Trust Is Enforced at Runtime - Not at Login
Traditional security and privacy controls were built for humans logging into applications. Identity systems, access controls, and DLP policies assume static users, long-lived credentials, and predictable data
AI agents don’t work that way.
They act autonomously, chain tools dynamically, reuse data across contexts, and operate continuously; making access, intent, and data use inseparable. An agent can be fully authorized, trigger no DLP alert, and still misuse data by combining, inferring, or propagating it beyond its intended purpose.
Operant enforces trust at runtime, at the point of action — governing not just who can access a system, but what an AI agent is allowed to do with data, in what context, and for what purpose
The Operant Trust Fabric
Operant enforces runtime trust through a protocol-gapped Trust Fabric that sits between AI agents and the systems they interact with.
Instead of embedding logic into agents or modifying application protocols, Operant enforces policy at the communication boundary — where requests are made, data flows, and actions occur. This allows Operant to govern identity, access, and data behavior per action, without trusting agent behavior or prompt logic.
The Trust Fabric acts as a runtime control plane for AI systems, continuously evaluating each interaction against centrally defined trust rules before allowing it to proceed
Identity and Authorization at Machine Speed
Traditional IAM authenticates humans at login and grants standing permissions. That model breaks down for AI agents that act continuously, invoke tools dynamically, and operate across systems.
Operant treats every participant: AI agents, tools, APIs, and data services as a first-class identity. Each action is evaluated in real time against centrally defined trust rules, and if permitted, Operant issues a short-lived, least-privilege credential scoped to that specific action.
There are no standing privileges and no long-lived secrets inside agents. Trust is evaluated continuously, not assumed.
Privacy Is Enforced Where Data Flows
Privacy failures in AI systems rarely come from breaches. Instead, they come from authorized misuse, inference, and unintended propagation of sensitive data.
Operant enforces privacy controls in the data path, not in prompts or agent logic. Sensitive data flows are routed through policy-driven sanitization and transformation components before reaching an agent or downstream system.
This prevents data retrieved in one context from being reused in another and ensures that privacy policies are enforced technically, not just documented.
Proof of Enforcement
AI systems need more than logs — they need provable evidence of how decisions were made and how data was used.
Every policy decision, credential issuance, and data access event in Operant generates a signed, tamper-evident record at the network layer. This creates an immutable evidence stream that supports audit, forensics, compliance, and post-incident analysis.
Protocol-Gapped Enforcement, Enabled by NDN
Operant enforces trust without modifying application protocols, agent frameworks, or model behavior because it is built on Named Data Networking (NDN); a data-centric networking architecture designed for secure, machine-to-machine systems.
NDN allows Operant to enforce identity, authorization, and data governance at the communication layer itself, rather than inside agents or applications. This creates a protocol-gapped trust fabric where enforcement is external, non-bypassable, and independent of how AI agents are implemented or how they behave.
Because trust lives in the network, Operant can evaluate and authorize each action at runtime, issue scoped credentials, govern data flows, and generate signed evidence. All without embedding logic into models, prompts, or APIs.
Multi-Dimensional Trust
Every interaction is verified across identity, context, policy, and scope. Trust is continuously re-established
Cross-Environment
Whether on-prem, in the cloud, or at the edge, trust policies follow the data removing the gaps at system boundaries
Invisible by Design
Our trust fabric operates as a protocol-gapped overlay, making it nearly impossible to detect, target, or disrupt
Blocks Session Hijacking
Updates and Insights from the Operant Team
Want to know more?
Get in touch and we'll get back to you as soon as we can. We look forward to hearing from you!