, not just IP.
• Continuously discover endpoints and eliminate shadow exposure. In fast moving environments, undocumented or newly released endpoints are where risk concentrates. Maintain a living inventory of APIs in use, flag unknown endpoints, and update baselines as APIs evolve.‍
• Integrate with CI/CD so new endpoints are protected from day one. The practical measure of readiness is “time to protect new endpoints.” The best programs reduce it to near zero by integrating protection with delivery workflows and applying baseline controls immediately, then tightening policies as usage becomes clear.

Challenges in API Protection

Some of the challenges that can occur:

1. Protection that only sits at the edge misses real attacks
   In cloud and microservices, a lot of API abuse happens after an attacker gets inside. Legacy perimeter WAF models mainly see north south traffic, but they often miss east west service to service API calls. That means the platform can look protected while internal APIs remain a blind spot where attackers can pivot and exploit.
   
2. Too many APIs, too little consistent enforcement
   Microservices multiply endpoints fast, including internal APIs that used to be hidden in a monolith. More endpoints means more places to misconfigure auth, validation, or data handling, and it becomes harder for a single protection layer to apply consistent blocking across the entire API surface.
   
3. New endpoints ship faster than protection can catch up
   In CI/CD environments, services appear and change constantly. If your protection module depends on manual onboarding or rule updates, new APIs can run in production unprotected for days. “Time to protect new endpoints” becomes a direct measure of how long you are exposed after every release.
   
4. Inspection limits create a choice between bypass risk and broken UX
   Many WAF and cloud WAF services inspect only part of the request body for performance. If they scan a limited window, attackers can hide malicious content beyond it and slip through. If you compensate by blocking large payloads, you risk false positives that break legitimate API calls, which turns security into customer friction and lost transactions.
   
5. Fail open and fail closed both undermine protection outcomes
   When the protection layer fails or overloads, fail open keeps the app up but removes blocking when you need it most. Fail closed preserves security posture but can take your APIs offline by blocking legitimate traffic. Either mode translates into a business incident: a security gap or an availability outage.
   
6. Static rule sets cannot keep up with API logic abuse
   Many API attacks are not obvious payload exploits. They are valid looking requests used maliciously, like enumeration and authorization bypass patterns. Static signatures and generic managed rules struggle to catch these, so teams either miss attacks or over block and lose trust in blocking mode.
   
7. Protection tooling becomes an operations tax, so teams turn it off
   If a module requires constant tuning, exception lists, and emergency bypasses to keep production stable, it stops being protection and becomes overhead. The common failure mode is predictable: teams leave it in monitor mode to avoid false positives, and the organization ends up with alerts instead of prevention.
   

Distributed environments demand distributed protection, which legacy tools cannot deliver
To actually block attacks across microservices, protection often needs to run closer to workloads, inside clusters and meshes, not just at a central gateway. Legacy approaches struggle to enforce consistently inside the mesh without adding latency, complexity, or gaps, which is why many API programs end up partially protected by design.

How to choose the right API Protection Tools

The fastest way to pick the right API protection tool is to judge it like a business critical control: does it stop real attacks without blocking revenue traffic, adding latency, or creating a constant operations tax on your teams.

1. Start with what “good” actually looks like. A well functioning protection module strikes a critical balance between strong detection and minimal false blocking. It identifies real threats with high accuracy while allowing legitimate transactions to flow uninterrupted, ensuring the system neither blocks too much nor lets real attacks slip through.

Look for these traits:

• Adaptive and context aware behavior that learns normal API patterns and uses identity and sequence context, not just simple signatures.
• Minimal performance impact and high reliability, including handling spikes gracefully and avoiding security caused outages.
• Trusted and actually used in blocking mode across critical apps, because the organization has confidence it blocks real threats without breaking the business.
• Actionable insight, like highlighting deprecated endpoints still being hit or spotting data leakage patterns so you can fix the underlying issue, not just block forever.

2. Treat false positives and false negatives as executive metrics. A poor protection module usually fails in predictable ways: it blocks legitimate calls (false positives), misses real attacks (false negatives), or swings between both, which destroys trust and forces teams into log only mode or bypass behavior.

From a CEO perspective, false positives are not “security noise.” They are revenue leakage and customer friction, and even a small failure rate can be material at scale.

What to demand in evaluation:

• Measured false positive and false negative behavior under realistic traffic and attack simulation, not marketing claims.

3. Eliminate tools that require constant babysitting. If your security team is constantly tweaking rules, adding exceptions, and troubleshooting week after week, you are paying an ongoing operational tax rather than gaining real protection. Long lists of bypass rules and frequent manual intervention are clear signs of a poorly functioning module and, over time, create a Swiss cheese effect that weakens security posture instead of strengthening it.

4. Verify it keeps pace with modern APIs and constant change. Security controls that cannot adapt to new threat patterns or evolving API styles quickly turn into blind spots. Signature driven approaches often miss emerging attacks, and tools that fail to understand newer API paradigms can leave portions of the business effectively unprotected.

5. Pressure test performance and failure behavior. Security that slows the product is still a business risk. Every added control introduces overhead, so latency, throughput limits, and how the system behaves during failures matter. Fail open or fail close behavior directly affects availability and must be evaluated as part of any real security decision.

6. Choose tools that improve posture over time. The best platforms do not just block. They help you get safer by surfacing insights you can act on, like exploited endpoints and sensitive data exposure patterns, so engineering can fix root causes and reduce recurring risk.

Top API Protection Tools

Below is a practical list of widely used tools/platforms that cover portions of API protection.

1. Levo.ai
Levo.ai provides full lifecycle API protection by combining continuous discovery, behavioral analysis, and runtime security. Instead of relying on static rules or signatures, it uses real time traffic context to identify authorization flaws, sensitive data exposure, and logic abuse before and after APIs go live. This makes it especially effective for fast moving, API driven businesses.

Key Features:

• Continuous discovery of internal, external, and shadow APIs
• Behavioral detection of authorization and logic abuse
• eBPF powered telemetry with no agents or latency impact
• Protection across development, pre production, and runtime
  

2. Traceable.ai
Traceable.ai focuses on protecting APIs in production by analyzing live traffic and identifying abnormal behavior. It is well suited for security operations teams that need real time visibility into active attacks, misuse, and data exfiltration attempts.

Key Features:

• Runtime API discovery and monitoring
• Behavioral anomaly detection and threat correlation
• Centralized dashboards for SOC investigation
• Strong integration with SIEM and incident response tools
  

3. Salt Security
Salt Security specializes in identifying and stopping API attacks in production environments. It is particularly strong at detecting sophisticated abuse patterns once APIs are live, though it offers limited protection earlier in the lifecycle.

Key Features:

• Production traffic analysis and anomaly detection
• API abuse and attack pattern identification
• Forensics and investigation capabilities
• SOC friendly alerting and workflows
  

4. Akamai

Akamai protects APIs at the edge using its global CDN, WAF, and DDoS mitigation capabilities. It is highly effective against volumetric attacks, bots, and availability threats, but has limited visibility into internal API logic and authorization behavior.

Key Features:

• Edge level API traffic filtering
• DDoS and bot mitigation at scale
• Integrated WAF protection for exposed APIs
• High availability and resilience
  

5. Qualys
Qualys extends its vulnerability management and attack surface capabilities to APIs, helping organizations understand exposure and compliance risk. While not a true runtime protection tool, it supports API protection programs through visibility and policy enforcement.

Key Features:

• API asset discovery and inventory
• Policy based risk identification
• Compliance and reporting dashboards
• Integration with broader vulnerability management workflows

Why Levo.ai Is the Right API Protection Platform for 2025

In 2025, APIs are how revenue moves, partners integrate, and customer trust is earned or lost. The right protection platform has to stop real attacks without slowing delivery, flooding teams with noise, or forcing a one size fits all policy across every service.

Levo delivers that balance.

It comes with out of the box coverage for the OWASP API Top 10 like broken object level authorization, mass assignment, and injection, then lets your engineers extend protection with Python, YAML, or Lua rules to capture the edge cases that are unique to your business.

It also gives you granular control at the endpoint and environment level: block in production, log in staging, suppress noise in test, and enforce company specific requirements like custom headers, correlation IDs, or regulatory triggers without redeploying services.

When Levo blocks an exploit, it goes beyond a security alert. It pinpoints the vulnerable endpoint and missing control, and provides practical remediation guidance so teams fix the root cause quickly and avoid repeat incidents.

Finally, Levo is designed to focus friction where it pays off. It identifies APIs handling sensitive data like PII or PHI, scores endpoints by risk, and automatically tightens enforcement on high risk flows. In Kubernetes and service mesh environments, it can run as a sidecar per microservice, so each service gets the right policy without introducing bottlenecks.

And instead of staying static, Levo gets better over time: blocked attacks can be converted into new pre production tests and fed back into detection, steadily improving coverage and reducing alert fatigue as your environment evolves.

How to achieve total API Protection with Levo?

Legacy API protection was built for a different era. Traditional WAF and WAAP stacks often depend on inline chokepoints, traffic mirroring, or full payload collection so they can ship data out for analysis and policy decisions. That approach creates the tradeoffs security leaders know too well: added latency, rising egress and infrastructure costs, operational complexity, and a compliance headache when sensitive data crosses boundaries.

Levo flips that model with a privacy preserving security architecture designed to protect APIs without slowing the business.

Levo’s protection module avoids traffic mirroring, full payload ingestion, and heavyweight inline deployments that strain production systems. Instead, Levo uses eBPF based sensors to passively observe API traffic across environments, including encrypted flows. That traffic is processed locally inside the Levo Satellite, which can be self hosted or deployed quickly wherever you run. Once analysis is complete, the traces are discarded.

Only sanitized metadata and OpenAPI specifications leave production. No raw payloads. No sensitive fields. Nothing that violates data residency requirements.

Most importantly, enforcement decisions happen entirely within your infrastructure, with no round trip to an external SaaS. That is the fundamental difference from legacy vendors that mirror traffic to the cloud before acting. The result is near zero added latency, real time protection that is realistic for highly regulated industries, lower egress costs, and a deployment model that security teams can adopt without dragging down developer velocity.

Conclusion

API security cannot be a collection of disconnected point tools and perimeter filters. Modern applications are dynamic systems where APIs constantly change, traffic is encrypted, identities are often non human, and real risk emerges at runtime. Levo is built for this reality as a Runtime Application Security Platform that brings together capabilities teams usually buy separately, including discovery, monitoring, testing, detection, sensitive data controls, remediation, and inline protection, so the business stays protected without slowing delivery.

Levo unifies API security into a single, coherent platform that protects APIs from the earliest stages of development through runtime. It starts with continuous API discovery and inventory to establish visibility, then applies documentation, testing, and vulnerability reporting to catch issues early. As APIs move into production, Levo adds monitoring, sensitive data discovery, and real time detection, backed by inline protection and remediation. The result is full lifecycle API security that evolves with the application, not a standalone blocking layer bolted on at the edge.

The outcome is what ultimately matters: real threats are blocked inline while customer experience and developer velocity remain intact. Sensitive data stays protected without sending payloads to a third party cloud, and teams maintain a tight feedback loop from exploit observed in the wild to fix implemented in code. Levo.ai makes this possible by delivering full lifecycle API protection built for modern, fast moving applications.

See how Levo.ai delivers full lifecycle API protection in practice. Book your demo to understand what modern API security should look like.