AegisLayer High-Level Architecture¶

Purpose¶

This document presents the public, high-level architecture for AegisLayer. It describes conceptual control layers, trust boundaries, and execution flow without exposing proprietary implementation details, confidential controls, customer environments, or patent-sensitive material.

AegisLayer is intended to help secure and govern AI systems that can reason, select tools, access data, trigger workflows, and influence real-world systems.

Architectural Objective¶

The core objective is to separate AI capability from execution authority.

An AI system may be able to propose an action, but execution should require independent checks for identity, authority, policy, risk, approval, runtime constraints, and evidence generation.

Conceptual Architecture¶

Users, Systems, and AI Agents
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| 1. Identity and Trust Context    |
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| 2. Intent and Request Analysis   |
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| 3. Policy and Risk Evaluation    |
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| 4. Approval and Authority Gate   |
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| 5. Controlled Execution Runtime  |
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| 6. Evidence and Audit Layer      |
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| 7. Monitoring and Response       |
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External Tools, Data, and Systems

1. Identity and Trust Context¶

This layer establishes who or what is requesting an action and under what authority.

Conceptual responsibilities include:

User, service, agent, and workload identity
Authentication status
Session and device context
Delegated authority
Role and permission context
Trust level and risk indicators
Request origin and provenance

A request should not be treated as authorized merely because it originated from a capable AI system.

2. Intent and Request Analysis¶

This layer converts a proposed action into a structured request that can be evaluated consistently.

Conceptual responsibilities include:

Requested objective
Target system or resource
Proposed action
Data categories involved
Required tools or connectors
Expected impact
Reversibility
Time and scope constraints

The purpose is to make the action understandable before it is authorized or executed.

3. Policy and Risk Evaluation¶

This layer evaluates the request against explicit rules and contextual risk.

Potential inputs include:

Identity and authority
Requested capability
Target resource
Data sensitivity
Operational context
Historical behavior
Threat indicators
Business and regulatory requirements

Potential outcomes include:

Allow
Deny
Allow with constraints
Require additional evidence
Require human approval
Escalate for specialist review

Policy evaluation should be independent of the model that proposed the action.

4. Approval and Authority Gate¶

Consequential or exceptional actions may require approval before execution.

Conceptual controls include:

Risk-based approval thresholds
Multi-party approval
Separation of duties
Time-limited authorization
Explicit exception handling
Human confirmation for irreversible actions
Revalidation when context changes

Approval should be specific to the action, scope, target, and time period.

5. Controlled Execution Runtime¶

The runtime layer constrains how authorized actions are performed.

Conceptual responsibilities include:

Capability-scoped execution
Least-privilege access
Tool and connector isolation
Input and output validation
Resource limits
Timeouts and cancellation
Idempotency where appropriate
Safe retries and compensation
Fail-closed behavior

The runtime should not grant broader authority than the policy and approval layers authorized.

6. Evidence and Audit Layer¶

Important decisions and actions should produce evidence sufficient for later review.

Potential evidence includes:

Request identity and origin
Structured intent
Policy inputs and decision
Approval records
Selected tools and capabilities
Execution events
Outputs and status
Errors and recovery actions
Correlation and causation identifiers
Timestamps and integrity information

Evidence should be protected against unauthorized alteration and retained according to applicable requirements.

7. Monitoring and Response¶

This layer observes behavior across the full lifecycle and supports detection, containment, and recovery.

Conceptual responsibilities include:

Security telemetry
Behavioral baselines
Policy violation detection
Credential and connector anomalies
Unusual tool sequences
Data-access anomalies
Incident correlation
Automated containment where authorized
Human escalation
Recovery and post-incident review

Trust Boundaries¶

AegisLayer treats the following as distinct trust domains:

Human users
AI models and agents
Policy and approval services
Execution runtimes
Connectors and external tools
Data stores
Evidence and audit systems
Administrative control planes

Crossing a trust boundary should involve explicit validation, authorization, and observability.

Core Security Properties¶

The public architecture aims to support the following properties:

Least Privilege¶

Each actor receives only the capabilities required for a specific task.

Explicit Authority¶

Execution authority is defined separately from model capability.

Policy Before Execution¶

Policy evaluation occurs before an action is allowed to reach an external system.

Evidence by Design¶

Evidence generation is part of the execution lifecycle rather than an optional afterthought.

Fail-Closed Control¶

When required identity, policy, approval, or system state cannot be verified, execution should stop or escalate.

Defense in Depth¶

Identity, policy, runtime, evidence, monitoring, and response controls reinforce one another.

Conceptual Request Lifecycle¶

1. Receive request
2. Establish identity and authority
3. Normalize intent and target
4. Evaluate policy and contextual risk
5. Obtain approval when required
6. Issue constrained execution capability
7. Execute within controlled runtime
8. Generate evidence and telemetry
9. Verify outcome
10. Close, compensate, contain, or escalate

Deployment Considerations¶

AegisLayer may be adapted conceptually to different environments, including:

Cloud AI platforms
Enterprise agent systems
Model-serving infrastructure
Internal copilots
Tool-using AI assistants
MLOps environments
Regulated workflows
Hybrid and on-premises systems

The specific deployment model, controls, and implementation details depend on the organization, risk profile, and operating environment.

Architectural Boundaries¶

This public document does not specify:

Production source code
Proprietary algorithms
Customer-specific controls
Internal administrative procedures
Confidential infrastructure
Secret material
Unapproved patent-sensitive implementation details

Current Status¶

This document describes a public reference architecture and research direction. It does not claim that every component is currently deployed, independently certified, or sufficient to prevent every cyberattack.