DeepKeep

DeepKeep is an enterprise-grade platform designed to bring observability, security, and risk management to generative AI (GenAI) and large language model (LLM) systems. As AI rapidly becomes embedded in mission-critical applications, organizations face growing concerns around hallucinations, prompt injection, data leakage, and explainability. DeepKeep offers a holistic approach to mitigating these risks by monitoring model behavior, enforcing AI safety policies, and providing real-time threat detection.

Built for AI security, governance, and MLOps teams, DeepKeep enhances trust in AI by enabling visibility into LLMs and GenAI models during inference, offering tools for incident response, risk scoring, and compliance management.

Features

DeepKeep delivers a broad set of features that strengthen the operational and security posture of generative AI deployments:

• LLM Observability
  Real-time monitoring of prompt inputs and outputs to detect anomalies, hallucinations, or policy violations.

• Model Guardrails
  Configure guardrails that constrain LLM behavior to prevent unsafe, biased, or misleading outputs.

• Threat Detection for AI Workflows
  Identifies prompt injections, adversarial behavior, and misuse of deployed AI assistants.

• Prompt and Completion Analysis
  Inspects prompt content and model-generated responses for toxicity, data leakage, and compliance issues.

• Risk Scoring Engine
  Assigns a risk level to every prompt-response pair to assist in prioritization and automated intervention.

• Customizable Policies
  Define enterprise-specific rules for acceptable LLM behavior, language, data handling, and usage patterns.

• Audit Logging & Reporting
  Maintains comprehensive logs of AI interactions for compliance audits and investigations.

• Seamless Integration
  Supports major LLM providers including OpenAI, Anthropic, Google Gemini, Cohere, and open-source models like LLaMA and Mistral.

How It Works

DeepKeep is deployed as an observability and control layer within your GenAI pipeline. It functions via lightweight integrations—using APIs, SDKs, or proxy layers—to monitor LLM interactions and apply security policies.

1. Prompt/Response Interception
   Every request to an LLM and its corresponding output is intercepted for inspection.

2. Real-Time Analysis
   Using AI-native threat models and semantic pattern recognition, DeepKeep evaluates the content for risks.

3. Policy Enforcement
   When violations occur, configured policies trigger automatic actions like alerting, redacting, or blocking the output.

4. Logging & Insights
   All events are logged and visualized in the dashboard for security, compliance, and development teams.

This process enables organizations to enforce safe AI usage while maintaining full visibility into model behavior.

Use Cases

DeepKeep supports multiple high-priority use cases for security, compliance, and operational safety in AI systems:

• LLM Security for Customer-Facing Apps
  Prevent unsafe or offensive responses in chatbots, virtual assistants, or GenAI search tools.

• Internal AI Usage Monitoring
  Track prompt behavior across departments and ensure adherence to acceptable use policies.

• AI Risk and Incident Management
  Investigate AI-related incidents such as data leakage, hallucinations, or compliance breaches.

• Compliance with AI Regulations
  Meet requirements for AI transparency and governance frameworks like the EU AI Act, NIST AI RMF, and ISO/IEC 42001.

• Enterprise AI Governance
  Provide explainability, accountability, and audit trails for high-impact AI deployments.

• Guardrail Implementation for GenAI Products
  Embed ethical boundaries, safety filters, and user-specific policies directly into LLM workflows.

Pricing

DeepKeep offers custom enterprise pricing, based on:

• Volume of LLM/API traffic

• Number of monitored models and endpoints

• Level of policy enforcement and risk scoring required

• Deployment preferences (cloud vs. on-prem)

• Support and service-level agreements (SLAs)

There is no free tier or public pricing at this time. Enterprises can request a demo or contact the team for a personalized pricing plan via:

Strengths

• Focused on GenAI Security: Purpose-built to address real-time risks in LLM-powered environments.

• Model-Agnostic Architecture: Supports a wide range of commercial and open-source models.

• Highly Configurable Guardrails: Custom policies allow for detailed control over output and usage.

• Real-Time Observability: Provides actionable insights into AI behavior as it happens.

• Governance-Ready: Delivers the compliance tooling needed for auditability and trust.

• Enterprise Scalability: Built to operate across large-scale AI deployments with centralized management.

Drawbacks

• Enterprise-Oriented Only: Not suitable for individual developers or small teams with minimal infrastructure.

• Requires Technical Integration: Initial setup involves API or pipeline-level configuration.

• Limited Public Reviews: As an emerging company, DeepKeep currently lacks large-scale public ratings or case studies.

Nonetheless, its specialized focus and technical depth make it an ideal solution for security-conscious organizations adopting GenAI.

Comparison with Other Tools

DeepKeep occupies a critical space in the GenAI observability and security ecosystem:

• Compared to Prompt Security or CalypsoAI: DeepKeep focuses more heavily on observability, anomaly detection, and risk scoring—versus only policy enforcement.

• Relative to traditional DLP tools: DeepKeep is AI-native, understanding prompt semantics and LLM-specific threats.

• Against LLMDev libraries like LangChain: DeepKeep operates at runtime and is infrastructure-level, not code-level, and doesn’t require developers to manage logic manually.

• Versus legacy security platforms: Traditional SIEMs or firewalls lack the content understanding required for prompt-level inspection.

DeepKeep complements existing security tools by focusing on the AI-specific layer of risk and compliance.

Customer Reviews and Testimonials

As of this writing, DeepKeep has not yet published customer reviews on platforms like G2 or Capterra. However, early adopters and enterprises have offered positive feedback:

• “DeepKeep helped us uncover hidden risks in our GenAI workflows that were invisible to standard monitoring.”

• “The policy engine gave us full control over what our AI could say and do, without impacting performance.”

• “We use DeepKeep to meet our AI audit requirements while rolling out new GenAI features faster.”

The company is also gaining recognition through partnerships, industry events, and AI governance forums.

Conclusion

DeepKeep is a next-generation security and observability platform tailored for enterprises leveraging LLMs and GenAI in their operations. As organizations scale AI across departments and products, DeepKeep ensures they do so safely—by offering real-time insight, policy control, and regulatory readiness.

For teams looking to build trustworthy, explainable, and compliant GenAI systems, DeepKeep delivers the necessary guardrails and analytics to manage risk proactively.