Relyance AI
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About
Relyance AI is an AI-native data security and governance platform designed to manage the complexities of modern data flows across enterprise environments. Unlike legacy security tools that rely on periodic static scans and manual workflows, this platform introduces the concept of Data Journeys. This technology tracks information in motion, following data from its origin in source code through cloud runtimes and into AI models or third-party SaaS applications. By providing this continuous, real-time visibility, the platform eliminates the security blind spots often created by rapid development cycles and the proliferation of shadow data. The platform is partitioned into three specialized expert modules: Data Security, AI Governance, and Privacy. The Data Security Expert focuses on dynamic Data Security Posture Management (DSPM), offering features like breach simulation and sensitive data discovery. The AI Governance Expert is specifically tailored for the AI era, helping organizations discover their first and third-party AI footprints, monitor agentic AI use, and maintain compliance with emerging standards like the EU AI Act. Finally, the Privacy Expert automates labor-intensive compliance tasks, including Data Subject Request (DSR) fulfillment and the maintenance of Records of Processing Activities (RoPA). Relyance AI is primarily built for high-growth enterprises and Fortune 500 companies with complex data ecosystems. It serves CISOs, Privacy Officers, and Engineering teams simultaneously. For security leaders, it provides a code-to-cloud defense that catches secrets and policy violations as they happen. Privacy teams use the platform to transform manual audits into automated, strategic operations. Meanwhile, engineering teams benefit from an AI-native architecture that provides feedback within existing DevOps workflows, ensuring that security and privacy reviews do not become a bottleneck for innovation. What differentiates Relyance AI is its commitment to real-time intelligence and flexible deployment. It moves beyond data-at-rest scanning to provide a live map of how data is actually being used. Organizations can choose from several deployment models, including a managed SaaS version or an InHost deployment that keeps the platform entirely within the customer's own Virtual Private Cloud (VPC). This flexibility, combined with the ability to provide total visibility into vendor services within minutes of connection, makes it a robust solution for companies needing to balance aggressive AI adoption with strict regulatory requirements.
Pros & Cons
Maps complete data journeys in real-time rather than relying on static scans.
Offers InHost deployment for keeping the platform within a customer's own VPC.
Provides visibility into vendor services within 15 minutes of initial connection.
Integrates directly with DevOps for security feedback during the development phase.
Supports complex global regulations like GDPR, CCPA, and the EU AI Act.
No transparent public pricing is available without requesting a demo.
Advanced identity intelligence and policy features are restricted to higher tiers.
Comprehensive implementation may require cross-departmental coordination during setup.
Use Cases
CISOs can monitor live data journeys from code to cloud runtime to detect unauthorized access or data exfiltration in real-time.
Privacy Officers can automate the fulfillment of Data Subject Requests and generate real-time RoPAs to maintain GDPR and CCPA compliance.
Engineering teams can implement shift-left security by identifying potential policy violations in source code before features reach production.
Compliance managers can use the AI Governance module to evaluate risks from third-party AI models and ensure alignment with the EU AI Act.
Platform
Task
Features
• consent management
• real-time policy enforcement
• ai model inventory
• dsr fulfillment automation
• automated data mapping
• shadow ai detection
• code-to-cloud dspm
• data journeys tracking
FAQs
How does Relyance AI handle deployment for security-conscious organizations?
The platform offers multiple flexible deployment options, including a full SaaS model and an InHost option. InHost allows the platform to run entirely within the customer's own Virtual Private Cloud, ensuring maximum data control and sovereignty.
Can the platform detect AI tools that have not been approved by IT?
Yes, the AI Governance Expert specifically identifies shadow AI usage across the organization by monitoring data flows. It provides an inventory of third-party models and agents, allowing teams to assess and onboard them securely.
Does Relyance AI support compliance with the EU AI Act?
The AI Governance module includes specific readiness templates for the EU AI Act, as well as NIST RMF and ISO 42001. It automates the risk assessment and documentation processes needed to maintain regulatory alignment.
What makes Data Journeys different from traditional data scanning?
Traditional tools scan data at rest, whereas Data Journeys tracks data in motion from source code to cloud runtime. This provides a continuous, real-time map of how data actually flows through systems and AI models.
Pricing Plans
Data Security Expert
Unknown Price• Data Store Inventory
• Asset Lifecycle Management
• Source Code Analysis
• Risk Monitoring
• Data Journeys
• Identity Intelligence
• Compliance Reporting
AI Governance Expert
Unknown Price• AI Model Inventory
• AI Risk Assessment Template
• EU AI Act Readiness
• Shadow AI Onboarding
• Real-time AI Monitoring
• Agentic AI Visibility
Privacy Expert
Unknown Price• Automated Data Mapping
• DSR Fulfillment Automation
• Consent Management
• Universal RoPAs
• Privacy Rights Automation
• Guided Assessments
Job Opportunities
Senior Software Engineer - AI Backend
Secure enterprise data and AI initiatives with real-time visibility from code to cloud using automated data mapping, governance, and privacy compliance tools.
Benefits:
End-to-end ownership and technical autonomy
Opportunity to build a generational AI Security product
Work with a global team in a fast-moving, high-impact environment
Competitive compensation and strong growth opportunities
Experience Requirements:
Excellent experience with Python backend development
Production level experience building solutions using with LLMs & Agentic AI systems(LangChain, LangGraph, PyTorch)
Strong experience with Docker & Kubernetes in production
Experience with MongoDB
Strong understanding of GraphQL
Other Requirements:
Experience with Terraform
Knowledge of API security & rate limiting
Experience with AWS, GCP, or Azure
Responsibilities:
Design and build production-grade backend services in Python that power AI-driven security products
Implement LLM-powered agent workflows for real-world AI Security use cases
Design, implement, and maintain GraphQL APIs for internal and customer-facing systems
Model, store, and optimize data in MongoDB for performance and scale
Containerize applications with Docker and deploy, scale, and operate them using Kubernetes
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Ratings & Reviews
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