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https://issues.apache.org/jira/browse/RANGER-5278?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Selvamohan Neethiraj updated RANGER-5278:
-----------------------------------------
Description:
Design and implement an Agentic AI component that integrates with Apache Ranger
to enable autonomous access control policy recommendations, real-time anomaly
detection, and closed-loop governance based on user behavior, audit logs, and
contextual metadata.
The goal is to improve proactive data security and reduce the manual overhead
of policy management.
h3. *Objectives / Scope:*
*1. Connect to Apache Ranger APIs*
* Read existing policies and audit logs
* Access metadata about resources, users, and roles
*2. Behavior Analysis Module*
* Ingest and analyze user access logs
* Identify frequent access patterns and policy gaps
*3. Agentic AI Core Engine*
* Implement reasoning engine (LLM-based)
* Generate intelligent policy suggestions (e.g., “User X should not access
Resource Y after 6PM”)
* Detect and flag anomalous behavior (e.g., off-hours access, privilege
escalation, etc.)
*4. Policy Suggestion & Feedback Loop*
* Generate human-readable policy change suggestions
* Provide options for manual approval or automatic update
* Support audit trail and rollback capability
*5. UI/UX Integration*
* Frontend dashboard to view agent outputs
* Admin interface to approve/reject agent suggestions
{*}6. Security & Logging{*}{*}{*}
* Ensure logs of AI actions and decisions are captured
* Add safeguards against unintended policy changes
h3. *Acceptance Criteria:*
* Agent connects to Apache Ranger and retrieves audit logs and policy metadata
* AI agent analyzes patterns and recommends policy updates with confidence
scores
* Anomaly detection is demonstrated using historical audit log patterns
* All agent recommendations are logged and optionally require admin approval
* No impact to existing Ranger operations or policies without approval
* Demo and report generated with at least 3 use cases (e.g., over-privileged
user, abnormal access time, stale policy)
was:
Design and implement an Agentic AI component that integrates with Apache Ranger
to enable autonomous access control policy recommendations, real-time anomaly
detection, and closed-loop governance based on user behavior, audit logs, and
contextual metadata.
The goal is to improve proactive data security and reduce the manual overhead
of policy management.
h3. *Objectives / Scope:*
# {*}Connect to Apache Ranger APIs{*}{*}{*}
** Read existing policies and audit logs
** Access metadata about resources, users, and roles
# {*}Behavior Analysis Module{*}{*}{*}
** Ingest and analyze user access logs
** Identify frequent access patterns and policy gaps
# {*}Agentic AI Core Engine{*}{*}{*}
** Implement reasoning engine (LLM-based)
** Generate intelligent policy suggestions (e.g., “User X should not access
Resource Y after 6PM”)
** Detect and flag anomalous behavior (e.g., off-hours access, privilege
escalation, etc.)
# {*}Policy Suggestion & Feedback Loop{*}{*}{*}
** Generate human-readable policy change suggestions
** Provide options for manual approval or automatic update
** Support audit trail and rollback capability
# {*}UI/UX Integration{*}{*}{*}
** Frontend dashboard to view agent outputs
** Admin interface to approve/reject agent suggestions
# {*}Security & Logging{*}{*}{*}
** Ensure logs of AI actions and decisions are captured
** Add safeguards against unintended policy changes
h3. *Acceptance Criteria:*
* Agent connects to Apache Ranger and retrieves audit logs and policy metadata
* AI agent analyzes patterns and recommends policy updates with confidence
scores
* Anomaly detection is demonstrated using historical audit log patterns
* All agent recommendations are logged and optionally require admin approval
* No impact to existing Ranger operations or policies without approval
* Demo and report generated with at least 3 use cases (e.g., over-privileged
user, abnormal access time, stale policy)
> Build Agentic AI Agent for Apache Ranger to Automate Policy Suggestions and
> Anomaly Detection
> ---------------------------------------------------------------------------------------------
>
> Key: RANGER-5278
> URL: https://issues.apache.org/jira/browse/RANGER-5278
> Project: Ranger
> Issue Type: New Feature
> Components: Ranger
> Reporter: Selvamohan Neethiraj
> Priority: Major
>
> Design and implement an Agentic AI component that integrates with Apache
> Ranger to enable autonomous access control policy recommendations, real-time
> anomaly detection, and closed-loop governance based on user behavior, audit
> logs, and contextual metadata.
> The goal is to improve proactive data security and reduce the manual overhead
> of policy management.
> h3. *Objectives / Scope:*
> *1. Connect to Apache Ranger APIs*
> * Read existing policies and audit logs
> * Access metadata about resources, users, and roles
>
> *2. Behavior Analysis Module*
> * Ingest and analyze user access logs
> * Identify frequent access patterns and policy gaps
>
> *3. Agentic AI Core Engine*
> * Implement reasoning engine (LLM-based)
> * Generate intelligent policy suggestions (e.g., “User X should not access
> Resource Y after 6PM”)
> * Detect and flag anomalous behavior (e.g., off-hours access, privilege
> escalation, etc.)
>
> *4. Policy Suggestion & Feedback Loop*
> * Generate human-readable policy change suggestions
> * Provide options for manual approval or automatic update
> * Support audit trail and rollback capability
>
> *5. UI/UX Integration*
> * Frontend dashboard to view agent outputs
> * Admin interface to approve/reject agent suggestions
>
> {*}6. Security & Logging{*}{*}{*}
> * Ensure logs of AI actions and decisions are captured
> * Add safeguards against unintended policy changes
>
> h3. *Acceptance Criteria:*
> * Agent connects to Apache Ranger and retrieves audit logs and policy
> metadata
> * AI agent analyzes patterns and recommends policy updates with confidence
> scores
> * Anomaly detection is demonstrated using historical audit log patterns
> * All agent recommendations are logged and optionally require admin approval
> * No impact to existing Ranger operations or policies without approval
> * Demo and report generated with at least 3 use cases (e.g., over-privileged
> user, abnormal access time, stale policy)
>
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