Your AI security expert. From AI asset inventory to AI data defense.

AI security is the practice of protecting AI systems, the data they consume, and the actions they take from threats that exploit the unique characteristics of artificial intelligence. This includes defending against data poisoning, prompt injection, model theft, supply chain compromise, and overprivileged AI agent access. AI security is distinct from traditional cybersecurity because AI systems are data-dependent, non-deterministic, and increasingly autonomous.

AI-SPM is the practice of continuously discovering, assessing, and monitoring security risks across an organization's AI assets, including models, agents, data pipelines, and third-party AI tools. AI-SPM provides foundational visibility into your AI footprint. However, AI-SPM alone focuses on posture awareness, meaning what exists and how it's configured. It does not provide the context or remediation needed for complete AI security. Relyance AI treats AI-SPM as a capability within its broader data defense platform.

AI security protects AI systems from threats. AI governance is the framework of policies, oversight, and compliance controls that ensures AI is deployed responsibly. Security is defense. Governance is management. In practice, they depend on each other: governance without security is unenforceable, and security without governance is reactive. Relyance AI addresses both through a unified platform. For a full treatment of governance, see our definitive guide to AI governance.

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

The most significant AI security threats facing enterprises in 2026 are data poisoning (corrupting training data to manipulate AI outputs), agentic AI risk (autonomous agents with overprivileged access acting as insider threats at machine speed), AI supply chain compromise (vulnerabilities in third-party models, MCP servers, and open-source components), prompt injection (manipulating AI agents into executing unauthorized actions), and identity exploitation (AI-generated deepfakes and non-human identity sprawl). The shift from generative AI to agentic AI has fundamentally expanded the attack surface because agents execute real-world actions, not just generate content.

MCP (model context protocol) servers connect AI agents to external services like databases, cloud platforms, SaaS tools, and APIs. They are becoming the standard mechanism for AI agent interaction with external systems. Each MCP server is a trusted channel that grants AI agents access to sensitive resources. A compromised or misconfigured MCP server can give an attacker direct access to enterprise data through a channel that security tools implicitly trust. Relyance AI provides automated discovery and continuous risk assessment for third-party MCP servers across the enterprise.

Wiz and Palo Alto Networks offer AI-SPM as a feature within their cloud-native application protection platforms (CNAPPs). Their approach starts from cloud infrastructure posture and adds AI asset discovery on top. Relyance AI takes a data-first approach—starting from the data journey and mapping AI risk in the context of what data is flowing, who or what is accessing it, and why. This surfaces compound risks that cloud-centric tools miss because they don’t map the relationships between AI agents and the sensitive data they access. This surfaces compound risks that cloud-centric tools miss because they do not map the relationships between AI agents and the sensitive data they access. Relyance also delivers contextual remediation and autonomous policy monitoring, not just posture alerts.

Relyance AI continuously discovers AI assets across your environment through agentless, API-first scanning across code repositories, CI/CD pipelines, cloud runtime, SaaS applications, and third-party integrations. This includes first-party models, third-party AI tools, AI features embedded within SaaS vendor applications, and unsanctioned AI tools adopted by teams without IT approval. Each discovered asset is mapped to the data it accesses, the permissions it holds, and the risk it introduces.

Relyance AI maps AI assets and data flows to obligations under the EU AI Act, ISO 42001, NIST AI RMF, GDPR, SOC 2, and sector-specific regulations including SOX, HIPAA, and NIS2. The platform continuously detects potential compliance gaps across first-party, third-party, and SaaS vendor AI and generates audit-ready evidence in seconds.

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).

Relyance AI is built for CISOs, information security teams, data engineering teams, and AI governance leads at mid-size and enterprise organizations deploying AI agents and tools at scale. It is designed for companies running AI in production across cloud services, databases, and enterprise applications that need unified visibility and control over what AI systems can access and do.