DeepKeep

DeepKeep is an AI Security Platform designed to ensure the security of AI applications, models, and agents throughout their lifecycle. It is built to meet the demands of scale and compliance in a real-world environment, offering full-lifecycle protection for any model. The platform understands text, images, and videos as a single signal, which allows for an insightful evaluation against the intent of every request. It features real-time security through policy enforcement and works in various environments, including SaaS, on-premises, and even air-gapped systems.

Key features of DeepKeep include its AI Firewall, a suite of runtime guardrails that offers real-time detection and protection; the AI Red Teaming feature for adaptive evaluation and robustness tests; DeepKeep for LLM, an end-to-end security solution for language models, and DeepKeep for Vision, a solution for computer vision pipeline security. Other features include AI Agent Scanner for attack surface scanning, Model Scanning for static & dynamic supply-chain checks, AI Lens for visibility & access control, and policy enforcement for real-time security.

DeepKeep's AI Firewall engages in runtime detection and guardrails, providing real-time security against potential threats. It acts defensively within the AI's operating environment so that if potentially harmful data or requests are identified, the firewall can respond immediately to neutralize the threat. It does not simply detect threats but employs strategies such as blocking, redacting, replacing, or alerting.

The AI Red Teaming feature in DeepKeep is designed for adaptive evaluation and robustness tests. This means it utilizes adversarial testing methods to evaluate the resilience of AI applications, models, and agents to attacks. It proactively finds vulnerabilities and underscores areas for improvement, striving to optimize the system's overall security posture.

DeepKeep's Model Scanning mechanism performs static and dynamic supply-chain checks. It scrutinizes various aspects of an AI model to identify potential security risks and vulnerabilities, monitoring for vulnerabilities not only in the AI's design and structure but also in the inputs and outputs throughout the modeling process.

By 'Full-lifecycle Protection', DeepKeep refers to its ability to secure AI applications, models, and agents from development to real-time use. It covers every stage of the AI's lifecycle, identifying and mitigating risks from the initial building phase, through testing and deployment, all the way to real-time application and use.

DeepKeep for LLM significantly enhances security by offering an end-to-end security solution for language models. It takes comprehensive measures to protect language processing AI programs against adversarial attacks and other security risks, thus helping to maintain the integrity of these models throughout their lifecycle.

DeepKeep for Vision plays a vital role in securing computer vision pipelines. It applies extensive safeguards to ensure that AI systems dealing with image processing and interpretation are well protected against potential security threats, thereby preserving the reliability of the insights obtained from such AI.

DeepKeep's AI Lens is designed to enhance visibility and access control across teams that work with AI. It provides a clear overview of the AI landscape and offers effective access management tools. This results in a more transparent workflow and helps maintain control over who has the authority to interact with various AI resources.

The AI Agent Scanner in DeepKeep adds to its robust security suite by performing attack surface scans for agents. By identifying the potential avenues for attack within AI agents, it allows for early detection and mitigation of vulnerabilities, thereby enhancing overall system security.

DeepKeep's Policy Enforcement feature assures real-time security by allowing for immediate action on the detection of any potential threats or breaches. Instead of reacting to security incidents after they have happened, this feature enables proactive measures, reducing the potential for damage and strengthening the AI system's overall security robustness.

Yes, DeepKeep can certainly be used for SaaS Security. Its comprehensive security measures and adaptable design make it suitable for SaaS environments. It can offer protection from development through to real-time use across the entire AI lifecycle in a SaaS context.

Absolutely. DeepKeep is designed for versatility and can be deployed for on-premises security. Its robust security solutions can be effectively implemented in an on-premises environment to provide full-lifecycle protection for AI applications, models, and agents.

Multimodal Functionality in DeepKeep is an innovative feature that understands text, images, and video as a single signal. This allows for a more integrated and comprehensive evaluation against the intent of every request, enhancing security by judging each request in its complete context rather than as separate components.

DeepKeep can be highly useful in the context of AI Applications Security. It offers end-to-end security solutions for AI applications, including runtime detection, guardrails, adaptive evaluation, robustness tests, and real-time policy enforcement. DeepKeep ensures that AI applications are protected throughout their lifecycle, from development to real-time use.

DeepKeep plays a crucial role in AI lifecycle protection by offering security measures loaded into AI systems from the moment they are developed until they are in real-time use. By considering the whole lifecycle, DeepKeep keeps AI applications, models, and agents secure and trustworthy at all times, allowing businesses to use AI with confidence.

Yes, DeepKeep supports Adaptive Evaluation and Robustness Testing. With its AI Red Teaming feature, it conducts adaptive evaluation to identify potential weaknesses and carry out robustness tests to ensure that the AI systems are durable enough to withstand various adversarial attacks.

Yes, you can use DeepKeep in an air-gapped system. DeepKeep's versatile design allows it to operate in various environments, including on-premises, SaaS, and even systems that are air-gapped, meaning they are isolated from other networks for enhanced security.

Rather than focusing on post-incident responses, DeepKeep emphasizes real-time security and immediate action. Its policy enforcement feature enables it to react immediately to potential threats, thus preventing incidents rather than responding to them after the fact. This proactive approach enhances overall system security and mitigates potential damage posed by threats.

Yes, DeepKeep can handle text, images, and video security simultaneously. It uses Multimodal Functionality to understand and evaluate these different types of data as a single signal. This allows it to judge each request comprehensively, taking into account the complete context, rather than treating each type of data individually.