Enkrypt AI Guardrails

Overview

Rather than focusing on a single prompt filter, Enkrypt applies controls at prompt, retrieval, tool, and output boundaries. It can filter, rewrite, block, escalate, or approve and deny tool calls, covering risks from prompt injection and jailbreaks to unsafe tool actions, data exposure, boundary violations, toxic content, and compliance concerns.

That workflow coverage makes it a fit for teams whose risk surface includes agents and connected tools, not just user-facing text generation. Policies also apply to image and audio inputs, including injection defenses, which broadens coverage for multimodal deployments.

Key features

Controls across the workflow

API-first integration, agent hooks or middleware, and an MCP Gateway let teams place enforcement around existing model and tool paths. Identity claims can draw on Okta, Azure AD, or custom JWT claims. This is useful where access context matters to policy decisions; teams should still account for integrating those hooks into their own stack.

Audit and performance

Enforcement records carry a policy ID, version, and reason code, and can be exported to SIEM. That gives security and compliance teams concrete evidence to review internally. The product page claims latency under 15 milliseconds and stable behavior under load; those claims are relevant for runtime controls, though teams should validate performance against their own traffic and deployment.

Security posture claims include SOC 2 Type II, ISO 27001, GDPR Ready, HIPAA Ready, and NIST AI RMF Aligned. Python SDK support is specified, alongside API access; teams relying on other SDK languages should account for that narrower stated language coverage.

Pricing

Explore is useful for spot checks and evaluation, but its 50 monthly credits and 7-day retention are constrained for ongoing production review. A comprehensive red team assessment requires 5,000 credits, so the free monthly allowance is not enough for that evaluation scope. Launch raises the monthly allowance to 250 and retention to 30 days, with email support; it is the entry paid option for smaller evaluations or deployments with modest usage. Scale quadruples Launch’s monthly credits and adds dedicated Slack or Teams support, making it better suited to teams that need a larger recurring allowance and a closer support channel.

Enterprise is the fit for organizations needing custom allocation, retention, SLAs, or VPC or on-premises deployment. VPC includes unlimited usage, a meaningful distinction from credit-based plans. The listed paid-from price is 134 /mo, while Launch is priced at 134.00 USD per year, billed annually; compare the explicit plan term and allocation when budgeting.

Platforms

Enkrypt supports API, self-hosted, and web use. Enterprise adds VPC or on-premises deployment, giving organizations with deployment constraints a route beyond the standard options. Teams that need those environments should factor in Enterprise’s custom pricing.

Who it's for

The strongest fit is an enterprise securing agentic or multimodal AI systems, particularly where tool permissions, retrieval boundaries, and audit evidence matter together. AI safety researchers can use Explore for spot checks, but its monthly credit cap limits sustained or comprehensive assessment. Teams seeking a simple standalone prompt filter, or those needing a larger free evaluation allowance, may prefer a different plan structure.

Pros and cons

• Pros: Enforcement spans prompts, retrieval, tools, and outputs, addressing risks across an AI workflow rather than only the final response.
• Pros: Policy-versioned logs with reason codes and SIEM export support internal review and evidence collection.
• Pros: Text, image, and audio policy coverage and identity integrations fit deployments with multiple modalities and access contexts.
• Cons: Explore’s 50 monthly credits are limited, especially against the 5,000-credit requirement for a comprehensive red team assessment.
• Cons: Retention is 7 days on Explore and 30 days on Launch and Scale; longer retention requires Enterprise customization.
• Cons: Python is the only specified SDK language, which may mean extra integration work for teams standardizing on other SDKs.

Alternatives

For a broader comparison, see AI Guardrail Software. Consider Fiddler Guardrails if its free real-time controls for harmful exposure, hallucinations, toxicity, PII/PHI, prompt injection, and jailbreak attempts better match the immediate need. Openlayer Guardrails is worth comparing when a free plan with 20,000 monthly inferences, one member, and one inference pipeline per project better fits evaluation needs.

PromptGuard may suit teams wanting a free allowance of 20,000 scans a month across desktop and web platforms, with a Team plan at 19.00 USD per month. HiddenLayer AI Runtime Security is another paid runtime-security option to consider. OpenAI Guardrails is a free API and self-hosted alternative. Pangea AI Guard offers a freemium option across API, extension, and self-hosted platforms.

Radware Alteon is a paid option with L4–L7 ADC, global server load balancing, routing, analytics, and bot management capabilities. Amazon Nova Reel is a paid API option.

Verdict

Choose Enkrypt AI Guardrails when your team needs runtime policy enforcement across agents, tools, retrieval, and multimodal inputs, backed by auditable decisions. The workflow breadth and SIEM-ready records are its clearest strengths. Look elsewhere if you need generous free evaluation, longer standard retention, or a specified SDK beyond Python; the lower plans’ credit ceilings and Enterprise’s custom pricing make those trade-offs central to the decision.