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LangGuard vs Operant AI

LangGuard and Operant AI solve different halves of the problem. Operant AI's category is Runtime AI application defense (cloud-native) — it secures how an agent operates. LangGuard is a deterministic governance control plane: it decides what an agent is allowed to do, before the action executes. Choose Operant AI if the gap you are filling is Runtime AI defense. Choose LangGuard if you need Segregation of Duties, named-approver routing and audit-grade SOX or GDPR evidence — Operant AI does not enforce Segregation of Duties.

The deterministic AI governance control plane vs. Runtime AI defense — compared on 12 governance & compliance criteria for enterprises putting AI agents into production.

First-party research · Comparison data last verified

Strong / documented Partial / indirect Absent / not publicly documented

LangGuard

Deterministic runtime AI governance control plane

LangGuard is a deterministic runtime AI governance control plane. Two engines work across the full agent lifecycle: SCOPE-MCP maps and compliance-classifies an agent's action surface before it ships, and Arbiter deterministically authorizes every agent action before it executes — clearing safe actions with no added latency and routing anything that crosses a Segregation-of-Duties boundary or policy threshold to a named approver. The authorization is the governance; the audit trail is automatic.

Early stage · Sells to IT, Security & GRC leaders

More: LangGuard platform

Operant AI

Runtime AI application defense (cloud-native)

Operant AI is a runtime AI application defense platform for cloud-native environments — "3D Runtime Defense," an MCP Gateway, Agent Protector, and the open-source Woodpecker red-teaming engine — with inline action blocking, data redaction, and prompt-injection defense.

Founded 2020 · San Francisco · ~$13.5M (Series A) · Sells to Platform / cloud-native engineering / AppSec

Source: operant.ai

How do LangGuard and Operant AI compare?

12 criteria that decide whether an enterprise can prove — not just hope — that its AI agents stay inside policy.

Criterion LangGuard Operant
Deterministic, rule-based authorization Provable allow/deny decisions, not ML/probabilistic detection
Pre-execution enforcement Evaluates and blocks an action before it executes
Segregation of Duties enforcement Conflict-of-duty rules across agent actions
Excessive-agency prevention / least privilege Scopes each agent to the narrowest action surface
Design-time action-surface mapping Maps what an agent can do before it ships
Compliance-classified tools catalog Tools/MCP servers pre-scored against SoD & regulations at design time
Full lifecycle coverage (design-time + runtime) Governs the agent before and during production
Named-approver human-in-the-loop routing Routes risky actions to specific accountable approvers
SOX / GDPR / financial-GRC control mapping & evidence Maps agent actions to financial & privacy control obligations
AI-specific standards (ISO 42001, EU AI Act, NIST AI RMF, OWASP LLM) Alignment to emerging AI governance standards
Immutable / tamper-evident audit ledger Cryptographically defensible evidence of every decision
GRC / internal-audit / IT-governance buyer fit Built for the compliance owner, not only the security engineer

What is Operant AI best at?

  • Strong inline runtime blocking in Kubernetes, with a low-friction agentless helm install
  • Prompt-injection/jailbreak defense and real-time DLP auto-redaction (PII/PCI/PHI/keys)
  • Dedicated MCP Gateway with trust zones and tool-poisoning detection
  • Woodpecker open-source red-teaming and broad Gartner coverage

Where does LangGuard pull ahead?

  • Detection leans behavioral/probabilistic rather than deterministic authorization
  • No Segregation-of-Duties enforcement
  • Runtime-only — design-time coverage is red-teaming, not a governance gate
  • Compliance is SOC 2 + Woodpecker's OWASP/MITRE/NIST — no SOX/GDPR/ISO 42001 evidence
  • Human-in-the-loop is directional, with no named-approver routing; security-eng buyer, not GRC

Which should you choose?

Operant AI is strong in its own category — Runtime AI defense. But securing how an agent operates is not the same as governing what it is allowed to do. LangGuard makes a deterministic, rule-based authorization decision on every action before it executes — enforcing Segregation of Duties, routing risky actions to named approvers, and emitting audit-grade SOX/GDPR evidence. It is the governance control plane that sits above the layer Operant AI operates in.

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Common questions

What is the difference between LangGuard and Operant AI?

LangGuard is categorised as Deterministic runtime AI governance control plane. Operant AI is categorised as Runtime AI application defense (cloud-native). The practical difference is where each one sits relative to the agent's action: one governs the request path, the other governs the decision to allow the action at all.

Does LangGuard enforce Segregation of Duties?

LangGuard enforces Segregation of Duties directly. Segregation of Duties is a conflict-of-duty rule across an agent's actions — the control that stops one agent both raising and approving the same transaction. It is the criterion most agent-security tools leave to the customer.

Does Operant AI enforce Segregation of Duties?

Operant AI does not enforce Segregation of Duties. Check this against your own control matrix before assuming runtime monitoring covers it — detecting a violation after the fact is not the same control as preventing it.

Which one gives you SOX and GDPR compliance evidence?

LangGuard maps agent actions to SOX and GDPR control obligations and emits evidence. Operant AI does not map to SOX or GDPR control obligations. Logging that an action happened is not the same as evidence that it was authorized against a named control, which is what an internal auditor asks for.

Can LangGuard and Operant AI be used together?

Yes. They operate at different layers, so running both is common — one handles the runtime path, the other the authorization decision. The question is not which to buy but which layer you have not covered yet.

How were these 12 criteria scored?

Each vendor was scored against 12 governance and compliance criteria using public documentation, product pages and published compliance material as of August 14, 2026. Full means the capability is documented and shipping; partial means it is indirect or requires customer-authored policy; absent means it is not publicly documented. No vendor was contacted for a private briefing.

How did we score this?

This comparison is first-party research by LangGuard. Every vendor in the set is scored against the same 12 governance and compliance criteria, drawn from public product documentation, pricing and compliance pages, and published technical material, last verified .

  • Strong — the capability is documented and shipping.
  • Partial — present but indirect, or dependent on policy the customer writes.
  • Absent — not publicly documented at the time of review.

We publish comparisons that include our own product, so treat the LangGuard column as a vendor claim and check it the same way you would check anyone else's. Scoring is against public material only; no vendor was given a private briefing or a right of reply. Found something out of date? Tell us and we will correct it.

Sources: LangGuard platform · Operant AI — official site

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