AegisLayer Architecture
AI-Native Security, Governance, and Runtime Protection
A public reference architecture for secure, governed, observable, and auditable AI systems.
Overview
The future of artificial intelligence depends not only on intelligence, but also on trust.
AegisLayer is an AI-native security and governance initiative developed by VND TECH LLC. It explores architectural approaches for securing autonomous and agentic AI systems through runtime governance, policy enforcement, controlled execution, evidence preservation, human oversight, and defense in depth.
This repository serves as the public architecture, research, and educational hub for AegisLayer.
The Problem
Modern AI systems increasingly interact with:
- Enterprise applications
- Cloud infrastructure
- APIs and external tools
- Databases and sensitive information
- Autonomous workflows
- Business-critical systems
- Physical and digital environments
These capabilities create risks that conventional application security was not designed to address fully.
AI systems may be exposed to:
- Prompt injection
- Unauthorized tool use
- Excessive privileges
- Credential compromise
- Data poisoning
- Model and dependency tampering
- Supply-chain attacks
- Policy bypass
- Sensitive-data exposure
- Manipulated outputs
- Inadequate auditability
- Unsafe autonomous execution
AegisLayer investigates how security controls can be placed around AI reasoning and execution without relying exclusively on the AI system to regulate itself.
Mission
Our mission is to advance trustworthy AI by developing and documenting architectures that improve security, governance, transparency, accountability, and operational resilience for autonomous AI systems.
Core Principles
Security by Design
Security controls should be part of the architecture from the beginning rather than added after deployment.
Governance by Default
AI actions should operate within defined authority, policy, and accountability boundaries.
Evidence Before Trust
Important decisions and actions should generate sufficient evidence for verification, review, and audit.
Least Privilege
AI agents, tools, users, and services should receive only the minimum authority required for a specific task.
Human Accountability
Human oversight and clearly assigned responsibility remain essential for consequential actions.
Defense in Depth
No single security control should be treated as sufficient. Identity, authorization, policy, runtime controls, monitoring, evidence, and recovery should work together.
Fail-Closed Execution
When authority, policy, identity, or system state cannot be verified, execution should stop safely rather than proceed by assumption.
Architectural Focus Areas
AegisLayer research and development focuses on:
- AI runtime security
- Autonomous-agent governance
- Identity and access control
- Policy-based execution
- Capability and permission management
- Secure tool and API use
- Approval workflows
- Evidence generation and preservation
- Immutable and tamper-evident auditing
- AI observability
- Incident detection and response
- Model and data supply-chain security
- Zero-trust architecture for AI systems
- Operational resilience and recovery
Conceptual Control Flow
User or System Request
|
v
Identity and Authority Validation
|
v
Context and Risk Evaluation
|
v
Policy Enforcement
|
v
Approval and Human Oversight
|
v
Controlled Runtime Execution
|
v
Evidence and Audit Generation
|
v
Monitoring, Verification, and Response