The rise of agentic AI creates new security and governance challenges as autonomous systems access data, make decisions, and act across enterprise environments. Built on seven core principles, Grafyn’s AI-TRUST™ Framework is a proprietary AI security framework that helps enterprises move from fragmented AI oversight to structured, principle-based governance.
Agentic AI introduces a fundamentally new security challenge. Unlike traditional software, AI agents can reason, access sensitive data, invoke tools, and take actions across systems with increasing autonomy. This expands the enterprise attack surface and creates new risks around identity, trust, behavior, permissions, and knowledge integrity. The AI-TRUST Framework provides a structured model for organizations to understand, secure, and govern agentic systems, helping reduce risk, strengthen oversight, and enable trusted AI adoption at scale.
Understand where an agent comes from, how it is built, and what powers it. Agent Lineage helps organizations trace the models, tools, prompts, resources, and dependencies behind every AI system so they can assess provenance, accountability, and risk with clarity.
Trace agent origins, components, and dependencies
Map connected models, tools, prompts, and resources
Strengthen accountability across the AI lifecycle
Know who is acting, what permissions they hold, and whether access is appropriate. Identity focuses on securing agent identities, inherited permissions, and access paths so organizations can reduce overprivilege and strengthen control.
Establish clear identity and ownership for agents
Detect excessive or misused permissions
Enforce least-privilege access across workflows
Agents are only as trustworthy as the knowledge they rely on. Trusted Knowledge helps validate the quality, integrity, and reliability of data, context, and external sources so organizations can reduce the risk of poisoned, manipulated, or low-trust inputs.
Validate the integrity of knowledge sources
Detect poisoning, tampering, and low-trust content
Improve confidence in AI-driven decisions and outputs
Measure how far an agent’s access, actions, and influence can extend if something goes wrong. Risk helps organizations identify exposure, prioritize critical weaknesses, and reduce blast radius across agentic systems.
Assess potential impact and blast radius
Identify high-risk actions, connections, and dependencies
Prioritize security gaps based on business exposure
Visibility is essential in agentic environments. Usage & Observability helps organizations monitor how agents behave across systems, detect anomalies, and understand activity in real time so threats and misuse do not go unnoticed.
Monitor agent behavior across tools and workflows
Detect abnormal activity and policy violations
Improve runtime visibility and investigation readiness
Not all data should be equally accessible to AI systems. Sensitivity focuses on identifying sensitive data exposure and ensuring that access, handling, and usage are governed appropriately across agentic workflows.
Identify exposure to sensitive and regulated data
Govern how agents access and handle critical information
Reduce the risk of leakage, misuse, and overexposure
Every connected system introduces a trust decision. Trust Boundary helps organizations define the limits of an agent’s authority and understand whether it can move beyond approved boundaries across tools, environments, and systems.
Define and enforce boundaries of agent authority
Detect cross-system movement and boundary violations
Contain lateral spread across connected environments
Understand where an agent comes from, how it is built, and what powers it. Agent Lineage helps organizations trace the models, tools, prompts, resources, and dependencies behind every AI system so they can assess provenance, accountability, and risk with clarity.
Trace agent origins, components, and dependencies
Map connected models, tools, prompts, and resources
Strengthen accountability across the AI lifecycle
Know who is acting, what permissions they hold, and whether access is appropriate. Identity focuses on securing agent identities, inherited permissions, and access paths so organizations can reduce overprivilege and strengthen control.
Establish clear identity and ownership for agents
Detect excessive or misused permissions
Enforce least-privilege access across workflows
Agents are only as trustworthy as the knowledge they rely on. Trusted Knowledge helps validate the quality, integrity, and reliability of data, context, and external sources so organizations can reduce the risk of poisoned, manipulated, or low-trust inputs.
Validate the integrity of knowledge sources
Detect poisoning, tampering, and low-trust content
Improve confidence in AI-driven decisions and outputs
Measure how far an agent’s access, actions, and influence can extend if something goes wrong. Risk helps organizations identify exposure, prioritize critical weaknesses, and reduce blast radius across agentic systems.
Assess potential impact and blast radius
Identify high-risk actions, connections, and dependencies
Prioritize security gaps based on business exposure
Visibility is essential in agentic environments. Usage & Observability helps organizations monitor how agents behave across systems, detect anomalies, and understand activity in real time so threats and misuse do not go unnoticed.
Monitor agent behavior across tools and workflows
Detect abnormal activity and policy violations
Improve runtime visibility and investigation readiness
Not all data should be equally accessible to AI systems. Sensitivity focuses on identifying sensitive data exposure and ensuring that access, handling, and usage are governed appropriately across agentic workflows.
Identify exposure to sensitive and regulated data
Govern how agents access and handle critical information
Reduce the risk of leakage, misuse, and overexposure
Every connected system introduces a trust decision. Trust Boundary helps organizations define the limits of an agent’s authority and understand whether it can move beyond approved boundaries across tools, environments, and systems.
Define and enforce boundaries of agent authority
Detect cross-system movement and boundary violations
Contain lateral spread across connected environments
Understand where an agent comes from, how it is built, and what powers it. Agent Lineage helps organizations trace the models, tools, prompts, resources, and dependencies behind every AI system so they can assess provenance, accountability, and risk with clarity.
Trace agent origins, components, and dependencies
Map connected models, tools, prompts, and resources
Strengthen accountability across the AI lifecycle
Know who is acting, what permissions they hold, and whether access is appropriate. Identity focuses on securing agent identities, inherited permissions, and access paths so organizations can reduce overprivilege and strengthen control.
Establish clear identity and ownership for agents
Detect excessive or misused permissions
Enforce least-privilege access across workflows
Agents are only as trustworthy as the knowledge they rely on. Trusted Knowledge helps validate the quality, integrity, and reliability of data, context, and external sources so organizations can reduce the risk of poisoned, manipulated, or low-trust inputs.
Validate the integrity of knowledge sources
Detect poisoning, tampering, and low-trust content
Improve confidence in AI-driven decisions and outputs
Measure how far an agent’s access, actions, and influence can extend if something goes wrong. Risk helps organizations identify exposure, prioritize critical weaknesses, and reduce blast radius across agentic systems.
Assess potential impact and blast radius
Identify high-risk actions, connections, and dependencies
Prioritize security gaps based on business exposure
Visibility is essential in agentic environments. Usage & Observability helps organizations monitor how agents behave across systems, detect anomalies, and understand activity in real time so threats and misuse do not go unnoticed.
Monitor agent behavior across tools and workflows
Detect abnormal activity and policy violations
Improve runtime visibility and investigation readiness
Not all data should be equally accessible to AI systems. Sensitivity focuses on identifying sensitive data exposure and ensuring that access, handling, and usage are governed appropriately across agentic workflows.
Identify exposure to sensitive and regulated data
Govern how agents access and handle critical information
Reduce the risk of leakage, misuse, and overexposure
Every connected system introduces a trust decision. Trust Boundary helps organizations define the limits of an agent’s authority and understand whether it can move beyond approved boundaries across tools, environments, and systems.
Define and enforce boundaries of agent authority
Detect cross-system movement and boundary violations
Contain lateral spread across connected environments
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