Agentic AI security & posture
mAIndala vs Zenity
Zenity and mAIndala both help organizations understand and control what AI agents are doing, but they answer different questions. Zenity discovers running agents and detects and responds to threats in their behavior. mAIndala governs what an agent is allowed to do in the first place, brokers the credentials it acts with, and exports a record a third party can verify independently that it happened that way.
What Zenity does well
- A single end-to-end platform across three layers — Surface (discovery of running agents, their impact scope, and exploitable vulnerabilities), Enforce (policy decisions and runtime guardrails), and Protect (threat detection, investigation, and response) — spanning buildtime to runtime.
- AI Security Posture Management (AISPM) applies proactive guardrails during agent creation itself, aligned to the OWASP LLM and MITRE ATLAS frameworks, before an agent ever reaches production.
- AI Detection & Response (AIDR) continuously analyzes decision trees, tool-invocation patterns, memory manipulation, and inter-agent communication to catch attacks — prompt injection, data exfiltration — that traditional defenses miss, with automated remediation playbooks.
- Deep, purpose-built coverage of Microsoft Copilot Studio: every copilot configuration and interaction is broken into discrete steps capturing actions, logic flows, triggers, and data access.
- Industry recognition: named "the Company to Beat in AI Agent Governance" by Gartner and a 2025 Gartner Cool Vendor in Agentic AI TRiSM.
How mAIndala is different
Is it allowed, and can I prove it — not only is it risky
Zenity’s posture and detection layers answer whether an agent’s behavior looks dangerous. mAIndala answers whether an agent is allowed to do something at all, under whose policy, and produces a signed, RFC 3161-timestamped record a third party verifies offline — independent of continuous monitoring by either company.
Definition-level attestation with a public trust status
mAIndala runs an automated, OWASP-aligned scan of an agent or tool’s definition before it enters a catalog, assigns a Verified, Partial, or Unrated status visible to anyone using it, and alerts if an approved definition later changes.
Enforcement that blocks, not only detects and remediates
mAIndala places a policy-controlled gateway in the path of every tool call — allow/deny per tool, rate limits, time-of-day windows, DLP redaction, an instant kill-switch — and issues credentials scoped and short-lived from an encrypted vault.
Discovery that feeds an open, cross-vendor catalog
Where Zenity discovers agents already running to assess their risk, mAIndala’s CLI scan finds agents already running in an organization’s repos and CI before anyone registers them, feeding a scheduled sweep for anomalous behavior — backed by an open catalog of vetted capabilities to build new agents from.
Side by side
| Dimension | mAIndala | Zenity |
|---|---|---|
| Primary question answered | Is this agent allowed to do this, under whose policy, and can I prove it later? | Is this agent’s behavior risky, and can we detect and stop an attack in progress?[2] |
| Definition-level attestation | Automated, OWASP-aligned scan producing a Verified, Partial, or Unrated status before a capability enters a catalog, plus drift alerts if an approved definition later changes. | AI Security Posture Management (AISPM) applies proactive guardrails during agent creation, aligned to OWASP LLM and MITRE ATLAS.[1] |
| Runtime response | A policy-controlled tool gateway that allows or denies each call, with rate limits, DLP redaction, and an instant kill-switch. | AI Detection & Response (AIDR) continuously monitors behavior and triggers automated remediation playbooks when a threat is identified.[2] |
| Discovery of unregistered agents | A CLI scan finds agents already running in an organization’s repos and CI before anyone registers them, feeding a scheduled sweep for anomalous behavior. | Continuous discovery of running agent configurations and interactions across SaaS applications, custom cloud platforms, and endpoints.[2] |
| Where evidence lives | Exports as a signed, RFC 3161-timestamped file, verified offline by a third party without a mAIndala account or platform access. | Not described as an independently verifiable, exportable evidence artifact in public material (reviewed 2026-08-25). |
| Credential handling | Agents act with scoped, short-lived credentials issued from an encrypted vault, never a held long-lived secret. | Not described in Zenity’s public product material (reviewed 2026-08-25). |
| Model selection | Choosing the model an agent runs on produces a record: the same agent run on several models against a byte-identical input, with each model’s cost, latency, token usage, and output-safety findings, and the verdict that followed — exported in the signed, independently timestamped evidence pack. | Not described in public documentation (reviewed 2026-08-26). |
When Zenity is the better choice
- Your agent estate runs substantially on Microsoft Copilot Studio, Power Platform, or M365, and threat detection and response for agents already in production is your immediate priority.
- You want buildtime security guardrails embedded directly in the agent-creation experience, aligned to OWASP LLM and MITRE ATLAS, before you need a cross-vendor governance and evidence layer.
- You need continuous behavioral monitoring feeding a SOC with automated remediation playbooks for attacks already underway.
When mAIndala is the better choice
- You need to prove, after the fact and to a party outside your organization, that a specific agent’s activity followed your configured policy — not just that it wasn’t flagged as an attack.
- You want the tool call itself blocked or redacted at the point of the request, not detected and responded to afterward.
- You need one governance layer — and an open catalog of vetted capabilities — spanning agents built across multiple vendors, not primarily Microsoft-centric estates.
Using both together
A security posture and detection tool and a governance control plane are genuinely different jobs: Zenity watching for anomalous or malicious behavior at runtime, mAIndala attesting what an agent is allowed to do and exporting proof of what it did.
Sources
- Zenity — Microsoft Copilot Studio use case — accessed 2026-08-25 (reference 1)
- Zenity — homepage — accessed 2026-08-25 (reference 2)
- Zenity — Extending AI Agent Security from Buildtime to Runtime — accessed 2026-08-25 (reference 3)
Comparison last reviewed August 26, 2026 against publicly available information. See something out of date or inaccurate? Let us know.
Zenity and any other product or company names mentioned are trademarks of their respective owners. mAIndala is not affiliated with, endorsed by, or sponsored by Zenity.
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