Enkrypt AI vs. AgentFox

AgentFox

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What is Enkrypt AI?

Enkrypt AI is an agentic AI security and compliance platform that helps enterprises identify, mitigate, monitor, and govern risks associated with AI models, applications, agents, and supporting infrastructure. The platform is designed to cover AI security threats and compliance requirements across different models and modalities while allowing organizations to continuously evaluate AI systems as they evolve. Its dynamic red teaming capabilities generate use-case-specific attacks that adapt in real time to uncover security, safety, compliance, and brand risks beyond those identified through static testing. Enkrypt AI can test for threats including prompt injection, jailbreaking, data leakage, model inversion, adversarial attacks, drip attacks, unsafe actuation, policy violations, and misuse by end users. It also evaluates AI quality and safety risks such as hallucinations, model drift, bias and discrimination, toxic content, deceptive behavior, misleading outputs, lack of explainability, and brand misalignment. Real-time guardrails detect and stop risky interactions as they occur while adapting to new prompts, behaviors, and emerging threats. Continuous monitoring gives security and compliance teams visibility into AI risks, protection effectiveness, and areas requiring action as models and applications change. Enkrypt AI's compliance functionality converts internal policies and external regulatory requirements into automated guardrails and generates evidence that can support audits and compliance programs. The product portfolio includes Agent Red Teaming, Agent Guardrails, an Agent Policy Engine, AI Data Risk Audit, MCP Scanner, and MCP Gateway for protecting AI agents, their data, and their connections to tools and infrastructure. Enkrypt AI supports use cases including customer-facing AI agents, frontier models, AI compliance and auditing, third-party AI risk, secure AI development, data risk assessment, and MCP security.

What is AgentFox?

AgentFox is an open-source control plane tailored for the creation and oversight of AI agents. It methodically organizes agent repositories while establishing a connection between agents, tools, MCP servers, skills, and handoffs, all without executing code. For the frameworks it accommodates, it upholds tool-access policies, verifies tool arguments against set thresholds, and possesses the ability to suspend or reject actions until human review is conducted. Runtime assessments evaluate the interactions between models and tools to identify possible prompt injection threats and the risk of exposing confidential or personal data. Furthermore, AgentFox can function in observation mode before implementing enforcement measures, and it carefully records policy decisions in a hash-linked audit trail. Developers can choose to install AgentFox via Python or deploy it through a Docker Compose stack, making it versatile for different environments. Its target audience comprises engineering and security teams that seek thorough visibility, control mechanisms, and reliable documentation for managing AI agent activities. Released under the Apache-2.0 license, AgentFox also provides a hosted preview for potential users. This feature enhances accessibility for teams striving to improve their AI workflows with effective management solutions, ultimately supporting better collaboration and security in AI development.