Audit Logs

Audit Logs provide a complete record of important activities performed within a Karyam workspace.

Every significant action—from creating an Agent to approving a workflow or updating platform settings—is recorded to create an immutable history of platform activity.

Audit Logs help organizations understand who performed an action, what changed, and when it happened.

Why Audit Logs Exist

As AI systems become part of business-critical operations, organizations need visibility into administrative and operational changes.

Examples include:

Security investigations
Compliance reporting
Operational reviews
Change management
Incident analysis

Audit Logs provide the accountability required to operate AI systems in production.

What Gets Recorded?

Audit Logs capture significant events across the platform.

Examples include:

Platform Administration

Workspace updates
Team changes
Role assignments
API Token management

AI Development

Agent creation
Agent updates
AI Flow changes
Model configuration updates
MCP Server configuration
Vector Database configuration

Governance

Approval requests
Approval decisions
Category updates
Service Catalog publishing

Operations

Listener configuration
Schedule changes
Knowledge ingestion events
File management activities

Audit Information

Each audit record typically contains:

Event
Resource
User
Timestamp
Action performed
Previous state (when applicable)
New state (when applicable)

This provides complete visibility into how the platform evolves over time.

Example Timeline

09:15  Agent Created
09:18  AI Flow Updated
09:25  MCP Server Connected
09:40  Approval Granted
09:52  API Token Generated
10:05  Vector Database Updated

Every important activity contributes to the audit history.

Security Benefits

Audit Logs help security teams answer questions such as:

Who changed an Agent?
When was a Model updated?
Who approved this execution?
When was an API Token generated?
Who connected an MCP Server?
When was a workspace setting modified?

This information is essential for security investigations and operational reviews.

Compliance

Many organizations must maintain records of administrative activity for regulatory or internal compliance requirements.

Audit Logs support these needs by providing:

Accountability
Traceability
Change history
Operational transparency

They create a reliable historical record without requiring manual documentation.

Audit Logs vs Runs

Although both provide visibility, they focus on different aspects of the platform.

Runs	Audit Logs
Records AI execution	Records platform activity
Captures workflow execution	Captures administrative actions
Shows execution status	Shows configuration changes
Used for debugging AI systems	Used for governance and compliance

Together, they provide complete operational visibility.

Relationship to Other Concepts

Users
    ↓
Platform Actions
    ↓
Audit Logs

AI Execution
    ↓
Runs

Knowledge Processing
    ↓
RAG Runs

Audit Logs complement Runs and RAG Runs by recording changes made to the platform itself rather than the execution of AI systems.

The Karyam Philosophy

Trustworthy AI requires more than intelligent models.

It requires complete visibility into how the platform is configured, managed, and used.

Audit Logs provide the accountability organizations need to operate AI systems with confidence.