Your data security expert.
From static scans to dynamic data defense

DSPM is the practice of discovering, classifying, and monitoring sensitive data across an organization's cloud, SaaS, and on-premise environments to identify security risks and compliance gaps. Traditional DSPM tools scan data at rest to answer the question "what sensitive data do we have and where is it?" This foundational visibility is essential, but in today's AI-driven enterprise, it is the starting point for data security, not the destination.

Dynamic DSPM traces data in motion rather than scanning data at rest. Traditional DSPM takes periodic snapshots of your data landscape. Dynamic DSPM continuously maps how data flows across code, cloud, SaaS, third parties, and AI systems in real time, capturing every transformation, API call, and model interaction. This means you see not just what data exists, but what it's doing, where it's going, who or what is accessing it, and why. Relyance AI pioneered this approach through its proprietary Data Journeys technology.

Traditional DSPM was designed for a world where data sat in databases and applications were deterministic. In the AI era, data instructs software, AI agents access and transform data autonomously, and data flows across code, cloud, SaaS, MCP servers, and third parties at speeds no periodic scan can track. Traditional DSPM misses data in code repositories and CI/CD pipelines, runtime data transformations, data flowing into and out of AI models and agents, and the identity relationships between AI agents and sensitive data. These are the exact blind spots where the most dangerous risks live.

DSPM focuses on securing an organization's data assets: discovering, classifying, and monitoring sensitive data across environments. AI-SPM focuses on securing AI assets: discovering AI models, agents, tools, and monitoring their security posture. In practice, the two are deeply connected because AI risk depends entirely on what data AI systems can access. Tools that separate DSPM from AI-SPM create the blind spots they claim to eliminate. Relyance AI unifies both as capabilities within a single data defense platform, so you see the full picture of how AI and data interact.

Compound risk is a security vulnerability that forms when individually low-risk elements combine to create a critical threat. For example, an AI agent alone may not be dangerous. A database with customer PII may be properly secured. But an AI agent with administrative access to that database creates a lethal combination that no individual scan would flag. Compound risks are invisible to tools that assess data posture and identity posture separately. Relyance AI detects compound risks by correlating data sensitivity, identity permissions, and access behavior in real time.

Most DSPM vendors discover and classify sensitive data in cloud storage and databases — but stop there. They don't follow data as it moves, track how AI systems access it, or connect it back to the source code where risk originates. Relyance AI takes a data-first approach—starting from the data journey itself and tracing how data flows from code to cloud to AI model to third party. This means Relyance discovers sensitive data in places other DSPM tools miss: source code, CI/CD pipelines, runtime transformations, and AI training and inference flows. Relyance also maps identity-to-data relationships for both human and non-human identities, delivers contextual remediation (not just alerts), and unifies data security with AI security and privacy in a single platform.

Identity-to-data intelligence maps how human users, service accounts, AI agents, and automated workflows gain access to sensitive data. It traces permission chains across your environment to identify overprivileged access, dormant permissions, and risky combinations of identity and data sensitivity. In the age of agentic AI, non-human identities (AI agents, MCP servers, automated pipelines) often outnumber human users and carry significant access privileges. Identity-to-data intelligence makes these invisible relationships visible.

Relyance AI discovers and classifies data across AI models, AI agents, MCP servers, and third-party AI tools alongside traditional data stores, cloud services, and SaaS applications. The platform traces how data enters AI training pipelines, how AI agents access sensitive data at runtime, and how data flows between AI systems and other enterprise assets. This unified view across AI and non-AI data assets is what enables compound risk detection.

Relyance AI maps data flows and security posture to obligations under GDPR, CCPA/CPRA, HIPAA, SOX, PCI DSS, NIST CSF, ISO 27001, and sector-specific regulations. The platform continuously detects potential compliance gaps and generates audit-ready evidence in seconds, with every data flow linked to its relevant legal, contractual, and policy obligations.

No. Relyance AI is fully agentless and API-first, designed for petabyte-scale enterprise environments. Deployment options include full SaaS (managed cloud for rapid deployment), InHost (runs entirely within your VPC), and DirectConnect (private link between your network and the platform).

The data security expert is built for CISOs, security engineering teams, data engineering teams, and compliance leads at mid-size and enterprise organizations. It is designed for companies with complex data ecosystems spanning cloud, SaaS, code repositories, and AI systems that need unified visibility into where sensitive data lives, how it moves, who or what accesses it, and whether it complies with policy.