Security & Trust Architecture
iAgentic provides deterministic policy enforcement, centralized governance authority, and audit survivability for operationally serious enterprise AI systems.
Zero Trust Enforcement
Every AI request is independently evaluated and authorized against centralized policy before execution is allowed. Fail-closed by default — when anything is uncertain, the answer is always DENY.
Learn moreIdentity-Aware Control
Integration with enterprise identity providers via OIDC and SAML. Agents authenticate via client credentials grant. Human users via standard OIDC flows. Identity context flows through every governance decision.
Learn moreData Protection
Configurable detection and redaction of sensitive data at the enforcement layer. Five data classifications: Public, Internal, Confidential, Restricted, and Regulated — each with configurable governance behavior.
Learn moreTenant Isolation
Strict logical isolation of data and policies between tenants within the platform architecture.
Learn moreImmutable Decision Evidence
Append-only records of every decision with cryptographic integrity hash, providing a verifiable chain of custody for forensic reconstruction. Records are never updated or deleted.
Learn moreOperational Assurance
Complete visibility into policy enforcement, system health, decision patterns, and policy lineage.
Learn moreSecurity Architecture Principles
Deterministic Enforcement
Governance logic is compiled into immutable decision bundles, ensuring that enforcement is predictable and not subject to probabilistic model drift.
Decoupled Authority
The management of authoritative policies is physically and logically separated from the runtime execution, preventing application-level bypasses.
Identity-Linked Governance
Every AI interaction is securely tied to a verified enterprise identity, enabling granular role-based access control (RBAC) and enterprise accountability.
Audit Survivability & Chain of Custody
All system decisions and enforcement actions are recorded in an append-only ledger. This enables complete decision replayability and forensic reconstruction for ultimate compliance defensibility.
Attack Paths Contained at Runtime
How iAgentic blocks common AI attack vectors through pre-execution policy enforcement — not post-hoc detection.
Prompt Injection → Tool Invocation
The Attack
Adversarial input manipulates a copilot into calling unauthorized APIs or writing to restricted data stores through a legitimate integration channel.
Why Monitoring Fails
Post-hoc monitoring detects the anomaly after execution. Prompt filters operate on syntax patterns and miss the semantic intent of the escalation.
Runtime Containment
Runtime intent normalization extracts what the AI intends to do. The Policy Engine evaluates normalized intent against centralized rules and blocks unauthorized tool invocations before execution.
Agent Privilege Escalation
The Attack
An autonomous agent discovers tool access beyond its intended scope through chained actions, progressively escalating its operational privileges.
Why Monitoring Fails
Application-level permissions are static and do not govern runtime tool invocation. The agent operates within its granted API scope but exceeds its intended operational boundaries.
Runtime Containment
Centralized RBAC enforcement at the execution layer evaluates every tool invocation independently, regardless of the agent's granted API access. Privileges are enforced at runtime, not at deployment.
Sensitive Data Exposure via Routing
The Attack
Model or tool routing decisions expose sensitive data to unauthorized endpoints — such as routing patient data to a non-compliant model provider or region.
Why Monitoring Fails
Network-level controls do not evaluate data sensitivity in the context of AI requests. Routing decisions are made by infrastructure, not by governance policy.
Runtime Containment
Context-aware policy evaluation classifies data sensitivity before routing. The Enforcement Fabric ensures that sensitive data is only routed to authorized, compliant endpoints.
Policy Bypass via Embedded Logic
The Attack
Application-level governance checks are bypassed when agents invoke tools directly through APIs, outside the intended application workflow path.
Why Monitoring Fails
Decentralized governance embedded in application code cannot enforce consistent policy when execution paths diverge from the intended workflow.
Runtime Containment
Externalized policy authority is evaluated independently at runtime. Every request passes through the Enforcement Fabric regardless of the invocation path.
Secrets & Credential Handling
iAgentic is designed to integrate with enterprise-grade secrets management systems to ensure the security of credentials used for AI infrastructure.
Agents Never Hold Keys
LLM provider API keys are never held by agents, never stored in application databases, and never transmitted between the agent and the gateway.
Secure Injection Flow
Admin provisions key through control plane. Key is written to enterprise secret store. Sync layer delivers to runtime. Gateway injects at request time. Agent never sees the key.
Immediate Rotation & Revocation
Rotating a key in the secret store takes effect immediately for all agents. Deleting a key immediately blocks all requests. Zero agent updates required.
Tenant-Isolated Credentials
Each tenant has its own keys stored at tenant-specific paths. One tenant's compromise cannot affect another tenant's credentials or execution.
Shared Responsibility Model
Security in the iAgentic ecosystem is a shared responsibility between iAgentic and the enterprise customer.
iAgentic Responsibility
- Security of the Enforcement Fabric runtime and orchestration
- Integrity and availability of the immutable decision audit store
- Logical isolation of multi-tenant data and control plane infrastructure
Enterprise Responsibility
- Policy definition, authoring, and lifecycle management
- Identity provider integration and access management configuration
- Management of deployment-specific secrets and network security