The Shadow AI Detection Framework (SAiDF) is a behavioral investigation methodology designed to identify, analyze, and reconstruct AI-mediated execution occurring across trusted enterprise systems.
Traditional security controls focus primarily on identities, permissions, authentication events, policy enforcement, and discrete actions. As AI capabilities become embedded within browsers, SaaS platforms, copilots, workflow engines, orchestration systems, and autonomous agents, organizations increasingly face situations where every individual action is authorized, logged, and policy-compliant, yet the resulting behavior becomes difficult to explain, attribute, or govern.
SAiDF was developed to help security teams investigate these situations by reconstructing execution chains that span multiple systems, identities, workflows, and trust boundaries.
The framework is designed to complement existing security architectures, including Zero Trust, rather than replace them.
Most security investigations focus on individual events:
- Who authenticated?
- What resource was accessed?
- Which permission was used?
- Which policy was triggered?
These questions remain essential.
However, AI-enabled environments introduce a different challenge:
Multiple authorized actions may combine into a larger execution path whose overall behavior is never evaluated as a single unit.
This can result in:
- Attribution gaps
- Behavioral ambiguity
- Fragmented audit trails
- Excessive trust propagation
- Unintended downstream execution
- Governance blind spots
SAiDF addresses this challenge by treating behavior as a sequence rather than a collection of isolated events.
The framework consists of four investigative layers.
The Identity Layer examines:
- Human identities
- Service identities
- OAuth grants
- Delegated permissions
- Authentication events
- Trust inheritance paths
Key Question:
Who was authorized to perform the observed actions?
The Behavior Layer examines:
- Workflow activity
- User interactions
- AI-generated actions
- Automation patterns
- Decision propagation
- Cross-system activity sequences
Key Question:
What behavior emerged across systems?
The Data Layer examines:
- Data access patterns
- Data movement
- Data transformation
- Retrieval activity
- Content generation
- Information propagation
Key Question:
How did information move through the environment?
The Execution Layer examines:
- Workflow execution
- Trigger chains
- Automation engines
- SaaS integrations
- API activity
- Downstream actions
Key Question:
What execution path was ultimately created?
SAiDF investigations follow six stages.
Initial indicators identify unusual activity.
Examples:
- Excessive AI interactions
- Unexpected workflow execution
- Unusual OAuth activity
- Cross-system automation behavior
- Abnormal data-access patterns
Output:
Potential AI-mediated activity.
Investigators identify:
- Users
- Service accounts
- Applications
- OAuth relationships
- Delegated authority
Output:
Trust and authorization map.
Investigators determine:
- What information was accessed
- What information was generated
- What information was transferred
- Which systems exchanged data
Output:
Data-flow reconstruction.
Events are correlated across:
- SaaS platforms
- Browsers
- Endpoints
- Identity providers
- Workflow systems
- Security monitoring platforms
Output:
Cross-system activity timeline.
The complete execution chain is reconstructed.
Example:
User → Copilot → SharePoint → Power Automate → CRM System → External Connector
Output:
Execution-chain model.
Investigators evaluate:
- Intent visibility
- Trust propagation
- Accountability gaps
- Governance controls
- Behavioral ownership
Output:
Attribution analysis.
A completed SAiDF investigation produces:
- Trust map
- Data-flow map
- Behavioral timeline
- Execution-chain reconstruction
- Attribution assessment
- Governance findings
- Detection opportunities
SAiDF operationalizes the concept of Unmodeled Execution.
Unmodeled Execution describes behavior that emerges from chains of individually authorized actions whose combined effect is never evaluated, attributed, or governed as a single execution path.
SAiDF provides the methodology used to identify, investigate, and reconstruct these execution paths.
SAiDF is intended for:
- Security Operations Centers (SOC)
- Threat Hunting Teams
- Incident Response Teams
- Security Architects
- AI Governance Programs
- Enterprise Risk Teams
- Zero Trust Initiatives
The framework is technology-agnostic and can be applied across cloud, SaaS, hybrid, and enterprise environments.
SAiDF is an active research project within the Shadow AI research program.
Related research includes:
- Shadow AI Operational Maturity Model (SAOMM)
- Unmodeled Execution
- Trust Inheritance Analysis
- Attribution Gap Assessment
- AI-Mediated Execution
This framework is released for research, educational, and defensive-security purposes.
Feedback, contributions, and collaboration are welcome.