DeepKeep

DeepKeep is an AI security platform that helps organizations safely adopt, deploy, and operate AI applications, AI agents, models, and employee AI tools. As enterprises move from experimenting with AI to embedding it in business-critical workflows, traditional application and cybersecurity controls are no longer enough. Large language models, multimodal models, and AI agents introduce a new class of risks: prompt injection, sensitive data leakage, unsafe or manipulated outputs, hallucinations, policy violations, malicious models, agent tool misuse, and vulnerabilities that only emerge through interaction with the AI system itself. These risks become even harder to manage as AI systems gain access to enterprise data, external tools, APIs, databases, and autonomous actions. Security teams need visibility into where AI is being used, a way to test AI systems before deployment, and controls that can protect them continuously in production. DeepKeep addresses these challenges across the AI lifecycle through a unified set of security capabilities. **AI Red Teaming** helps organizations identify weaknesses in AI applications and agents before attackers or users encounter them. DeepKeep provides both automated red teaming for broad, systematic security testing and Vibe AI Red Teaming, a human-steered approach in which an adaptive red-teaming agent follows the direction and intuition of a security expert. The platform can probe systems across multiple turns, adapt attacks based on responses, test business-specific scenarios, and uncover vulnerabilities that static test datasets may miss. **AI Firewall and runtime protection** secure AI applications and agents while they are being used. DeepKeep evaluates inputs, outputs, and relevant context in real time to detect threats such as prompt injection, sensitive-data exposure, harmful or inappropriate content, policy violations, hallucinations, and other unsafe behavior. Organizations can define how the system responds, including blocking, redacting, replacing, refining, obfuscating, or alerting on problematic content. **AI Usage Control** gives security teams visibility and control over how employees and developers use external AI services and AI development tools. It helps discover shadow AI, apply role-based policies, protect sensitive information, and enforce security controls without preventing legitimate AI adoption. **AI Agent Scanner** analyzes AI agents before deployment to identify their attack surface. It maps the tools, integrations, model calls, permissions, and data connections available to an agent, highlights potential security weaknesses, and provides remediation guidance. **Model Scanning** protects the AI software supply chain by inspecting models before they are introduced into an enterprise environment. DeepKeep analyzes model files for vulnerabilities, unsafe serialization, embedded malware, licensing issues, and other risks, and can dynamically evaluate models in an isolated environment. DeepKeep is designed for modern enterprise AI environments rather than a single model or provider. It is model-agnostic and supports both language and computer-vision systems, including multimodal applications where text and images interact. The platform also provides native multilingual security, allowing organizations to test and protect AI systems across languages without relying on translation into English as an intermediate security layer. DeepKeep can be deployed as SaaS, in a private cloud, on-premises, or in air-gapped environments, enabling organizations to meet data sovereignty, regulatory, privacy, and infrastructure requirements. Together, these capabilities allow security teams to discover AI usage, assess AI systems before deployment, understand agent attack surfaces, inspect models entering the organization, and continuously protect AI interactions at runtime. The goal is to make AI security part of the way enterprises build and operate AI, rather than an additional control added after deployment.