Protect your entire AI and agentic ecosystem in real-time with automated discovery, threat detection, and active defense across APIs, models, and cloud infra.
DeepKeep
Secure enterprise AI applications, models, and agents across their entire lifecycle with multimodal guardrails, automated red teaming, and model scanning.
About DeepKeep
DeepKeep is an enterprise AI security and trust platform designed to protect generative AI systems, agents, and traditional machine learning models. As organizations rapidly deploy large language models and autonomous agents, they face unique risks stemming from non-deterministic outputs, an expanding attack surface via tool access, and emerging regulatory requirements like the EU AI Act. DeepKeep tackles these challenges by applying a unified policy engine across the entire AI lifecycle, safeguarding applications from initial development through to real-time production deployment. The platform integrates five primary security capabilities: AI Firewall, AI Red Teaming, AI Agent Scanner, Model Scanning, and AI Lens for discovery and usage control. The AI Firewall provides over 60 runtime guardrails using context- and intent-aware, cognition-based detection to stop zero-day threats, data leaks, and prompt injection attacks in real time across text, image, and video modalities. Model Scanning generates Machine Learning Bills of Materials (MLBOM) and identifies common vulnerabilities and exposures (CVEs), while the Agent Scanner evaluates autonomous agents by mapping their tools and accessible actions. Red teaming capabilities allow security teams to execute both automated vulnerability simulations and human-guided, vibe-based testing. DeepKeep is tailored for security engineers, DevSecOps teams, enterprise CISOs, and AI developers operating in heavily regulated industries such as finance, technology, defense, and consulting. It enables teams to enforce role-based usage policies, uncover shadow AI usage among developers and employees, and generate compliance-ready risk reports aligned with auditor frameworks. What sets DeepKeep apart is its natively multimodal approach and patented security architecture (US Patent 12450363-B2). Rather than chaining together separate text and image filters, DeepKeep processes multimodal inputs unified by context and intent. Furthermore, it offers highly adaptable deployment architecture, operating as a SaaS solution, inside customer VPCs, on-premises, or within completely air-gapped environments.
DeepKeep pros & cons
Pros
- Evaluates inputs based on context and intent rather than basic surface text matching.
- Natively supports multimodal data including text, images, and video in one unified signal.
- Supports air-gapped and on-premise deployments for strict compliance and privacy requirements.
- Holds established enterprise security credentials including ISO 27001, ISO 9001, and SOC 2 compliance.
Cons
- Public pricing details are not listed on the website and require booking a demo.
- Primarily designed for enterprise-scale AI deployments rather than small individual projects.
DeepKeep use cases
- Enterprise security teams can deploy the AI Firewall to prevent prompt injection and data leaks in customer-facing LLM applications.
- DevSecOps engineers can use Model Scanning to detect CVE vulnerabilities and generate MLBOMs before deploying models into production.
- Red teams and penetration testers can conduct automated and guided adversarial attacks across text and vision models to identify blind spots.
- IT compliance officers can monitor shadow AI usage and map model risk scores directly against regulatory frameworks like the EU AI Act.
DeepKeep features
- unified risk posture dashboard mapped to auditor-recognized compliance frameworks
- flexible deployment across saas, on-prem, private vpc, and air-gapped setups
- ai discovery and usage control for monitoring shadow ai and enforcing role-based policies
- ai agent scanner for mapping tool integrations and reachable action paths
- model scanning with mlbom and cve vulnerability detection
- automated and vibe-based ai red teaming simulations
- multimodal security understanding text, images, and video as unified signals
- ai firewall with 60+ runtime guardrails
DeepKeep FAQs
What deployment environments does DeepKeep support?
DeepKeep offers flexible deployment options designed for enterprise data security needs. It can run as a hosted SaaS solution, on-premises, inside your own virtual private cloud (VPC), or within fully air-gapped environments.
What is DeepKeep's AI Firewall?
The AI Firewall provides continuously updated, real-time protection featuring over 60 runtime guardrails. It monitors both pre- and post-deployment environments to detect and block threats across text, image, and video modalities using context- and intent-aware evaluation.
How does DeepKeep support AI red teaming?
DeepKeep provides adversarial attack simulations across multiple modalities. It supports fully automated testing for continuous coverage as well as an interactive 'vibe-based' mode that lets human security experts steer attacks by intuition.
What compliance certifications and standards does DeepKeep adhere to?
DeepKeep is certified for ISO 27001, ISO 9001, and SOC 2, and complies with GDPR. Its reporting and policy mechanisms also map to external compliance frameworks such as the EU AI Act and NIS2.
What does DeepKeep's Model Scanning feature do?
Model Scanning analyzes machine learning and AI units to detect known CVE vulnerabilities and generate a Machine Learning Bill of Materials (MLBOM) for complete supply chain transparency.
Ratings & reviews
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