Move fast on AI. Stay safe doing it.
Compliance and risk frameworks that protect you.
Your team is already using AI — whether you've sanctioned it or not. ChatGPT prompts with client data. Copilot suggestions in production code. AI-generated content going out unsigned. The risk isn't AI itself; it's AI without guardrails. That's why Phoenix AI Solutions builds governance frameworks that enable safe AI adoption.
What We Deliver
Governance frameworks that let you move fast without getting caught out. Evaluating vendors? Phoenix Shield provides technical due diligence to complement policy frameworks.
Internal AI Usage Policy
Clear rules for how your team can and can't use AI tools, tailored to your industry and risk profile.
Vendor AI Governance
Frameworks for evaluating and managing AI vendors, including data handling, bias risk, and contractual protections. Pair with Phoenix Shield for technical due diligence and AI Strategy for implementation planning.
Regulatory Compliance Prep
Whether it's the EU AI Act, sector-specific regulations, or emerging standards, we help you prepare before enforcement hits.
AI Risk Assessment
Identify and prioritize the AI-related risks specific to your business, from data privacy to reputational exposure.
Who It's For
Leadership, legal, and compliance teams who need to govern AI usage without killing innovation. Combine policy with Phoenix Shield for technical vendor evaluation and AI Strategy for implementation planning.
Real AI Governance Implementations
AI governance policies deliver measurable risk reduction and compliance assurance. Here are three verified implementations across different industries.
The Challenge
Lawyers and consultants using ChatGPT for client work without oversight. No vendor approval process for AI tools. GDPR compliance team had no visibility into AI data flows. Leadership wanted to enable AI productivity but feared regulatory and reputational risk.
The Solution
Developed comprehensive AI governance framework: internal usage policy with clear data handling rules, vendor approval process with risk scoring, GDPR-compliant data flow documentation, and training program for 120 staff.
Measured Results
- Zero compliance incidents in 18 months post-policy implementation
- Approved 8 AI vendors using governance framework (rejected 4 due to data risks)
- 87% staff adoption of approved AI tools (measured via internal audit)
- Documented GDPR compliance for all AI data processing activities
- Avoided estimated £180K regulatory risk from unregulated AI use
The Challenge
Evaluating AI-powered investment analytics vendor. Marketing pitch strong, but contract had vague data handling terms and no bias monitoring commitments. Internal team lacked framework to assess AI vendor risk. Needed governance process before signing £320K/year contract.
The Solution
Built AI vendor governance framework with risk assessment criteria, contract negotiation playbook, and ongoing monitoring requirements. Applied framework to vendor evaluation and contract negotiation.
Measured Results
- Identified 12 unacceptable contract terms (data retention, liability, audit rights)
- Negotiated vendor contract revisions: quarterly bias audits, UK data residency, audit access rights
- Avoided signing contract with inadequate protections (would have created compliance exposure)
- Governance framework now applied to all AI vendor evaluations (3 additional vendors assessed)
- FCA audit found zero findings on AI vendor governance
The Challenge
Clinical and administrative staff using AI tools with patient data. No HIPAA-compliant AI usage guidelines. IT discovered ChatGPT Plus subscriptions across 14 departments. Risk of data breach, HIPAA violations, and regulatory fines. Needed urgent policy implementation.
The Solution
Rapid AI policy deployment: HIPAA-compliant usage guidelines, approved AI tools list, data de-identification requirements, emergency training program, and compliance monitoring system.
Measured Results
- HIPAA-compliant AI policy deployed in 3 weeks (emergency timeline)
- Migrated from unapproved tools to HIPAA-compliant alternatives (zero patient data exposure)
- Trained 450 staff on AI usage policy and data handling requirements
- Documented HIPAA compliance for all AI processing activities (passed audit)
- Avoided estimated $1.3M HIPAA violation penalties from unregulated AI use
- Enabled safe AI productivity: appointment scheduling AI, clinical documentation support
Phoenix AI Governance Framework
Our proprietary methodology for building AI policies that organizations actually follow. Unlike legal-only or IT-only approaches, this framework balances compliance, risk mitigation, and practical enablement of AI productivity.
Phase 1: Risk Assessment & Scope Definition (Week 1)
Map current AI usage (sanctioned and shadow IT), identify regulatory obligations, and prioritize governance areas by risk and urgency.
- AI usage audit: discover what tools are being used and how (often uncovers 3-5x more AI usage than expected)
- Regulatory mapping: identify applicable laws (GDPR, EU AI Act, sector regulations, contractual obligations)
- Risk inventory: data privacy, bias, security, compliance, vendor, reputational risks
- Stakeholder alignment: legal, IT, compliance, business units on governance priorities
- Scope definition: what policies to build first (internal usage, vendor governance, compliance)
Phase 2: Policy Development (Week 2-4)
Draft AI governance policies tailored to your industry, regulatory environment, and risk profile. Practical policies that enable AI productivity while managing risk.
- Internal AI usage policy: acceptable use cases, prohibited uses, data handling rules
- Vendor AI governance framework: vendor evaluation criteria, contract requirements, ongoing monitoring
- Compliance documentation: GDPR/HIPAA/sector-specific requirements for AI data processing
- Risk assessment templates: tools for evaluating new AI use cases and vendors
- Enforcement mechanisms: monitoring, incident response, accountability structures
Phase 3: Stakeholder Review & Refinement (Week 3-5)
Validate policies with legal, IT, compliance, and business stakeholders. Refine based on feedback to ensure policies are enforceable and practical.
- Legal review: ensure compliance with applicable regulations and contractual obligations
- IT/security review: verify technical feasibility of monitoring and enforcement
- Business unit review: ensure policies enable (not block) legitimate AI productivity
- Refinement: adjust policies based on stakeholder feedback and edge cases
- Executive approval: present final policies to leadership for sign-off
Phase 4: Training & Rollout (Week 5-6)
Deploy policies with comprehensive training so staff understand and follow governance requirements. Enforcement without education fails.
- Training program: role-specific training for staff, managers, IT, legal
- Communication plan: announce policies, explain rationale, provide resources
- Approved tools list: publish list of sanctioned AI vendors and how to request new tools
- Monitoring setup: implement tools to detect policy violations and shadow AI usage
- Incident response: define process for handling policy violations and AI-related incidents
Phase 5: Ongoing Monitoring & Updates (Quarterly)
AI governance is not set-and-forget. Regular reviews ensure policies stay current with new AI tools, regulatory changes, and organizational needs.
- Policy effectiveness review: are policies being followed? Where are gaps?
- Regulatory updates: monitor new AI regulations and update policies accordingly
- New AI tools evaluation: assess and approve/reject new AI vendor requests
- Incident review: analyze any policy violations or AI-related incidents
- Policy updates: revise policies based on lessons learned and changing requirements
DIY AI Policy vs. Legal-Only Approach vs. Phoenix AI Governance
Three options for building AI governance. Here's how they compare on risk mitigation, implementation speed, and ongoing effectiveness.
DIY with Internal Resources
Timeline
4-8 months (if completed)
Cost
Looks free, but hidden costs in staff time, missed risks, and implementation failures
Coverage
Often incomplete: focuses on acceptable use, misses vendor risk, compliance gaps, enforcement
Expertise
Low AI-specific expertise. Policies are generic adaptations of IT policies, not AI-tailored.
Enforcement
Weak. Policy exists but no monitoring, training, or accountability mechanisms.
Best For
Large enterprises with dedicated governance teams, long timelines, and high tolerance for iteration.
Real Risk
Policy sits on shelf. Staff don't follow it. Compliance gaps discovered during audit or incident. Most DIY policies fail on enforcement.
Legal-Led AI Policy
Timeline
3-6 months (legal review cycles)
Cost
£25K-£85K in legal fees for policy drafting and review
Coverage
Strong on compliance and liability, weak on technical feasibility and vendor evaluation.
Expertise
High legal expertise, low AI/technical expertise. Policies are legally sound but operationally impractical.
Enforcement
Medium. Legal teams define policy but lack tools to monitor AI usage or assess vendor risk.
Best For
Highly regulated industries where legal precision is paramount and budget is not constrained.
Real Risk
Policies are too restrictive (kill productivity) or too vague (not enforceable). Technical teams can't implement. Vendor risk assessment missing.
Phoenix AI Governance
Timeline
2-6 weeks from kickoff to policy deployment
Cost
£8K-£45K depending on organizational size and scope
Coverage
Comprehensive: internal usage, vendor governance, compliance prep, risk assessment, training, enforcement.
Expertise
Combined AI, legal, and technical expertise. Policies are compliant, practical, and enforceable.
Enforcement
High. Includes training programs, monitoring frameworks, vendor evaluation tools, and ongoing reviews.
Best For
Organizations that need AI governance fast, with practical policies that enable (not block) AI adoption.
Real Risk
Low. Policies are tested frameworks tailored to your industry. Implementation support ensures adoption. Quarterly reviews keep policy current.
Frequently Asked Questions
What is an AI governance policy and why do I need one?
An AI governance policy defines how your organization uses AI tools, evaluates AI vendors, manages data in AI systems, and ensures regulatory compliance. You need one because: (1) Your team is already using AI (ChatGPT, Copilot, AI writing tools) whether sanctioned or not. (2) Unregulated AI use creates legal, security, and reputational risks. (3) The EU AI Act, sector regulations, and emerging standards require documented governance. (4) Without policy, you can't audit vendor contracts or assess AI risk. A good policy enables safe AI adoption, not restriction.
How long does it take to develop an AI policy?
Timeline varies by organizational complexity. Small businesses (under 50 employees) can develop foundational AI policies in 2-3 weeks. Mid-market companies (50-500 employees) typically require 4-6 weeks for comprehensive governance frameworks covering internal usage, vendor management, and compliance. Enterprise organizations (500+ employees, multiple business units) may need 8-12 weeks for full policy suites including risk assessment, training programs, and enforcement mechanisms. Phoenix accelerates this with templates tailored to your industry and regulatory environment.
What does AI policy consulting cost?
AI policy consulting pricing depends on scope and organizational size. Foundational policies for small businesses: $8,000-$18,000 (internal usage guidelines, basic vendor evaluation framework). Comprehensive governance for mid-market: $18,000-$45,000 (internal policy, vendor governance, compliance prep, risk assessment). Enterprise policy suites: $45,000-$130,000+ (multi-business unit frameworks, regulatory compliance, training programs, ongoing governance support). All engagements include policy documentation, stakeholder workshops, implementation guidance, and quarterly review sessions. Contact us for a custom quote.
What should an AI usage policy include?
Effective AI usage policies cover five core areas: (1) Acceptable use cases — what AI tools can be used for, and what's prohibited (e.g., no client data in public AI tools). (2) Data handling rules — how to handle sensitive, confidential, and personal data in AI systems. (3) Vendor approval process — how to evaluate, approve, and monitor AI vendors. (4) Risk and compliance requirements — regulatory obligations, bias monitoring, security standards. (5) Accountability and enforcement — who's responsible for compliance, how violations are handled. Phoenix policies are practical and enforceable, not shelf-ware.
How is AI policy different from general IT or data policies?
AI policies address unique risks that traditional IT/data policies don't cover: (1) Model behavior and bias — AI systems can produce discriminatory or inaccurate outputs even with clean data. (2) Explainability and transparency — AI decisions must be auditable, especially in regulated industries. (3) Vendor model risk — you don't control vendor AI models or training data, creating third-party risk. (4) Emerging regulations — the EU AI Act, industry-specific AI rules, and evolving compliance requirements. (5) Continuous learning risk — AI systems change behavior over time as they learn. AI policy complements existing IT governance with AI-specific controls.
Can you help with EU AI Act compliance?
Yes. Phoenix helps organizations prepare for EU AI Act compliance by: (1) Classifying your AI systems by risk tier (unacceptable, high, limited, minimal). (2) Documenting high-risk AI systems with required technical documentation and risk assessments. (3) Building conformity assessment processes for high-risk AI. (4) Preparing for transparency obligations (e.g., disclosing AI-generated content). (5) Establishing governance structures for ongoing compliance monitoring. We stay current with evolving guidance and implementation timelines so you're ready before enforcement.
Related Solutions
These solutions work well together or complement this offering
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