Salt Security, Noma Security, and Zenity are increasingly mentioned in the same AI security conversations, but they are not interchangeable. Salt’s current public story starts with APIs and the agentic action layer. Noma presents a broad platform for AI applications, models, RAG systems, agents, posture, governance, red teaming, access control, and runtime protection. Zenity focuses on securing enterprise AI agents across SaaS, cloud, endpoint, coding, and homegrown environments.
The right comparison is therefore not “which company has more AI features?” It is “which platform can see, explain, govern, and control the risks that matter in our environment?” That answer depends on where agents are built, which APIs and tools they can reach, how identities and permissions are assigned, what sensitive data moves through the system, and which team must investigate or stop unsafe behavior.
Executive Summary: Start With the Risk, Not the Category
Salt Security
Most natural shortlist when API inventory, API posture, agent-to-API relationships, MCP exposure, action-layer behavior, and code-to-runtime policy are central to the buying problem.
Noma Security
Most natural shortlist when the organization wants broad AI ecosystem security across applications, models, RAG, agents, AI-SPM, governance, red teaming, agent access control, and runtime protection.
Zenity
Most natural shortlist when enterprise agents are distributed across SaaS, cloud, endpoint, coding, and homegrown platforms and security needs continuous posture, identity, MCP, behavior, and execution context.
Layered approach
Most natural choice when no single product provides enough depth across AI governance, agent security, API security, identity, gateways, data security, endpoint controls, and incident operations.
All three vendors now use language that spans discovery, posture, runtime behavior, governance, and enforcement. That overlap makes homepage comparisons unreliable. A useful evaluation must test control depth at the point where risk becomes real: code, configuration, model interaction, agent identity, MCP connection, tool call, API request, API response, or downstream business action.
Current Public Positioning in 2026
The descriptions below summarize vendor-owned materials. They are useful for defining the starting hypothesis for a proof of value, but they should not be treated as independently verified capability ratings.
Salt Security: APIs, agents, MCP, and the action layer
Salt currently describes an Agentic Security Platform that connects AI agents with the APIs, MCP servers, capabilities, and workflows they can reach. Its public materials continue to emphasize API discovery, unified inventory, posture management, runtime threat protection, incident response, and visibility into agent-to-API activity. In June 2026, Salt also announced Salt Code, extending policy into AI-assisted development and CI/CD, while a July 2026 platform update emphasized an interactive map of agents, MCP, capabilities, and APIs.
This makes Salt especially relevant when the buyer believes the highest-risk moment is an agent using an API or tool to perform a business action. The proof of value should still verify whether the deployment sees the required traffic, identities, responses, object relationships, asynchronous work, and private environments.
Noma Security: broad AI security and agent access control
Noma publicly presents a holistic platform for autonomous agents, large language models, RAG systems, AI applications, AI security posture management, governance, red teaming, runtime protection, and MCP security. In June 2026, Noma announced Agent Access Control for discovering agents and MCP servers, assigning agent identity, defining access policy, and combining access boundaries with runtime detection and response.
Noma is therefore a strong candidate when the evaluation spans the AI lifecycle rather than one traffic layer. Buyers should test how its broad platform translates into depth for their exact model providers, AI development stacks, agent frameworks, gateways, data sources, and production control points.
Zenity: enterprise agent security across build time and runtime
Zenity publicly positions an AI Agent Security and Governance Platform with AI observability, AI security posture management, exposure management, agentic identity, MCP security, and AI detection and response. Its platform materials emphasize agents across SaaS, cloud, endpoint, coding, and homegrown environments. In March 2026, Zenity described continuous contextual security that correlates posture, permissions, configuration change, and multi-step runtime activity.
Zenity is particularly relevant when the agent estate extends beyond a centrally managed AI engineering team. The proof of value should verify supported platforms, the quality of identity and permission context, detection across multi-step behavior, inline control points, and evidence available for investigation.
Salt Security vs Noma Security vs Zenity: Side-by-Side
This table compares public emphasis and the evidence a buyer should request. It deliberately avoids universal feature checkmarks because capability depth can vary by integration, environment, license, and release.
| Evaluation area | Salt Security | Noma Security | Zenity | Evidence to request |
|---|---|---|---|---|
| Primary public emphasis | API and agentic action-layer security | Holistic AI ecosystem and agent security | Enterprise AI agent security and governance | Architecture diagram showing the exact control points used in your environment |
| Inventory | APIs, agents, MCP, capabilities, relationships | AI assets, applications, agents, models, RAG, MCP | Agents, platforms, extensions, identity, permissions, dependencies | Reconcile vendor inventory against a known test inventory and measure misses, duplicates, and ownership quality |
| API action-layer depth | Core area to validate | Relevant through agents, gateways, tools, and integrations | Relevant through agent actions and connected systems | Show endpoint, identity, object, request, response, tool, and downstream action context |
| AI application lifecycle | Increasing code-to-runtime emphasis | Broad posture, red teaming, governance, and runtime emphasis | Build-time posture plus runtime and execution emphasis | Trace one application from repository and configuration through deployment, runtime, and incident response |
| Agent identity and access | Agent, MCP, capability, and API relationship context | Publicly announced agent identity and access-control capabilities | Agentic identity, permissions, boundaries, and execution context | Demonstrate unique agent identity, delegated authority, shared credentials, permission drift, and revocation |
| Runtime behavior | API and agent behavior, posture, threats, action paths | AI detection and response across agent interactions and tool use | Stateful detection across sessions, agents, permissions, and execution | Use a multi-step scenario that appears normal per event but becomes risky as a sequence |
| MCP security | Discovery and mapping of MCP servers and agent access | MCP inventory, access control, runtime protection, and governance | MCP inventory, extension posture, permissions, and runtime behavior | Discover approved and shadow MCP servers, tools, owners, permissions, and risky changes |
| Data security | API payloads, responses, data movement, agent action context | Prompts, responses, data sources, applications, models, and agents | Prompts, responses, tools, execution, data leakage, and downstream work | Test masking, evidence minimization, retention, deletion, residency, and support access |
| Enforcement | Validate gateway, API, policy, and runtime control options | Validate access control, guardrails, gateway integration, and runtime response | Validate boundaries, inline prevention, platform-specific controls, and response | Run monitor-only, alert, approval, restriction, and block modes with rollback |
| Best-fit hypothesis | API-heavy enterprise with agent action risk | Central AI security program spanning many AI asset types | Distributed enterprise agent estate across business and engineering platforms | Prove the hypothesis with measured outcomes rather than vendor presentation scores |
Do not confuse broad coverage with equal depth
A platform may detect that an agent accessed a sensitive system without understanding object-level authorization inside the target API. Another platform may understand API behavior deeply but have less context about model lineage, agent configuration, or business-created SaaS automations. The evaluation should therefore trace complete scenarios across model, agent, identity, tool, API, data, and business outcome.
Map the comparison to recognized risk frameworks
The OWASP Top 10 for Agentic Applications 2026 provides agent-specific risks such as goal hijacking, tool misuse, identity and privilege abuse, memory and context poisoning, insecure inter-agent communication, cascading failures, and rogue agents. The OWASP API Security Top 10:2023 covers API authorization, authentication, resource consumption, sensitive business flows, SSRF, inventory, and unsafe third-party API consumption. A serious vendor evaluation should test both perspectives.
Architecture, Deployment, and Data Governance
Security quality depends on the data the platform can observe and the control points it can use. Before scoring detections, require each vendor to document how it connects to your AI and API environment.
Collection model
Document agents, APIs, gateways, SaaS connectors, cloud logs, endpoints, repositories, model providers, MCP servers, identity systems, and whether traffic or content leaves your environment.
Enforcement point
Identify whether policy is enforced in an AI gateway, API gateway, reverse proxy, agent platform, endpoint, SaaS integration, MCP connection, code assistant, CI/CD workflow, or vendor cloud.
Failure behavior
Test high availability, latency, throughput, scaling, queue behavior, fail-open or fail-closed modes, bypass, certificate handling, upgrade, and rollback.
Evidence path
Confirm which event fields reach the SIEM, ticketing, SOAR, data platform, and audit systems, and ensure evidence is useful without exposing raw secrets or unnecessary sensitive content.
Questions every vendor should answer
- Which prompts, responses, API requests, API responses, source files, tool calls, credentials, and metadata are collected?
- Can sensitive fields be masked before transmission, and can specific routes, tools, projects, users, or payload fields be excluded?
- Where is data processed and stored, how is it encrypted, and which vendor personnel can access it?
- How are agent identities represented when agents use shared service accounts, delegated user sessions, or gateway credentials?
- How does the platform handle asynchronous jobs, streaming responses, background tool calls, and actions that occur after the original prompt?
- Can customers export inventory, policy, evidence, and configuration in usable formats before contract termination?
- What happens during service degradation, loss of connectivity, model-provider outage, gateway failure, or incorrect blocking?
Use the NIST Generative AI Profile to keep the evaluation connected to governance, mapping, measurement, and management across the AI lifecycle. Product telemetry is valuable, but it does not replace ownership, risk tolerance, testing, change control, incident planning, or executive accountability.
A Weighted 100-Point AI Security Vendor Scorecard
Weight the scorecard before vendor demonstrations. Otherwise, teams often award points to impressive features that do not reduce their highest business risks.
| Area | Weight | What strong evidence looks like |
|---|---|---|
| Inventory and ownership | 10 | Accurate agents, APIs, MCP, tools, models, applications, owners, environments, versions, and dependencies |
| AI posture and lifecycle context | 12 | Configuration, model, RAG, extension, source, deployment, and change context with actionable remediation |
| Agent identity and access | 12 | Unique identity, delegated authority, permission mapping, policy, drift detection, revocation, and auditability |
| Runtime detection and response | 12 | Stateful, explainable detections across prompts, sessions, tools, data, actions, and downstream outcomes |
| API and action-layer security | 12 | API inventory, authorization context, requests, responses, business flows, resource abuse, data exposure, and third-party calls |
| MCP and tool governance | 10 | Approved and shadow server discovery, tool schemas, ownership, provenance, permissions, changes, and runtime controls |
| Data governance and privacy | 10 | Minimization, masking, residency, encryption, access, retention, deletion, tenant isolation, and contract clarity |
| Safe enforcement | 8 | Monitor, alert, approval, restrict, block, exception, staged rollout, rollback, and measurable false-positive handling |
| SOC and engineering operations | 7 | Useful SIEM events, investigation timelines, case management, tickets, developer feedback, APIs, and automation |
| Resilience, cost, and exit | 7 | Performance proof, HA, support, upgrade safety, transparent licensing, predictable growth cost, export, and exit plan |
| Total | 100 | Score only evidence demonstrated in the target environment |
Set minimum gates in addition to the total score. For example, a vendor should not win with excellent dashboards if it fails mandatory data-residency, availability, identity, or enforcement requirements.
Five-Phase Proof-of-Value Plan
- Define scope and truth data. Create a known inventory of agents, APIs, MCP servers, tools, owners, identities, permissions, sensitive data, and business workflows. Define what the vendor should and should not discover.
- Deploy in observation mode. Connect representative SaaS, cloud, endpoint, code, gateway, identity, and API sources. Measure deployment effort, coverage gaps, data movement, performance, and operational ownership.
- Run controlled scenarios. Test excessive permissions, shadow assets, unsafe tool use, prompt or context manipulation, sensitive data movement, unusual API access, BOLA-style object access, resource abuse, and multi-step behavior using synthetic data and authorized identities.
- Evaluate investigation and enforcement. Measure alert precision, context, time to triage, policy explanation, SIEM forwarding, ticket quality, safe exceptions, staged enforcement, rollback, and whether the platform helps the responsible team take action.
- Review production and commercial fit. Validate HA, scaling, support, upgrade process, administration effort, data governance, licensing metrics, renewal exposure, roadmap dependencies, and the ability to export data and policies.
Recommended success metrics
- Percentage of known agents, APIs, MCP servers, tools, and owners correctly discovered
- Number of previously unknown or unowned assets confirmed as real
- Precision of high-severity detections after exclusions and tuning
- Median time from event to a defensible investigation conclusion
- Percentage of events containing useful identity, permission, asset, action, and data context
- Performance and availability impact under representative load
- Time required to implement, approve, test, and roll back one enforcement policy
- Weekly operating effort for security, engineering, platform, privacy, and compliance teams
Practical Decision Framework
Choose the starting shortlist by the dominant control problem
API and action layer first
Start with Salt when the urgent problem is API inventory, API posture, agent-to-API relationships, MCP exposure, API behavior, and actions that agents perform through enterprise APIs.
Broad AI program first
Start with Noma when the organization needs a shared program across AI applications, models, RAG, agents, posture, red teaming, governance, access control, and runtime protection.
Distributed agent estate first
Start with Zenity when AI agents are already spread across SaaS, cloud, endpoints, coding tools, citizen development, and homegrown platforms.
Multiple control points
Evaluate a layered model when API security, AI governance, endpoint behavior, identity, gateways, data security, and agent platforms require different depths of control.
Common comparison mistakes
- Using a generic demo. A polished demonstration cannot prove visibility or enforcement in your environment.
- Scoring product names instead of evidence. Terms such as AI-SPM, AIDR, runtime protection, and agent governance do not guarantee equivalent depth.
- Ignoring the API action layer. An agent can be well governed at the model layer and still misuse a weakly authorized API.
- Ignoring agent identity. Shared service accounts and delegated user sessions can make activity difficult to attribute or revoke.
- Skipping response and downstream data. Request-only visibility can miss what the API returned or what the agent did after receiving it.
- Underestimating operating effort. Inventory cleanup, tuning, policy review, exception handling, and incident ownership determine whether the platform remains useful.
- Buying roadmap features. Separate generally available capabilities from previews, limited integrations, and future commitments.
For API-focused evaluation criteria, see Ammune’s API security vendor evaluation checklist, AI agent API security risks, API protection guide, and API testing versus runtime monitoring.
Primary References Used for This Update
- Salt Agentic Security Platform and Salt agentic AI security overview
- Salt platform update published July 15, 2026
- Noma Security platform overview and Noma Agent Access Control announcement
- Zenity AI Agent Security and Governance Platform and Zenity continuous contextual security update
- OWASP Top 10 for Agentic Applications 2026
- OWASP API Security Top 10:2023
- NIST AI RMF Generative AI Profile
Conclusion: Let the Evidence Choose the Vendor
Salt, Noma, and Zenity are moving toward overlapping AI and agent security territory, but their public centers of gravity remain different. Salt is strongly connected to APIs and the agentic action layer. Noma presents a broad AI ecosystem and agent security platform. Zenity emphasizes continuous security and governance for enterprise agents across a wide range of platforms.
A defensible decision requires more than a feature matrix. Map the assets, identities, permissions, data, tools, APIs, workflows, and business outcomes you must protect. Weight the scorecard before demonstrations. Run controlled scenarios using your integrations. Measure discovery, precision, investigation, enforcement, performance, governance, operating effort, and total cost. Then select the product—or layered architecture—that proves it can reduce the risks your organization actually has.
FAQs
Which is better: Salt Security, Noma Security, or Zenity?
There is no universal winner. Salt is most naturally evaluated when APIs and the agent action layer are central. Noma is most naturally evaluated for a broad AI security program spanning applications, agents, models, RAG, posture, red teaming, access control, and runtime protection. Zenity is most naturally evaluated when the priority is enterprise AI agents across SaaS, cloud, endpoint, coding, and homegrown environments. The decision should follow a controlled proof of value.
How does Salt Security differ from Noma Security and Zenity?
Salt publicly emphasizes API and agentic security, including agent-to-API visibility, API inventory, posture, action-layer risk, and code-to-runtime policy. Noma publicly emphasizes holistic AI ecosystem security, while Zenity publicly emphasizes AI agent observability, posture, identity, MCP security, exposure management, and detection and response.
When should a company shortlist Salt Security?
Shortlist Salt when API discovery, API posture, API behavior, agent-to-API relationships, MCP exposure, and the security of actions performed through APIs are leading requirements. Validate how the platform collects traffic, handles response data, explains detections, and supports enforcement in your architecture.
When should a company shortlist Noma Security?
Shortlist Noma when the organization wants one AI security program across AI applications, autonomous agents, models, RAG systems, AI security posture management, red teaming, governance, agent access control, and runtime protection. Confirm product depth and integration coverage for the AI platforms you actually use.
When should a company shortlist Zenity?
Shortlist Zenity when enterprise AI agents are distributed across SaaS, cloud, endpoint, coding, and homegrown environments and you need continuous visibility into configurations, permissions, identities, MCP extensions, runtime behavior, and downstream actions. Verify supported platforms and enforcement points during the proof of value.
Do these platforms replace API gateways, WAFs, or identity systems?
No. AI and API security platforms normally complement gateways, WAFs, identity providers, cloud controls, endpoint tools, and SIEM systems. Buyers should map which control enforces authentication, authorization, traffic policy, agent identity, tool access, data protection, runtime detection, and incident response.
What should an AI security proof of value test?
Test inventory accuracy, ownership mapping, agent and API relationships, MCP and tool discovery, permission context, sensitive-data handling, runtime detection, alert precision, policy enforcement, investigation speed, deployment overhead, SIEM integration, resilience, and the ability to export evidence without exposing raw secrets.
Why is API security important in an AI agent comparison?
AI agents often act through APIs. They read records, update systems, call tools, trigger workflows, and move data. Model-layer controls cannot by themselves prove that object authorization, property authorization, business rules, resource limits, API inventory, and downstream integrations are secure.
Which standards should guide the comparison?
Use the OWASP Top 10 for Agentic Applications 2026 for agent-specific risks, the OWASP API Security Top 10 2023 for API risks, and the NIST AI Risk Management Framework and Generative AI Profile for governance, measurement, and lifecycle risk management.
How should buyers evaluate data governance?
Ask exactly what prompts, responses, API payloads, tool calls, credentials, source code, and metadata are collected; where they are processed; how masking works; who can access them; how long they are retained; how deletion is verified; and what happens to data when the contract ends.
Should an enterprise buy one platform or use layered tools?
Either model can work. A single platform may reduce integration and operating effort, while layered tools may provide greater depth at specific control points. Compare total operating cost, overlapping telemetry, policy consistency, incident workflows, and the risk of blind spots between products.
Can Ammune support an AI and API security vendor evaluation?
Ammune can help teams define API-focused proof-of-value criteria such as runtime visibility, request and response inspection, sensitive-data detection, behavior analysis, SIEM-ready evidence, monitoring-first deployment, and safe enforcement. The comparison should still be based on verified requirements and measured results.
Build an evidence-based AI and API security evaluation
Define the architecture, risk scenarios, success metrics, data requirements, runtime evidence, and safe enforcement plan before committing to a vendor.