Implementation in autobotAI Use Cases

Security Operations: Incident Response

Explainability: When a bot detects and enriches a suspected breach:

• Execution logs show exact threat intelligence sources consulted
• AI reasoning explains why threat confidence is high/medium/low
• Evidence is presented (malicious IP reputation, file hash verdicts, etc.)
• Remediation recommendation includes supporting context

Accountability:

• Alert triggers bot-driven investigation, then pauses for human approval
• Security analyst reviews enriched threat data and AI recommendation
• Analyst approves or rejects remediation action
• Action is logged with analyst name, timestamp, and decision

Fairness:

• Similar threats are handled consistently regardless of affected asset
• Alert enrichment uses multiple threat intelligence sources
• Remediation thresholds are uniform across the environment

Human-Centric:

• Bot automates investigation (data gathering)
• Human makes containment decision (approve/reject/modify)
• Analyst can override recommendation if needed
• System learns from overrides

Security:

• Bot credentials are limited to read operations
• Remediation actions require escalated approval
• All actions logged to audit trail

Compliance:

• Incident response proves timely detection and remediation
• Audit trail documents evidence and decisions
• Automated response meets SLA requirements

Compliance Automation: Policy Violations

Explainability: When a bot finds a compliance violation from posture management integration:

• Specific rule that was violated is documented
• Evidence showing the violation is captured (e.g., "data not encrypted")
• AI suggestion explains why the issue needs remediation
• Expected compliance outcome is clear

Accountability:

• Remediation workflow requires approval from resource owner to make corrective action.
• Resource owner confirms the fix is appropriate
• Action is logged with resource owner's name, approval time and reason.

Fairness:

• All resources are checked consistently against the same policies
• Similar violations receive similar remediation
• Resources applicable to a rule are checked; non-applicable resources are skipped
• No resources are treated arbitrarily

Human-Centric:

• Bot identifies violations, humans decide remediation approach
• Compliance team can customize policies and thresholds
• Override capability for exceptions is available

Security:

• Remediation actions are minimized (least privilege)
• Only authorized personnel can approve fixes

Compliance:

• Violations are captured for audit reports
• Evidence of remediation is documented
• Automated checks ensure continuous compliance

Access Management: Just-in-Time Provisioning

Explainability: When a bot processes an access request:

• Reason for access request is captured.
• AI recommendation explains why access is approved/denied or sent to authorized human approval.
• Risk assessment is shown (user history, resource sensitivity, IP location and reputation etc.).
• Approval conditions are listed.

Accountability:

• Access approval is made by appropriate manager if access involves any calculated risk.
• Approval is logged with manager name, reason and timestamp.
• Access grant is traceable to specific approval.
• Revocation is automatic when need expires.

Fairness:

• Similar access requests are evaluated consistently.
• No biased favoritism in approvals.
• All requests follow same evaluation criteria.

Human-Centric:

• Manager reviews request and makes approval decision when required and low risk and short time access are provided automatically.
• AI suggests duration and scope; manager can override.
• Requests can be approved, denied, or conditionally approved

Security:

• Access tokens are scoped and time-limited.
• Credentials used for access are secured and logged.
• Revocation is enforced when terms expire.

Compliance:

• Access decisions are documented for audit
• Evidence supporting approvals is captured
• Compliant with least-privilege principles