Core Principles

1. Explainability

What it means: Every automation action is understandable, traceable, and justifiable.

How we implement it:

Bot Execution History & Audit Trails

Every bot execution is recorded with timestamps, status, and detailed activity logs
Capture of exact actions taken, errors encountered, and outcomes
Searchable execution history for forensic analysis and debugging
Complete visibility into which bot performed what action, when, and with what credentials

Example: When autobotAI enriches a security finding with threat intelligence, the execution log shows:

The exact threat and vulnerability data sources queried (e.g., VirusTotal, Trivy, Crowdstrike, Azure Defender, AbuseIPDB)
The decision criteria applied
Why the bot classified the threat as high/medium/low risk
What remediations were recommended and why

Visual Workflow Builder

All automation logic is built visually, making workflows transparent
Non-technical stakeholders can review what automation will do before execution
Each step in the workflow is labeled with its purpose and expected output
Conditional logic is clearly shown (if/then decision points)

AI Assistant and Agent Reasoning

When autobotAI's AI Assistant generates a workflow and/or multi-agent workflows executed, the reasoning is captured
Users see the suggested steps and can understand why each step was recommended
Generated workflows with agent nodes or generative AI nodes or deterministic action nodes can be edited or rejected before deployment
Context about the workflow's intended use case is documented as part of bot title, description, use case type and severity for business operation

Contextual Summaries for Approvals

When approvals are required, LLM-driven summaries explain the proposed action in plain language
Technical details are abstracted for human decision-makers but if low level details required then the same can be captured from workflow execution logs
Key risks and implications are highlighted
Evidence supporting the recommendation is provided

How to use this: Review bot execution logs to understand what happened. Check audit trails to prove compliance. Enable stakeholders to review workflow logic before deployment.

2. Accountability

What it means: Clear ownership and responsibility for every automated action and decision.

How we implement it:

Human-in-the-Loop Approvals

Critical operations require human authorization before execution
Approval workflows capture who approved what, when, and their justification
Escalation paths ensure decisions are made by appropriate authority levels
No automation can execute critical actions without documented human sign-off

Example in Security Operations:

Incident response playbooks trigger for detected threats but pause for human approval
Security analyst reviews threat context and chooses to remediate
Action is logged with analyst name, approval timestamp, and decision rationale
If Security analyst or remediation task owner rejects the recommendation, alternative steps like adding details to risk register is being done

Role-Based Access Control

Workspace isolation ensures only authorized personnel can create, modify, or approve workflows
Permission boundaries control who can execute automations with sensitive access
Different roles have different approval thresholds (e.g., low-risk: auto-approve; high-risk: requires manager)
Access logs show who modified workflows and when

Audit Trail for Compliance

Every action taken by automation is logged with full context
User who triggered the automation (manual or scheduled)
Exact resources affected and changes made
Outcome (success/failure) and any errors encountered
Complete chain of custody for sensitive operations

Governance Structure

Policy enforcement happens before automation execution
Workflows should be reviewed and approved by center of excellence governance committees
Business rules and security policies are enforced at each decision point
Violations trigger escalation and halt workflow

How to use this: Assign clear approvers for each automation type. Review approval logs for accountability. Use role-based access to control who can execute sensitive automations.

3. Reproducibility

What it means: Automation produces consistent, verifiable results every time it runs.

How we implement it:

Version-Controlled Bot Library

Every bot workflow is versioned and tracked
Previous versions can be restored with bot import/export feature
Templates ensure standardized approaches to common tasks

Standardized Workflow Templates in Library

Common security and compliance tasks use proven, tested templates
Parameters are documented and validated
Customer's CoE team can define and design workflows following best practices and organizational policies
New bots inherit standards from templates

Documented Execution Parameters

Each workflow has defined inputs, environment variables, and configuration
Expected behaviors under different conditions are documented
Edge cases and error handling are specified upfront
Assumptions about data quality and system state are recorded

Execution History & Replay Capability

Past executions can be reviewed to validate consistency
Parameters used in past runs are stored and comparable
Failed executions can be debugged by replaying with same conditions
Trend analysis at dashboard shows whether bot behavior is consistent over time

How to use this: Use workflow templates and AI assistant for new automations. Document workflow parameters. Review agent node provided tools (MCP/API/full-code/CLI/ or query based node) and execution history to spot inconsistencies between defined use case and operational flow.

4. Fairness

What it means: Automation produces equitable, unbiased outcomes across different users, environments, and contexts.

How we implement it:

Multi-Source Data Validation

Threat intelligence checks multiple sources (VirusTotal, AbuseIPDB, internal feeds like MISP) to avoid single-source bias
Compliance rules are validated against multiple frameworks (SOC 2, GDPR, RBI, and other industry-specific standards)
Decisions incorporate diverse data perspectives before conclusions are drawn

Contextual Decision-Making

AI evaluators and agentic nodes analyze rich context before making recommendations
Decisions are not based on single factors but comprehensive assessment designed by customer
Edge cases and special circumstances are considered with verification and human in loop
Similar situations receive similar treatment

Testing Across Diverse Environments

Workflows are validated across different customer environments and use cases
Bot performance is monitored for consistency across different data patterns
Fairness metrics track whether automation treats all entities equally
Results are disaggregated by customer type, asset type, and use case to spot disparities

Bias Detection in Agentic and GenAI powered Workflows

AI Assistant generated workflows should be reviewed for potential bias before deployment
AI Agent node, GenAI nodes and approval/notification node system prompt can be tuned by customer in a way to prevent bias
Historical outcomes can be analyzed by workflow to spot patterns of unfair treatment
Sensitive decision points are marked for additional human review
Feedback loops capture in agent session memory and long term memory when users override AI recommendations

Human Review of AI Recommendations

AI-generated recommendations are not auto-approved without human oversight
Critical decisions require explicit human confirmation
Users can override AI suggestions with documented reasoning
Override patterns are analyzed to improve AI fairness

How to use this: Enable multi-source validation for critical decisions. Review AI-generated workflows before deployment. Monitor execution outcomes for bias patterns. Document and analyze human overrides.

5. Human-Centric

What it means: AI augments and empowers human decision-makers rather than replacing Security operation owner's judgment.

How we implement it:

Mandatory Approval Nodes

Critical security and compliance decisions require human approval
Approvers receive rich context to make informed decisions
Approval is mandatory—not a rubber stamp, but an active decision point
Easy rejection paths ensure humans can override automation and workflow can update risk register based on provided reason.

AI Suggestions Require Human Validation

autobotAI proposes actions, humans decide whether to execute
Workflow can be designed to make sure AI cannot autonomously make high-impact decisions.
Humans see what the AI recommends and why, then decide
If human disagrees with AI, the human decision is documented and details are captured for session memory and long term memory.

Override Capabilities

At every decision point in a workflow, humans can pause and review.
Approved overrides are logged and explained.
Humans can manually execute different actions than recommended.
System learns from overrides to improve future recommendations.

Context-Aware Notifications

Security Operation owner / analyst receive alerts with pre-digested context
Relevant information is highlighted to support quick decision-making
Historical context is available for informed decisions
Notification fatigue is minimized by intelligent filtering

Analyst Empowerment

Workflows are designed to reduce analyst cognitive load
Routine investigations are automated; complex decisions stay with remediation and response owner
Analysts spend time on high-value analysis, not data gathering and execution of remediation steps.
Customizable workflows let analysts define their preferred approach.

How to use this: Design workflows with approval gates for critical actions. Ensure AI recommendations include reasoning. Allow easy human override like review at code repo or approval message at slack/ms teams. Monitor how often humans override AI to improve recommendations with maintained risk register.

6. Security

What it means: Protecting customer data, workflow integrity, and automation security throughout the platform.

How we implement it:

Zero-Trust Architecture

Workspace isolation ensures customer data is never shared.
Each customer's workflows and data are siloed.
Cross-workspace access is prevented at infrastructure level.
Service-to-service authentication is enforced with serverless identity authentication.

Data Privacy Framework with self-hosted autobotAI workspace

Customer data remains in customer workspaces.
autobotAI does not access customer data except as authorized.
Data is encrypted in transit and at rest.
Customers control data retention and deletion.

Encryption Standards

All data in transit uses TLS 1.2 or higher.
Data at rest is encrypted using industry-standard algorithms.
Encryption keys are managed securely and rotated regularly with provider's default key mechanism.
Sensitive credentials are tokenized and encrypted.

Secure AI Integration

Third-party LLM calls (Amazon Bedrock, OpenAI) use secure, encrypted channels.
Prompts and responses do not contain customer PII unless explicitly needed. This can be additional controlled by LLM API provider using Guardrails.
Customer data is not used to train third-party models.
API calls are authenticated and authorized with respective AI provider authentication mechanism.

Permission-Based Execution

Bots / Agentic workflows execute only with the minimum permissions required
Credentials are scoped to specific resources and time periods.
Bot / Agentic workflow actions are restricted by role and policy.
Failed authorization attempts are logged.

Audit Logging for Security

All security-relevant actions are logged with context.
Login attempts, permission changes, and sensitive actions are recorded.
Logs are immutable and stored securely.
Log retention meets compliance requirements.

How to use this: Deploy autobotAI in isolated workspaces. Use role-based access controls. Enable encryption for all data. Monitor security audit logs regularly.

7. Compliance

What it means: Ensuring automation aligns with regulatory requirements and customer-specific compliance needs.

How we implement it:

Compliance Insights Dashboard

Real-time visibility into which resources are compliant
IT resources (Cloud and Container environment) Violations are detected and flagged automatically
Remediation recommendations are generated with evidence
Compliance status trends are tracked over time

Automated Compliance Checks

Workflows automatically scan for common compliance issues (e.g., unencrypted data, overprivileged access)
Policies are encoded into automation rules
Checks run continuously without manual intervention
Results feed into compliance reporting

Regulatory Framework Support

autobotAI supports major compliance frameworks (SOC 2, HIPAA, GDPR, RBI etc.)
Workflows can be configured to enforce specific regulatory requirements
Evidence collection is automated to support audit readiness
Compliance documentation is generated automatically

Evidence Collection for Audits

Compliance checks generate detailed evidence
Remediation actions are fully documented
Audit trails show who authorized each compliance action
Reports can be exported for external auditors

AI Guardrails for Compliance

Content filtering and advanced reasoning provided by LLM provider can provide AI-generated workflows from violating compliance rules.
Safety checks with agent system prompts ensure recommendations don't breach policies.
Sensitive data handling is validated before execution.
autobotAI can integrate with partner compliance tools like Wiz, Crowdstrike, trendmciro etc to automate violation remediation.

Customer-Specific Requirements

Workflows can be customized to match customer compliance needs
Policy definitions are flexible and customer-configurable
Compliance rules can be added by integrating additional external posture management solutions like CSPM, KSPM, AISPM etc.
Override processes for exceptions are documented and controlled

Data Retention & Deletion

Customer controls data retention policies.
Optional Automated deletion enforces retention limits.

How to use this: Configure compliance policies relevant to your industry. Enable automated compliance checks. Review compliance insights regularly. Maintain audit-ready documentation.