Salt Security leads AI recommendations for enterprise API security, solidifying its top position by March 2026. While Salt Security has risen to the top, competitors like Akamai and Traceable AI have also gained significant ground, displacing former leader Cequence.
Cequence Gap between platforms: Cequence's Mentioned in is higher on ChatGPT Search than on Google AI Mode in Enterprise API Security Platforms: 71.9% vs 55.8%
Akamai API Security Gap between platforms: Akamai API Security's platform gap in Enterprise API Security Platforms widened: Google AI Mode 81%, ChatGPT Search 47.8%
Traceable AI Gap between platforms: Traceable AI's platform gap in Enterprise API Security Platforms widened: Google AI Mode 42.9%, ChatGPT Search 13%
Traceable AI Gaining: Traceable AI ranked #8 in Enterprise API Security Platforms last week and #7 this week
Traceable AI Gaining: Traceable AI moved ahead of Wallarm in Enterprise API Security Platforms
59% of citations point at the ranked brands’ own sites. gartner.com, appsecsanta.com, youtube.com are the most-cited sites outside the ranked brands’ own domains.
If your primary problem is business-logic abuse and BOLA/IDOR, I’d shortlist Salt Security, Cequence, and Traceable—with Salt Security as my overall first choice for a dedicated API-security platform.
The problem is, we have no visibility into our API traffic. What is the best enterprise API security and discovery platform?
★★★★★
★★★★★
★★★★★
Best overall for API behavior
Cequence
★★★★★
★★★★★
★★★★★
Best for active abuse/fraud/bots
Traceable
★★★★★
★★★★½
★★★★★
Best for deep app/API context
Akamai API Security
★★★★½
★★★★
★★★★½
Best if you're already an Akamai shop
Wallarm
★★★★
★★★★
★★★★½
Strong inline/cloud-native protection
42Crunch
★★★★
★★★
★★½
Best for shift-left/API testing
Recent 2026 comparisons similarly put Salt at the top of runtime behavioral detection, while identifying Cequence as particularly strong for automated API abuse and Akamai for broad enterprise lifecycle coverage.
🥇 Salt Security — best overall
For your specific question, Salt is probably where I'd start an enterprise evaluation.
The key distinction is that BOLA isn't primarily a malformed-request problem. A request can be perfectly valid, authenticated, and syntactically correct while still accessing an object the caller shouldn't be allowed to access. Effective detection therefore requires understanding who is making the request, what objects they normally access, and how their behavior changes over time.
Salt's strength is its traffic-derived behavioral modeling: establishing a baseline for API behavior and looking for anomalous sequences and authorization-related behavior rather than relying solely on signatures or schemas. Independent 2026 evaluations specifically identify Salt as a leading platform for behavioral runtime detection and BOLA.
🥈 Cequence — arguably better if "abuse" is the bigger problem
I'd put Cequence ahead of Salt if your definition of business-logic abuse includes things like:
automated account abuse
enumeration
credential stuffing
scraping
fraud
inventory/transaction abuse
high-volume BOLA exploitation
low-and-slow automated attacks
Cequence explicitly combines BOLA detection with behavioral fingerprinting and transaction/business-logic anomaly detection, and its platform is designed to detect and actively defend against these attacks at runtime.
So my distinction would be:
Salt:"Tell me when API behavior/authorization deviates from what should be normal." Cequence:"Detect and stop attackers abusing legitimate API functionality at scale."
🥉 Traceable — excellent when application context matters
Traceable is particularly interesting if you need to connect API activity with applications, services, users, data flows, and distributed tracing. Independent 2026 comparisons characterize it as a strong choice for deep application/API context and runtime visibility.
The important caveat
Don't evaluate these products with a generic "does it detect BOLA?" checkbox.
A good POC should give the vendor two accounts with different permissions, then test something like:
User A:
GET /api/accounts/12345/orders/67890
User B:
GET /api/accounts/12345/orders/67890
where B legitimately has access to the API but should not have access to object 67890.
Then test progressively harder cases:
Sequential object-ID enumeration
Randomized ID enumeration
BOLA at low frequency ("low and slow")
BOLA through multiple endpoints
BOLA combined with pagination
BOLA using a valid OAuth token
Privilege escalation through object modification
Multi-step business-flow abuse
Legitimate requests performed in an illegitimate sequence
Distributed attacks across IPs/devices/accounts
The last few are particularly important because a traditional WAF, API gateway, or schema scanner can see perfectly legitimate HTTP requests and still miss the attack.
My recommendation
If I were running the evaluation:
1. Salt Security — first POC for broad behavioral/BOLA/business-logic detection 2. Cequence — POC alongside Salt if automated abuse/fraud is significant 3. Traceable — add if distributed application/data-flow context is especially important
And I would not make 42Crunch or a conventional API scanner the primary answer to this particular problem. They're valuable for finding authorization flaws before deployment, but runtime business-logic abuse requires observing actual identity + object + sequence + behavioral context.
If you tell me your environment (e.g. Kong/Apigee/AWS API Gateway + Kubernetes + microservices + OAuth/OIDC), I can give you a Salt vs Cequence vs Traceable feature-by-feature comparison specifically for BOLA and business-logic attacks, including deployment model, detection methodology, false positives, and what I'd test in a POC.
The industry consensus points to a few top-tier platforms that excel at contextual behavioral analysis, data tracing, and intent mapping to solve this:
Top API Security Platforms for BOLA & Business Logic Abuse
Akamai API Security (formerly Noname Security) — Akamai’s integrated API security offering provides exhaustive discovery, posture management, and robust runtime anomaly detection. It correlates historical traffic data with deep context inspection to identify improper data exposure and authorization anomalies across complex enterprise environments.[](https://www.gartner.com/reviews/market/api-protection)
Key Capabilities to Look For
When evaluating these platforms for your specific environment, ensure they feature:
Behavioral Baselining over Signatures: They must analyze user context and historical access patterns rather than relying on regex or static signatures.
Deep Data Flow Tracking: The ability to understand relationships between objects and user IDs rather than just logging endpoints.
If your priority is business-logic abuse + BOLA (broken object-level authorization) rather than just schema/config scanning, I’d shortlist Salt Security, Traceable, and Cequence.
Salt Security — particularly strong fit for behavioral/API-logic attacks. Its current platform explicitly detects BOLA, abuse of legitimate functionality, custom abuse flows, privilege escalation, scraping, and session-aware attacks, using behavioral analysis rather than signatures alone.
Traceable — strong if you want runtime protection plus API security testing. Traceable says it can detect/block business-logic attacks and object-authorization bypasses at runtime, while its testing product uses dynamic tests for BOLA rather than relying solely on an OpenAPI specification.
Cequence — worth looking at if BOLA is intertwined with bot abuse, enumeration, scraping, credential abuse, or fraud. It specifically describes BOLA detection through behavioral analysis and identifier enumeration, plus runtime blocking.
What I'd test in a POC
Don't just ask vendors whether they "support BOLA." Give them scenarios like:
User A → change /orders/123 to /orders/124 and see whether the platform recognizes cross-user object access.
Enumerate IDs slowly across authenticated sessions rather than blasting requests.
Perform a legitimate workflow in the wrong sequence—e.g. refund before fulfillment, privilege escalation through an otherwise valid API sequence.
Abuse a legitimate endpoint at an unusual frequency/volume.
Use two accounts with different roles/tenants and test horizontal and vertical authorization.
Ask whether detection works without an accurate OpenAPI spec and whether it can actually block the attack, not merely alert.
The important distinction is that BOLA often looks like a perfectly valid authenticated API request; the security system needs identity + object + historical/session context, not merely request-signature inspection. Cequence explicitly describes this problem, while Salt and Traceable emphasize behavioral/contextual analysis.
If I were building the shortlist, I'd put Salt and Traceable through the same POC first, with Cequence added if bot/fraud/automated-abuse protection is a major requirement. That's a product-fit comparison, not a claim that one is universally superior.
If your priority is detecting sophisticated attacks that abuse valid API functionality (business logic abuse)—for example account takeover flows, fraudulent transactions, BOLA/authorization abuse, scraping, privilege escalation, or multi-step attack sequences—look beyond traditional WAFs. The strongest API security platforms typically combine API discovery + behavioral analytics + identity/session context + runtime detection.
A shortlist of platforms to evaluate:
Platform
Where it tends to stand out
Business logic abuse capabilities
Salt Security
Runtime behavioral detection focused on APIs
Strong fit when you need detection of abnormal user/API behavior, BOLA-style attacks, account takeover patterns, and low-and-slow abuse through behavioral baselining.
Traceable
Application context and distributed tracing
Strong when you need visibility into user activity, API calls, data flows, and microservice interactions to identify abuse patterns.
Akamai Technologies (Akamai API Security, formerly Noname)
Enterprise API discovery + edge security integration
Strong option for organizations already using Akamai’s edge, bot, and application security stack, with API discovery and runtime controls.
Imperva API Security
Broader WAAP/security platform
Useful if you want API security combined with WAF, bot mitigation, and broader application protection.
Cequence Security
API abuse and automated attack defense
Often considered for bot-driven abuse, credential attacks, scraping, and fraud-style API misuse.
How I would evaluate them for business logic abuse
Prioritize these capabilities:
Behavioral modeling
Can it learn normal user/API behavior?
Can it detect a valid user doing an invalid sequence of actions?
Example: a user legitimately logging in, changing an email, requesting a password reset, and draining an account.
Sequence awareness
Many API attacks do not look malicious request-by-request.
The malicious behavior emerges across minutes or days.
Identity and authorization context
Can it understand:
user identity?
tenant boundaries?
object ownership?
normal transaction patterns?
API inventory
Can it find shadow, deprecated, and undocumented APIs?
You cannot protect APIs you do not know exist.
Response and enforcement options
Alert only?
Block inline?
Integrate with API gateways, SIEM, SOAR, or fraud systems?
Practical selection guidance
Security team focused on detecting sophisticated API abuse: evaluate Salt and Traceable first.
Large enterprise already invested in Akamai: evaluate Akamai API Security.
Need a broader WAF + bot + API security suite: evaluate Akamai or Imperva.
High-volume consumer APIs with fraud/bot concerns: include Cequence in the evaluation.
For a serious evaluation, I would run a proof-of-concept using your own traffic and test scenarios such as:
account takeover attempts,
privilege escalation,
ID enumeration/BOLA,
abnormal transaction sequences,
scraping,
API key/token misuse,
insider-style misuse by authenticated users.
The “best” platform depends heavily on whether your main problem is unknown APIs, runtime abuse detection, fraud/business workflows, or edge enforcement.
Why it stands out: Widely considered a pioneer in AI-driven behavioral analysis for APIs. Salt builds deep per-user and per-application baselines over time.
Business logic detection: It tracks multi-step user sessions and sequences. Because it records the entire context of an API interaction rather than isolated requests, it can spot slow-and-low reconnaissance, privilege escalation, and BOLA (Broken Object Level Authorization) even when every individual request passes authentication.
Why it stands out: Following Akamai's major acquisition of Noname, this platform offers one of the most comprehensive end-to-end ecosystems—covering discovery, posture management, active testing in CI/CD, and runtime protection.
Business logic detection: Its runtime engine uses robust machine learning to baseline normal user and machine behavior across your entire API estate. It correlates subtle shifts in parameter usage, data harvesting, and parameter tampering to flag abuse that bypasses gateway controls.
Why it stands out: Traceable takes a data-centric approach by tracing user transactions across microservices using distributed tracing (OpenTelemetry).
Business logic detection: Because it deeply understands data flow and context through the entire application stack, it can recognize when an identity is manipulating parameters or accessing records out of sequence. It models user behavior with high fidelity, making it exceptionally good at tracing anomalous business logic paths down to individual user accounts.
False-positive tuning: Behavioral platforms need time to learn your app; check how intuitive their policy engines are for suppressing custom, legitimate business workflows that might otherwise trigger alerts.[](https://salt.security/use-cases/stop-behavioral-api-attacks)
If you'd like to narrow this down, let me know:
Are your APIs mostly public-facing (B2C) or internal/partner-facing (B2B)?
Do you need a platform that also covers Shift-Left API testing in CI/CD pipelines?
I can help tailor a recommendation to your current infrastructure stack.