DeepKeep
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About
DeepKeep is an enterprise-grade AI security platform designed to protect the integrity and trustworthiness of artificial intelligence systems throughout their entire lifecycle. The platform addresses the unique vulnerabilities inherent in large language models (LLMs), vision-language models (VLMs), and tabular data models. By providing a multi-layer defense strategy, it enables organizations to deploy AI applications with confidence, ensuring that models remain secure against adversarial attacks, data leakage, and operational risks. The solution is model-agnostic, meaning it can be integrated with various AI architectures regardless of the underlying framework or deployment environment. The platform's core capabilities are divided into four primary pillars: AI Firewall, Automated AI Red Teaming, AI Lens, and Model Scanning. The AI Firewall provides real-time protection by monitoring inputs and outputs for malicious activity or policy violations. Automated Red Teaming allows teams to stress-test their models using adaptive evaluations to identify weaknesses before they are exploited. AI Lens offers visibility into how employees interact with AI tools, providing usage control and discovery features. Finally, Model Scanning secures the AI supply chain by performing static and dynamic checks to verify the provenance and safety of models before they are integrated into production. DeepKeep is primarily built for security researchers, AI developers, and Chief Information Security Officers (CISOs) across industries like finance, technology, and manufacturing. It is particularly beneficial for organizations operating in highly regulated sectors where compliance and risk management are paramount. By automating complex security tasks like red teaming and model provenance verification, the platform reduces the manual overhead typically associated with securing advanced machine learning pipelines. It serves as a bridge between the rapid pace of AI innovation and the stringent requirements of corporate security protocols. What distinguishes DeepKeep from conventional security tools is its AI-native approach, which is specifically engineered to understand the non-linear logic and boundless connections of generative AI. Unlike traditional firewalls that rely on static rules, DeepKeep’s solutions are built to comprehend multimodal data, including images and complex tabular structures. This deep integration allows the platform to identify unseen vulnerabilities that standard scanning tools might miss. Furthermore, its focus on both security and trustworthiness ensures that AI outputs are not only safe from attack but also aligned with organizational ethics and performance standards.
Pros & Cons
Supports multimodal data including LLM, image, and tabular sources.
Provides real-time threat detection and mitigation with an AI-specific firewall.
Includes automated red teaming to streamline model robustness evaluations.
Verified with ISO 27001, SOC2, and GDPR compliance certifications.
Model-agnostic platform works across different AI frameworks and architectures.
Pricing information is not publicly available on the website.
No free version or self-service trial is offered for individual users.
Implementation may require dedicated security personnel to manage complex alerts.
Use Cases
Security researchers can automate model stress-testing using red teaming to find vulnerabilities before deployment.
CISOs in regulated industries can use AI Lens to gain visibility and control over internal AI usage and compliance.
AI developers can scan models for provenance and operational safety to secure their AI supply chain.
Data scientists can utilize risk assessments to evaluate the confidence and trustworthiness of model outputs.
Platform
Task
Features
• risk assessments
• ai firewall
• automated ai red teaming
• ai lens
• confidence evaluation
• multimodal protection
• model scanning
• ai discovery
FAQs
Does DeepKeep support multimodal models?
Yes, the platform is designed to protect a variety of model types, including Large Language Models (LLMs), Vision-Language Models (VLMs), and tabular data. This ensures comprehensive security across different AI applications, from chatbots to computer vision systems.
How does the AI Firewall function in real-time?
The AI Firewall acts as a protective layer that continuously monitors the pre- and post-deployment environment. It triggers alerts and applies guardrails to prevent malicious inputs or unsafe outputs from reaching users or internal systems.
What is covered during the Automated AI Red Teaming process?
DeepKeep uses adaptive evaluation techniques to assess the robustness and trustworthiness of an AI application. This automated process identifies potential vulnerabilities and provides streamlined metrics to help developers patch models before production.
Can DeepKeep help with compliance and model provenance?
Yes, the Model Scanning feature is specifically designed to secure the AI supply chain. It uses static and dynamic scanning to guarantee the provenance, compliance, and operational safety of models before they are deployed.
Pricing Plans
Enterprise
Unknown Price• AI Firewall
• Automated AI Red Teaming
• AI Lens (Usage Control)
• AI Discovery
• Model Scanning
• Risk Assessments
• Multimodal Protection
• Confidence Evaluation
Job Opportunities
AI Agent Security Team Leader
Secure the entire AI lifecycle with real-time firewalls, automated red teaming, and model scanning to ensure trustworthiness across LLM, vision, and tabular data.
Education Requirements:
Certifications (CISSP, CEH, OSCP)
Background in AI ethics and responsible AI
Experience Requirements:
Proven experience (4+ years) in cybersecurity
2+ years in a technical leadership role
Hands-on skills in Python, C++ or similar
Experience with open-source frameworks like PyRIT, Garak and OWASP GenAI Top10
Knowledge of current security practices, cloud solutions, adversarial ML, and threat modeling
Other Requirements:
Startup mindset: adaptable, proactive, and eager to learn
Clear, collaborative communicator
Strong background in Offensive Security (Application penetration testing and red teaming)
Publications or open-source contributions in AI security
Responsibilities:
Mentor and guide a team of researchers and developers
Design, develop, and deploy security frameworks and tools
Identify vulnerabilities and conduct red-team exercises
Collaborate cross-functionally with AI researchers and product leaders
Keep pace with emerging research and help set the direction for AI security
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Senior Python Developer
Secure the entire AI lifecycle with real-time firewalls, automated red teaming, and model scanning to ensure trustworthiness across LLM, vision, and tabular data.
Experience Requirements:
6+ years in software engineering
2+ years with Python
Experience with Docker, Kubernetes, and cloud platforms (AWS/GCP)
Other Requirements:
Strong grasp of design patterns, OOP, and testing frameworks
Excellent problem-solving and communication skills
Familiarity with microservices and modern CI/CD (advantage)
Experience in adversarial machine learning or secure AI systems
Responsibilities:
Develop and maintain core backend services in Python
Work with ML engineers to defend models against adversarial attacks
Deliver high-quality, tested, and scalable code
Contribute to system design, architecture, and deployment
Collaborate across teams to ship reliable, production-ready features
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Ratings & Reviews
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