StarkAI is a subsidiary of John Reed Stark Consulting that provides consulting services for companies relating to AI, such as risk and security assessments of AI security, audits of AI policies, practices and procedures, building a successful AI framework and training attorneys to best leverage AI safely, securely and effectively. Attorneys who master AI-powered legal strategies, tools and techniques won't just survive -- they'll dominate. They'll handle more matters, achieve better outcomes and deliver more value. They'll also earn respect from clients and adversaries alike because they respond fast, produce clean documents, and present data-driven arguments. StarkAI provides consulting, audits, training, etc. on all aspects of artificial intelligence, especially how white collar defense attorneys (such as SEC enforcement defense lawyers) can best leverage AI in defense, litigation and all other aspects of legal representation. StarkAI services include:

-- AI Risk and Security Assessment

In an era where artificial intelligence drives critical business decisions and operations, ensuring the security, compliance, and reliability of AI systems has become paramount. Our AI Security Audit service provides organizations with comprehensive assessment and validation of their AI infrastructure, models, and governance frameworks -- from development through deployment and continuous monitoring. Our AI Audit practice combines deep technical expertise with regulatory knowledge to deliver thorough evaluations of your organization's AI ecosystem. We assess vulnerabilities across the entire AI lifecycle, evaluate compliance with emerging regulations, and provide actionable recommendations to strengthen your AI security posture while enabling innovation.

Our audit approach follows a structured, risk-based methodology aligned with leading frameworks:

  1. Discovery & Scoping: Comprehensive inventory of AI assets, models, and data flows
  2. Risk Assessment: Threat modeling and vulnerability identification across the AI stack
  3. Technical Testing: Automated scanning, manual testing, and adversarial simulation
  4. Compliance Review: Mapping against regulatory requirements and standards
  5. Analysis & Prioritization: Risk scoring and business impact assessment
  6. Reporting & Recommendations: Clear, actionable findings with implementation guidance
  7. Knowledge Transfer: Training and capability building for internal teams

At the conclusion of the audit, StarkAI provides a strategic overview of AI security posture with prioritized risks, business impact analysis, and roadmap for any remediation. If appropriate, John Reed Stark will prepare a sworn declaration providing an expert assessment of a company or firm's AI security, which can be used for everything from remediation and defending challenges to briefing Boards of Directors, employees and other critical company constituencies to marketing collateral for concerned clients and customers.

-- Building a Successful AI Security Framework

As organizations accelerate AI adoption across critical business functions, establishing a robust security framework is no longer optional—it's a strategic imperative. Our AI Security Framework Development service helps enterprises design, implement, and operationalize comprehensive AI security programs that protect against emerging threats while enabling responsible innovation at scale. We partner with organizations to build tailored AI security frameworks that integrate seamlessly with existing enterprise architecture while addressing the unique challenges of AI systems. Our approach combines security engineering, risk management, and governance expertise to create frameworks that are both technically robust and operationally practical—ensuring your AI initiatives are secure by design, compliant by default, and resilient in production. Framework architecture components include:

1. AI Security Strategy & Roadmap

  • Vision & Objectives Setting: Align AI security goals with business strategy and risk appetite
  • Maturity Assessment: Baseline current capabilities and define target state architecture
  • Strategic Roadmap Development: Phased implementation plan with clear milestones and success metrics
  • Investment Planning: Resource allocation and technology stack recommendations
  • Stakeholder Alignment: Cross-functional engagement model for security, data science, and business teams

2. Secure AI Development Lifecycle (SAIDLC)

  • Security-by-Design Principles: Embed security controls throughout the ML pipeline
  • Development Standards: Secure coding practices for AI/ML applications and model development
  • Testing Protocols: Automated security testing, adversarial validation, and robustness checks
  • CI/CD Integration: Security gates and automated scanning in MLOps pipelines
  • Model Registry & Versioning: Secure model management with cryptographic signing and integrity verification

3. AI-Specific Security Controls

  • Threat Protection Layer: Real-time defenses against prompt injection, data poisoning, and model extraction
  • Input Validation & Sanitization: Comprehensive filtering for malicious inputs and adversarial examples
  • Output Security Controls: PII detection, hallucination prevention, and response validation
  • Access Management: Fine-grained permissions for models, data, and AI services
  • Behavioral Monitoring: Anomaly detection for unusual model behavior or usage patterns

4. Data Governance & Privacy Framework

  • Data Classification System: AI-specific categorization for training, validation, and inference data
  • Privacy-Preserving Techniques: Implementation of differential privacy, federated learning, and secure multi-party computation
  • Data Lineage Architecture: End-to-end tracking from source systems through model outputs
  • Consent Management: Automated consent tracking and purpose limitation enforcement
  • Data Minimization Strategies: Techniques for reducing sensitive data exposure in AI workflows

5. Risk Management & Compliance Program

  • AI Risk Taxonomy: Comprehensive catalog of AI-specific risks and threat vectors
  • Risk Assessment Methodology: Quantitative and qualitative evaluation frameworks
  • Control Mapping: Alignment with NIST AI RMF, ISO standards, and regulatory requirements
  • Compliance Automation: Tools and processes for continuous compliance monitoring
  • Third-Party Risk Management: Vendor assessment criteria and supply chain security protocols

6. Operational Security & Monitoring

  • Security Operations Center (SOC) Integration: AI-specific threat detection and response playbooks
  • Continuous Monitoring Platform: Real-time visibility into model performance and security metrics
  • Incident Response Framework: AI-specific incident classification, investigation, and remediation procedures
  • Threat Intelligence Integration: Feeds for emerging AI vulnerabilities and attack patterns
  • Security Metrics & KPIs: Dashboard design for executive reporting and operational monitoring

7. Governance & Accountability Structure

  • AI Security Governance Board: Charter, roles, and operating procedures
  • Policy Framework: Comprehensive policies covering AI ethics, security, and acceptable use
  • Approval Workflows: Risk-based model approval and deployment gates
  • Audit & Assurance Program: Internal audit procedures and external validation requirements
  • Training & Awareness: Role-based security training for developers, users, and executives

-- Creating the Best AI Policies, Practices and Procedures

The explosive growth of AI tool adoption has created an urgent need for comprehensive usage policies that balance innovation with security. Our AI Usage Policy & Governance Development service helps organizations establish clear guidelines, practical procedures, and robust training programs that enable employees to harness AI's transformative power while protecting against data leakage, compliance violations, and reputational risks. We partner with organizations to develop tailored AI usage policies that address the reality of modern workplace AI adoption, which is growing dramatically every day. Our approach transforms shadow AI from a security threat into managed innovation, creating frameworks that employees will actually follow while maintaining enterprise security and compliance standards. Core service components include:

1. AI Usage Policy Development

  • Acceptable Use Framework: Clear guidelines defining authorized vs. prohibited AI applications
  • Tool Authorization Matrix: Pre-approved AI tools with specific use cases and restrictions
  • Data Classification Guidelines: Rules for handling different data sensitivity levels
  • Ethical Usage Standards: Principles for responsible AI adoption aligned with organizational values
  • Compliance Requirements: Integration with regulatory obligations (GDPR, HIPAA, industry-specific)
  • Exception Management Process: Streamlined procedures for evaluating new tools and use cases

2. Shadow AI Discovery & Management

  • Current State Assessment: Comprehensive audit of existing AI tool usage across the organization
  • Risk Profiling: Evaluation of discovered tools for security, privacy, and compliance risks
  • Tool Rationalization: Strategy for transitioning from shadow AI to sanctioned alternatives
  • Amnesty Programs: Safe disclosure mechanisms to surface hidden AI usage
  • Continuous Discovery: Monitoring systems to detect new unauthorized AI adoption
  • Vendor Management: Evaluation and approval processes for AI tool procurement

3. Role-Based AI Guidelines

  • Department-Specific Policies: Tailored guidelines for different functional areas
    • Sales & Marketing: Content generation, customer analytics, personalization boundaries
    • Human Resources: Resume screening, performance review assistance, bias prevention
    • Engineering: Code generation, security review requirements, IP protection
    • Finance: Data analysis, forecasting, audit trail maintenance
    • Legal: Research assistance, document review, confidentiality preservation
  • Seniority-Based Permissions: Graduated access based on role and responsibility
  • Use Case Libraries: Approved and prohibited scenarios with real examples
  • Decision Trees: Clear pathways for determining appropriate AI usage

4. Training & Awareness Programs

  • Executive Briefings: Strategic implications and governance responsibilities
  • Manager Training: Leading AI-enabled teams and managing policy compliance
  • Employee Education: Practical skills for secure and effective AI usage
  • Security Awareness: Understanding risks and protective measures
  • Ethics Workshops: Navigating bias, fairness, and responsible AI principles
  • Continuous Learning: Regular updates on new tools, techniques, and threats

5. Implementation & Change Management

  • Stakeholder Engagement: Cross-functional alignment and buy-in strategies
  • Communication Campaign: Multi-channel rollout of policies and procedures
  • Pilot Programs: Controlled testing of policies with early adopter groups
  • Feedback Mechanisms: Channels for employee input and policy refinement
  • Success Metrics: KPIs for measuring adoption, compliance, and productivity
  • Cultural Integration: Embedding AI governance into organizational DNA

6. Technical Controls & Enablement

  • Data Loss Prevention (DLP): Configuration to detect and prevent unauthorized AI usage
  • Browser Extensions Management: Controls for AI-powered browser tools
  • API Gateway Configuration: Monitoring and control of AI service access
  • Enterprise AI Platforms: Implementation of secure, centralized AI environments
  • Monitoring & Analytics: Dashboards for usage patterns and compliance tracking
  • Integration Architecture: Connection with existing security and IT management tools

7. Compliance & Risk Management

  • Regulatory Alignment: Ensuring policies meet current and emerging regulations
  • Risk Assessment Framework: Methodologies for evaluating AI usage risks
  • Audit Procedures: Regular reviews of policy effectiveness and compliance
  • Incident Response: Protocols for handling AI-related security incidents
  • Documentation Standards: Record-keeping for compliance demonstration
  • Third-Party Risk: Managing AI usage by contractors and partners

-- Training Legal Teams How to Best Leverage AI Tools

The legal profession stands at an inflection point, the question is no longer whether to adopt AI, but how to master it before competitors do. Our Legal AI Training & Enablement service transforms attorneys from AI-curious to AI-empowered, providing the practical skills, ethical frameworks, and strategic insights needed to leverage AI as a competitive advantage while maintaining the highest professional standards. We deliver comprehensive, practice-specific AI training that goes beyond generic tutorials to address the unique challenges and opportunities facing legal professionals. Our programs combine hands-on tool mastery, ethical compliance training, and strategic implementation guidance to ensure lawyers not only understand AI but can immediately apply it to improve client outcomes, reduce costs, and enhance service delivery—all while avoiding the pitfalls that have led to sanctions, malpractice claims, and reputational damage.

The stakes for AI adoption in law have never been higher:

  • Regulatory bodies increasingly use AI for enforcement and expect sophisticated responses
  • Courts are sanctioning lawyers for AI misuse, with fines reaching thousands of dollars
  • Clients expect AI-driven efficiency and are questioning traditional billing models
  • Competitors using AI effectively handle more matters with better outcomes at lower costs
  • Malpractice carriers are beginning to scrutinize firms' AI competence and controls

Our training ensures that legal professionals are equipped to navigate this new landscape with confidence and competence. Core components include: foundational AI literacy for legal professionals, including:

  • Understanding AI Architecture: How large language models work and their limitations
  • Legal-Specific AI Tools: Comprehensive overview of platforms (Harvey, Lexis+ AI, CoCounsel, etc.)
  • Prompt Engineering Mastery: Advanced techniques for extracting precise, reliable outputs
  • Hallucination Detection: Identifying and preventing AI-generated falsehoods
  • Security Fundamentals: Understanding data retention, encryption, and confidentiality
  • Ethical Frameworks: ABA Model Rule 1.1 compliance and jurisdiction-specific requirements

StarkAI also focuses on; advanced AI workflow integration; risk management and compliance (including avoiding sanctions and malpractice); data security and client confidentiality; building successful AI team -- and most of all, keeping up with the rapidly evolving AI landscape and ecosystem.