The 9 Best API Security Tools

Most teams reach this category after one of two conversations. Either someone asked how many APIs the company exposes and nobody could answer, or a test report came back with an endpoint that returned another customer's records to an attacker who changed one number in a URL. Vendors sell those as one product. They are not the same problem, and few tools are good at both.

What separates these tools is first where they get their data. A platform reading mirrored traffic sees only what crosses the mirror, one sitting inline only what it terminates, one built on eBPF the service-to-service calls that never touch an ingress, one built on the OpenAPI contract only what you documented. Each has a blind spot, usually where the interesting endpoint lives. The second split is what the tool does with that data: catalog, alert, block, or test.

That second split deserves a blunt statement. Broken object level authorization, with its function level and property level siblings, is the dominant real-world API failure and is how breaches actually happen. Yet nearly everything here is better at discovering that an endpoint exists than at proving its authorization is correct. This article covers nine tools, each strongest in a specific situation, and says which get near that question.

What actually matters when choosing #

Start with data source, because it sets your blind spots and you cannot tune your way out of one. Sketch your estate honestly: public edge, internal east-west traffic, partner integrations, functions with no gateway in front. Then ask which of those a collection method reaches. Excellent detection over half your traffic still puts a green dashboard over the rest.

Second, ask what the tool can prove about authorization. Most answer indirectly, flagging behavior that resembles enumeration after the fact. A few test it directly, replaying requests under different role credentials and comparing the results. A program with only the first detects a breach in progress rather than preventing one.

Third, be realistic about the operating model. Inline protection needs the team that owns ingress. Behavioral detection needs someone reading alerts. Discovery needs someone mapping endpoints to owning teams.

The obvious criterion, endpoint count from a proof of concept, is nearly useless: every tool here finds more APIs than you expected. That is a conversation starter, not a differentiator.

How these were selected #

These are scenario picks, not a ranking. Each entry names a distinct situation where that tool is the strongest answer, so position says nothing about quality: broadly applicable platforms come first, specialists later. No vendor funded inclusion and there is no paid placement. The right choice depends on your architecture and who will operate the tool, not on a score.

Akamai API Security (Noname) #

Best for: building a first authoritative inventory across a sprawling, mixed-hosting estate

The mechanism is out-of-band analysis. Collectors pull from API gateways, load balancers, cloud provider logs and mirrored traffic, then reconstruct an endpoint catalog with parameter-level detail, classify which endpoints handle sensitive data, and score posture against problems such as missing authentication, weak transport settings and overly verbose responses. Because nothing sits in the request path, there is no added latency and no outage risk if the analyzer fails, so platform teams approve it faster than an inline option. It wins when your APIs sprawl across two clouds, a legacy gateway and undocumented services.

The caveat follows from that architecture: observing is not stopping. Blocking means wiring findings into an enforcement point you already own. The second caveat is organizational: the first inventory arrives as a large set of endpoints with no owner attached, and without a mapping from endpoint to team it becomes a dashboard nobody opens.

Salt Security #

Best for: catching patient, low-volume enumeration that looks normal request by request

Salt mirrors traffic to a cloud engine and correlates one actor's behavior across long time windows instead of scoring each request in isolation. An attacker walking object identifiers, cycling parameter values and probing endpoints over days produces traffic that is individually unremarkable and only becomes visible when stitched together across sessions and identities. It is the closest thing here to detection built for authorization reconnaissance, paired with posture governance over the inventory.

Cloud-side correlation means mirroring traffic, including request and response bodies, into a vendor environment, which is a data residency conversation with privacy and legal counsel before it is a technical evaluation. The behavioral engine also needs a learning period before its output is worth acting on, and even then you get a signal about behavior, not evidence that an endpoint's authorization logic is wrong. Someone still has to read the code.

Traceable AI #

Best for: tying every finding to full request context and the identity behind it

Traceable builds on distributed tracing rather than edge sampling. Spans are collected in process or through service mesh sidecars, so a flagged request arrives with the downstream calls it triggered, the authenticated identity that made it and the data classes it touched. That context turns an unusual-access alert into something a responder can act on: you can follow the request and see whether the caller read a record belonging to someone else. It covers the common server-side runtimes.

The caveat is deployment weight. This is the heaviest footprint in the category, and coverage is exactly as good as instrumentation. Getting agents or sidecars into every service is a platform project measured in quarters, with real resistance from service owners who did not ask for it. Partial instrumentation gives partial truth, which is worse than an acknowledged gap because the resulting map looks complete.

Wallarm #

Best for: a single inline enforcement point covering web and API traffic together

Wallarm's detection is grammar based rather than signature based. It parses payloads structurally, decoding nested encodings and serialization formats until it reaches the actual content, then decides whether that content is an attack. This holds up better than pattern matching against the usual evasions. It deploys inline in front of your services, commonly as an NGINX or Envoy module or a Kubernetes ingress component, so it can block rather than alert, derives an inventory from the traffic it terminates, and replays recorded attacks to check exploitability.

The caveat is that inline is a commitment. It lives in the request path, so capacity planning, failure behavior and upgrade windows become production concerns, and whoever owns ingress owns this too. Detection stays payload centric: strong on injection, weak on authorization failures, because a request reading another tenant's record is perfectly well formed.

Cequence Security #

Best for: public APIs under sustained automated abuse rather than exploit attempts

Cequence pairs external attack surface discovery with behavioral analysis aimed at the abuse problem: credential stuffing, scraping, gift card fraud, account takeover, inventory hoarding. These differ from vulnerability exploitation because the attacker uses the API exactly as designed, at machine scale, as the wrong person. Signatures do not help, so the platform leans on client fingerprinting and behavioral models that survive an adversary rotating through fresh infrastructure. If your API is the product, this is the threat model that actually hurts.

The caveat is that bot defense is an arms race, and buying it commits you to operating it. Someone must review what got blocked, tune thresholds and respond when the adversary retools; left alone the models drift and you block real customers or catch nothing. It is purpose built for abuse: if your concern is broken authorization in internal APIs, the emphasis points elsewhere.

Imperva API Security #

Best for: adding API coverage to an estate already fronted by Imperva

The mechanism is reuse of a data path you already run. Traffic flowing through the Imperva WAF and CDN is analyzed to discover endpoints, classify which handle sensitive or regulated data, and enforce a schema so non-conforming requests are rejected before reaching the application. Because the enforcement point is already deployed and already owned, time to first coverage is short. No new agent, no new network hop, no fresh argument with the platform team.

The caveat is that same sentence read backwards. Coverage ends where the Imperva path ends, so internal service-to-service traffic, partner integrations routed differently and anything behind another provider stay invisible. Schema enforcement is only as good as the specifications you supply, and a stale spec blocks legitimate traffic in a way that looks like an outage. Assessed standalone, it is less differentiated than the discovery specialists.

42Crunch #

Best for: teams practicing design-first APIs who want the contract enforced end to end

42Crunch treats the OpenAPI definition as the artifact under test. It audits the definition for weak security schemes, missing constraints and permissive types while the developer is still writing it, scans the running endpoint to check the implementation conforms to what was promised, then enforces that same definition at runtime with a firewall that rejects non-conforming requests and responses. One artifact, three enforcement points. Response-side enforcement is underrated: constraining what an endpoint may return is a real control against accidental data exposure.

The caveat is that the approach rests entirely on specification discipline. Teams whose specs are generated after the fact, partial or drifted from the code get rigorous audit results about a fiction. A high audit score describes the definition, not the implementation. Conformance also says nothing about whether the right user is being served, since a perfectly valid request can still return another customer's record.

APIsec #

Best for: automated authorization testing before release rather than detection afterwards

APIsec generates test cases from your specification and executes them under several sets of role credentials, then compares what each role could actually reach. That is a direct attack on the problem the rest of this category works around. Running the same request as an administrator, a standard user and an unrelated tenant turns "is this endpoint properly protected" from an inference drawn from traffic patterns into a question with a reproducible answer. It runs in a pipeline, so the answer arrives before the code ships.

The caveat is that it only tests what you describe and configure. Undocumented endpoints go untested, and if the role accounts do not own genuinely distinct data, everything passes and the pass means nothing. Generated tests also miss domain rules no specification encodes: who may approve a refund, who may read a case file after it closes. Expect ongoing fixture maintenance.

Levo.ai #

Best for: mapping APIs and sensitive data flows in Kubernetes without touching application code

Levo uses eBPF sensors to observe socket level traffic from the kernel, so it catalogs endpoints, including internal service-to-service calls that never cross an ingress, without any developer adding a library or redeploying with an in-process agent. That removes the largest source of friction in getting instrumentation adopted at all. It traces which endpoints carry regulated data types through the call graph, giving a data flow map rather than a flat list, and seeds tests from captured traffic so they reflect real request shapes.

The caveat is that eBPF requires privileged access and kernel compatibility, so adoption is a conversation with the platform team, not a security-team decision. It is a younger product with a smaller integration ecosystem than the established platforms. And discovery from live traffic only sees what runs: an admin endpoint invoked once a quarter stays invisible until that quarter.

How to choose #

If you cannot say how many APIs you expose and your estate spans several clouds and gateways, start with Akamai API Security.

If your estate is mostly Kubernetes and you want internal traffic mapped without asking developers for anything, use Levo.ai.

If test reports keep coming back with broken object level authorization, start with APIsec and treat the rest as secondary.

If you already run Imperva at the edge, start there and put the saved effort into authorization testing.

If your public APIs are being scraped, stuffed or defrauded, start with Cequence Security; if you need to block attacks now and own the ingress tier, start with Wallarm.

If your organization genuinely practices design-first API development, 42Crunch fits how you already work.

If you already emit distributed traces, Traceable AI extracts the most from that investment; if your worry is a quiet adversary already inside and mapping you, Salt Security is built for it.

What these tools will not do for you #

None of them will tell you whether your authorization logic is correct. Discovery is close to commoditized and behavioral detection is useful, but the dominant real-world API failure is an authenticated user reaching data belonging to someone else, and that defeats automation structurally: the tool does not know which user is supposed to see which record. Only your application knows, and if it gets that wrong the tool has nothing to check against. Tools that test under multiple role credentials get closest, and still cover only the cases you configured. What closes this gap is architectural: authorization enforced centrally rather than reimplemented per handler, tests written by people who understand the domain, and code review aimed at access checks.

Second, inventory is not ownership. A discovery platform hands you a list; if nothing maps each endpoint to a named team, nothing gets fixed and you have bought a description of your problem.

Third, runtime detection only detects. It assumes someone reads the output and can act, which means alert routing, an on-call path, and blocking you control.

Frequently asked questions #

Do I still need one of these if I already have a WAF?

Usually yes. A WAF inspects payloads for attack patterns and is reasonable on injection classes, but it has no model of your endpoints, schemas or which identity should reach which object, so it cannot see an authenticated user enumerating valid identifiers. If your WAF vendor offers API capabilities on the same data path, start there.

Is discovery alone worth buying?

For most organizations, yes, as a first step. You cannot protect or test an inventory you do not have, and forgotten endpoints from deprecated services cause real incidents. But discovery without ownership mapping and authorization testing produces a thorough list of things nobody is fixing.

Can any of these actually find broken object level authorization?

Partially. Tools that execute tests under several role credentials can prove an endpoint lets the wrong role reach a given object, which is real evidence. Behavioral platforms flag the access patterns that accompany enumeration. Neither knows your business rules, so treat both as coverage of the obvious cases and rely on design review for the rest.

Should protection sit inline or out of band?

Out of band is easier to get approved, adds no latency and cannot take production down, but it only observes. Inline can block and owns a share of your availability risk. A sensible sequence is out of band first, to learn what normal traffic looks like, then inline enforcement on high-value endpoints.