Intermediate14 min read

30-Day Action Plan

Step-by-step plan to get compliant in 30 days with templates, tools, and cost estimates.

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AI Compliance: 30-Day Action Plan (Step-by-Step)

Last Updated: January 23, 2026
Next Review: April 23, 2026

"Where Do I Even Start?"

A founder called us last week, overwhelmed.

His Series A startup had just learned they needed to comply with NYC Local Law 144. Their first enterprise customer required SOC 2. And they were processing EU customer data without a GDPR Data Processing Impact Assessment.

His question: "We need to fix all of this. Where do we even start?"

Our answer: "Week 1, Day 1. Here's exactly what to do."

30 days later, they had:

✅ AI inventory complete
✅ Bias audit commissioned
✅ GDPR DPIA in progress
✅ SOC 2 scoping done
✅ Legal policies updated
✅ Team trained on compliance

Total cost: $45,000 (vs. $125,000+ for violations)

Here's the exact plan we gave them.

The 30-Day Framework

This plan assumes you're a startup or SMB with:

AI system(s) in production or near-production
Limited compliance resources
Need to get compliant quickly
Budget of $20,000-$50,000

If that's you, follow this plan exactly.

Week 1: Foundation (Discovery & Assessment)

Day 1: AI Inventory

Goal: Document every AI system you use or build.

What to document:

[ ] System name - What you call it internally
[ ] Purpose - What it does (resume screening, fraud detection, etc.)
[ ] Data processed - What data it uses (personal data, biometric, health, etc.)
[ ] Decisions made - What decisions it makes or influences
[ ] Users affected - Who it impacts (employees, customers, applicants)
[ ] Geographic scope - Where users are located
[ ] Vendor or in-house - Who built it
[ ] Deployment status - Production, staging, development

Template:

System: Resume Screening AI
Purpose: Ranks job candidates based on resume analysis
Data: Names, work history, education, skills (personal data)
Decisions: Candidate ranking for interviews (consequential)
Users: Job applicants in NYC, Colorado, California
Geography: US (NYC, CO, CA)
Vendor: Built in-house using OpenAI API
Status: Production since March 2024

Don't forget:

Vendor AI (Salesforce Einstein, HubSpot AI, etc.)
Embedded AI in tools you use
AI in development (not yet deployed)

Time: 2-4 hours
Cost: $0 (internal)
Tool: AI Inventory Template

Day 2: Determine Which Laws Apply

Goal: Know exactly which regulations you must comply with.

Use our tool: Law Finder - 2 minutes

Or manually check:

Step 1: Geographic scope

[ ] NYC users? → NYC Local Law 144
[ ] Colorado users? → Colorado AI Act (effective Feb 1, 2026)
[ ] Illinois users? → Illinois BIPA (if biometric)
[ ] California users? → CCPA/CPRA
[ ] EU users? → GDPR + EU AI Act
[ ] Federal contractor? → AI Executive Order 14110

Step 2: AI use case

[ ] Hiring? → NYC LL144, Colorado AI Act, EEOC
[ ] Credit? → Colorado AI Act, FCRA, ECOA
[ ] Healthcare? → FDA, HIPAA
[ ] Biometric? → Illinois BIPA, EU AI Act

Step 3: Data type

[ ] Personal data? → GDPR, CCPA
[ ] Health data? → HIPAA
[ ] Biometric? → BIPA

Step 4: Customer requirements

[ ] Healthcare customers? → HIPAA + BAA
[ ] Finance customers? → FINRA compliance
[ ] Enterprise customers? → SOC 2 Type II
[ ] EU customers? → GDPR DPA

Document your findings:

Laws that apply to us:
1. NYC Local Law 144 (we have NYC users, hiring AI)
2. GDPR (we have EU users, process personal data)
3. SOC 2 (enterprise customers require it)

Action required:
- NYC LL144: Bias audit, candidate notice, publish results
- GDPR: DPIA, consent mechanisms, DPA with customers
- SOC 2: Type I audit (6-9 months to complete)

Time: 1-2 hours
Cost: $0 (internal)
Tool: Law Finder

Day 3: Gap Analysis

Goal: Identify what you're missing.

Use our tool: Self-Audit - 15 minutes

Or manually assess:

For each applicable law, check:

NYC Local Law 144:

[ ] Have you conducted a bias audit in the last 12 months?
[ ] Do you notify candidates 10+ days before AI screening?
[ ] Are audit results published on your careers page?

Colorado AI Act (if applicable):

[ ] Have you completed an impact assessment?
[ ] Do you have a risk management policy?
[ ] Do you provide consumer disclosures?

GDPR (if applicable):

[ ] Have you conducted a DPIA for AI processing?
[ ] Do you have consent mechanisms?
[ ] Do you have DPAs with customers?
[ ] Can users exercise their rights (access, deletion, objection)?

SOC 2 (if needed):

[ ] Do you have documented security policies?
[ ] Do you have access controls?
[ ] Do you have logging and monitoring?
[ ] Do you have incident response plan?

Document your gaps:

Critical gaps (must fix immediately):
1. No bias audit (NYC LL144) - 18 months overdue
2. No GDPR DPIA - processing EU data without assessment
3. No candidate notice system - violating NYC LL144

Important gaps (fix within 90 days):
1. No SOC 2 - blocking enterprise deals
2. Incomplete logging - can't demonstrate compliance
3. No incident response plan - required for SOC 2

Nice-to-have (fix within 6 months):
1. Bias monitoring dashboard
2. Automated compliance reporting
3. AI governance committee

Time: 2-3 hours
Cost: $0 (internal)
Tool: Self-Audit

Day 4: Calculate Risk Exposure

Goal: Understand the financial risk of non-compliance.

Use our tool: Penalty Calculator - 5 minutes

Or manually calculate:

NYC Local Law 144:

Penalty: $500-$1,500 per violation per day
Your exposure: 18 months without audit = 540 days
Calculation: 540 days × $500 = $270,000 (low end)
Calculation: 540 days × $1,500 = $810,000 (high end)

GDPR:

Penalty: Up to €20M or 4% of global revenue
Your revenue: $5M
Calculation: $5M × 4% = $200,000 (max for your size)

Lost deals (SOC 2):

Enterprise deals blocked: 3 deals
Average deal size: $200,000
Lost revenue: $600,000

Total exposure: $1.07M - $1.61M

Document your risk:

Financial risk summary:
- NYC LL144 violations: $270K-$810K
- GDPR violations: Up to $200K
- Lost enterprise deals: $600K
- Total exposure: $1.07M-$1.61M

Cost to remediate: $45,000
ROI: 24x-36x (avoid $1M+ in penalties and lost revenue)

Time: 1 hour
Cost: $0 (internal)
Tool: Penalty Calculator

Day 5: Build Business Case & Get Budget

Goal: Get executive/board approval for compliance budget.

Create 1-page business case:

AI Compliance Business Case

Problem:
- 18 months without required bias audit (NYC LL144)
- Processing EU data without GDPR compliance
- Blocking $600K in enterprise deals (no SOC 2)

Risk:
- Penalty exposure: $1.07M-$1.61M
- Reputational damage if violations become public
- Investor due diligence red flags

Solution:
- 30-day compliance sprint
- Commission bias audit, conduct DPIA, start SOC 2
- Update policies, implement controls, train team

Investment:
- Immediate (30 days): $25,000
- Short-term (90 days): $20,000
- Total: $45,000

ROI:
- Avoid $1M+ in penalties
- Unlock $600K in blocked deals
- Pass investor due diligence
- 24x-36x return on investment

Timeline:
- Week 1: Assessment (complete)
- Week 2: Legal & audit kickoff
- Week 3: Implementation
- Week 4: Validation & training

Approval needed: $45,000 budget

Present to:

CEO/Founders
CFO (budget approval)
Board (if required for this amount)

Time: 2-3 hours
Cost: $0 (internal)

Week 2: Legal & Audit Kickoff

Day 6-7: Hire Compliance Counsel

Goal: Get legal expertise for policy review and audit coordination.

What you need:

Employment lawyer (if NYC LL144 applies)
Privacy lawyer (if GDPR/CCPA applies)
General compliance counsel

Options:

Option 1: Law firm ($300-$600/hour)

Pros: Deep expertise, full-service
Cons: Expensive, slow
Best for: Complex situations, high-risk

Option 2: Fractional GC ($5,000-$10,000/month)

Pros: Dedicated resource, responsive
Cons: Monthly commitment
Best for: Ongoing compliance needs

Option 3: Project-based consultant ($10,000-$20,000 project)

Pros: Fixed cost, focused scope
Cons: Limited ongoing support
Best for: One-time compliance sprint

What they'll do:

Review your gap analysis
Draft/update policies (privacy policy, terms of service, AI use policy)
Coordinate audits (bias audit, DPIA)
Review vendor contracts
Provide legal opinions on ambiguous requirements

Action items:

[ ] Get 3 quotes from lawyers/consultants
[ ] Check references
[ ] Sign engagement letter
[ ] Schedule kickoff call

Time: 1-2 days
Cost: $10,000-$20,000 (project-based)

Day 8-9: Commission Required Audits

Goal: Start audits that take 4-8 weeks to complete.

If NYC LL144 applies:

Commission bias audit:

[ ] Find independent auditor (not your employee or AI vendor)
[ ] Check auditor qualifications (statistical expertise, employment law knowledge)
[ ] Provide 12 months of hiring data (or test data if new system)
[ ] Sign audit agreement
[ ] Pay 50% upfront (typical)

Auditor options:

Employment law firms with statistical teams ($25K-$75K)
Specialized bias audit firms ($15K-$50K)
Academic researchers ($10K-$30K)
I-O psychology firms ($20K-$60K)

Timeline: 4-8 weeks from kickoff to report

Cost: $15,000-$50,000

Tool: NYC LL144 Compliance Checker

If GDPR applies:

Conduct DPIA (Data Protection Impact Assessment):

[ ] Describe AI processing activities
[ ] Assess necessity and proportionality
[ ] Identify risks to data subjects
[ ] Document mitigation measures
[ ] Get legal review

Options:

DIY with template ($0, but risky)
Privacy consultant ($10,000-$30,000)
Law firm ($15,000-$40,000)

Timeline: 2-4 weeks

Cost: $10,000-$30,000 (consultant) or $0 (DIY)

Tool: GDPR AI Checklist

If SOC 2 needed:

Start SOC 2 Type I process:

[ ] Select audit firm (Big 4 or specialized)
[ ] Scope audit (which systems, which Trust Service Criteria)
[ ] Gap assessment
[ ] Implement controls (3-6 months)
[ ] Audit observation period (3 months minimum)
[ ] Audit and report (1-2 months)

Timeline: 6-12 months total

Cost: $30,000-$75,000 (audit fees only, not including implementation)

Note: SOC 2 won't complete in 30 days, but you need to start now.

Day 10: Update Legal Policies

Goal: Update privacy policy, terms of service, and AI-specific policies.

Work with your lawyer to update:

Privacy Policy:

[ ] Add section on AI data processing
[ ] Explain what AI does with user data
[ ] Describe automated decision-making (if applicable)
[ ] Explain user rights (GDPR Article 22, CCPA)
[ ] Add contact for AI-related questions

Terms of Service:

[ ] Add AI disclaimers (accuracy, limitations)
[ ] Explain AI use in your service
[ ] Disclaim liability for AI errors (where legally allowed)
[ ] Add dispute resolution for AI decisions

AI Use Policy (new document):

[ ] Purpose of AI systems
[ ] Types of decisions AI makes
[ ] Human oversight procedures
[ ] How to contest AI decisions
[ ] Contact information

Candidate Notice (if NYC LL144):

[ ] Explain AI use in hiring
[ ] Provide link to bias audit results
[ ] Offer alternative selection process
[ ] Give 10+ days notice before screening

Time: 1-2 days (with lawyer)
Cost: Included in legal fees ($10K-$20K project)

Week 3: Implementation

Day 11-12: Implement Logging & Monitoring

Goal: Log all AI decisions for audit trails.

What to log:

[ ] User ID (or anonymized identifier)
[ ] Model ID and version
[ ] Input data (sanitized if contains PII)
[ ] Output/decision
[ ] Confidence score
[ ] Timestamp
[ ] Human override (if applicable)

Implementation:

// Structured logging for AI decisions
import winston from 'winston'

const logger = winston.createLogger({
  level: 'info',
  format: winston.format.json(),
  defaultMeta: { service: 'ai-service' },
  transports: [
    new winston.transports.File({ 
      filename: 'ai-decisions.log',
      maxsize: 10485760, // 10MB
      maxFiles: 10,
    }),
  ],
})

export function logAIDecision(params: {
  userId: string
  modelId: string
  modelVersion: string
  input: string
  output: string
  confidence: number
  humanOverride?: boolean
}) {
  logger.info('AI decision', {
    user_id: params.userId,
    model_id: params.modelId,
    model_version: params.modelVersion,
    input_length: params.input.length, // Don't log actual input if PII
    output: params.output,
    confidence: params.confidence,
    human_override: params.humanOverride || false,
    timestamp: new Date().toISOString(),
  })
}

Why: SOC 2 CC7.2, GDPR Article 5(2), EU AI Act Article 12 all require audit trails.

Time: 1-2 days (engineering)
Cost: $0 (internal engineering time)

Day 13-14: Implement Bias Monitoring

Goal: Track AI fairness metrics continuously.

What to monitor:

[ ] Selection rates by protected group (race, gender)
[ ] Impact ratios (EEOC four-fifths rule: ≥ 0.80)
[ ] Flagged groups (impact ratio < 0.80)
[ ] Trend over time

Implementation:

# Calculate impact ratios per EEOC guidelines
def calculate_impact_ratios(decisions: list[dict]) -> dict:
    from collections import defaultdict
    
    # Count selections by group
    group_counts = defaultdict(lambda: {'total': 0, 'selected': 0})
    
    for decision in decisions:
        group = decision['group']
        group_counts[group]['total'] += 1
        if decision['selected']:
            group_counts[group]['selected'] += 1
    
    # Calculate selection rates
    selection_rates = {}
    for group, counts in group_counts.items():
        if counts['total'] > 0:
            selection_rates[group] = counts['selected'] / counts['total']
    
    # Find highest selection rate
    max_rate = max(selection_rates.values()) if selection_rates else 0
    
    # Calculate impact ratios
    impact_ratios = {}
    for group, rate in selection_rates.items():
        if max_rate > 0:
            impact_ratios[group] = rate / max_rate
    
    return {
        'selection_rates': selection_rates,
        'impact_ratios': impact_ratios,
        'threshold': 0.80,
        'flagged_groups': [
            group for group, ratio in impact_ratios.items()
            if ratio is not None and ratio < 0.80
        ]
    }

# Run monthly and alert if issues
results = calculate_impact_ratios(last_month_decisions)
if results['flagged_groups']:
    alert_compliance_team(results)

Why: NYC LL144, Colorado AI Act, EU AI Act require bias monitoring.

Time: 2-3 days (engineering)
Cost: $0 (internal engineering time)

Day 15-16: Implement Human Oversight

Goal: Enable human review of AI decisions.

What to implement:

[ ] Review queue for low-confidence decisions
[ ] Human override capability
[ ] Override logging (who, when, why)
[ ] Escalation procedures

Implementation:

// Human-in-the-loop for high-stakes decisions
export async function processAIDecision(decision: AIDecision) {
  // 1. Check if human review required
  if (decision.requiresReview || decision.confidence < 0.85) {
    await db.reviewQueue.create({
      data: {
        decisionId: decision.id,
        status: 'PENDING_REVIEW',
        queuedAt: new Date(),
      }
    })
    
    await notifyReviewer({
      decisionId: decision.id,
      priority: decision.confidence < 0.70 ? 'HIGH' : 'NORMAL'
    })
    
    return { status: 'PENDING_REVIEW' }
  }
  
  // 2. If no review needed, proceed
  return { status: 'APPROVED', decision: decision.modelOutput }
}

// Allow human override
export async function overrideAIDecision(
  decisionId: string,
  reviewerId: string,
  override: any,
  reason: string
) {
  // Log override for audit trail
  await db.aiOverride.create({
    data: {
      decisionId,
      reviewerId,
      originalDecision: await getOriginalDecision(decisionId),
      overrideDecision: override,
      reason,
      timestamp: new Date(),
    }
  })
  
  await db.decision.update({
    where: { id: decisionId },
    data: {
      finalDecision: override,
      reviewedBy: reviewerId,
      reviewedAt: new Date(),
    }
  })
}

Why: GDPR Article 22, EU AI Act Article 14, Colorado AI Act require human oversight.

Time: 2-3 days (engineering)
Cost: $0 (internal engineering time)

Day 17: Implement Candidate Notice System

Goal: Notify candidates 10+ days before AI screening (NYC LL144).

If NYC LL144 applies:

Implementation:

// Automated candidate notice system
export async function sendCandidateNotice(config: {
  jobId: string
  candidateEmail: string
  aedtDescription: string
  biasAuditUrl: string
}) {
  // 1. Check if notice already sent
  const existing = await db.candidateNotice.findFirst({
    where: { jobId: config.jobId, candidateEmail: config.candidateEmail }
  })
  
  if (existing) return existing
  
  // 2. Send email
  await sendEmail({
    to: config.candidateEmail,
    subject: 'Notice: AI-Assisted Hiring Process',
    body: `
      Dear Candidate,

      We use automated employment decision tools (AEDT) to assist in 
      evaluating candidates. Specifically: ${config.aedtDescription}

      You have the right to:
      - Request an alternative selection process
      - Request reasonable accommodation
      - Review our bias audit results: ${config.biasAuditUrl}

      Your application will be reviewed at least 10 days from today.

      Best regards,
      [Company] Hiring Team
    `
  })
  
  // 3. Record notice
  const notice = await db.candidateNotice.create({
    data: {
      jobId: config.jobId,
      candidateEmail: config.candidateEmail,
      sentAt: new Date(),
      scheduledScreeningDate: new Date(Date.now() + 10 * 24 * 60 * 60 * 1000),
      noticeMethod: 'email',
    }
  })
  
  return notice
}

// Check if can screen (10+ days since notice)
export async function canScreenCandidate(
  candidateEmail: string,
  jobId: string
): Promise<boolean> {
  const notice = await db.candidateNotice.findFirst({
    where: { candidateEmail, jobId }
  })
  
  if (!notice) {
    throw new Error('No candidate notice found')
  }
  
  const daysSinceNotice = (Date.now() - notice.sentAt.getTime()) / (1000 * 60 * 60 * 24)
  
  return daysSinceNotice >= 10
}

Time: 1 day (engineering)
Cost: $0 (internal engineering time)

Week 4: Validation & Training

Day 18-19: Internal Audit

Goal: Verify everything is working before external validation.

Test checklist:

[ ] Logging: Generate test AI decision, verify log entry
[ ] Bias monitoring: Run calculation on test data, verify output
[ ] Human oversight: Submit low-confidence decision, verify review queue
[ ] Candidate notice: Submit test application, verify 10-day notice sent
[ ] Policies: Review updated privacy policy, terms, AI use policy

Document findings:

Internal audit results:
✅ Logging working (verified 100 test decisions logged)
✅ Bias monitoring working (calculated impact ratios correctly)
✅ Human oversight working (low-confidence decisions queued)
✅ Candidate notice working (10-day notice sent automatically)
✅ Policies updated and published

Issues found:
- Bias monitoring dashboard showing incorrect dates (fixed)
- Candidate notice email template had typo (fixed)

Time: 1-2 days
Cost: $0 (internal)

Day 20-21: External Validation

Goal: Get expert review of your compliance program.

Options:

Option 1: Compliance consultant review ($5,000-$10,000)

Review documentation
Test controls
Provide gap report
Recommend improvements

Option 2: Legal review (included in legal fees)

Review policies
Verify legal compliance
Provide legal opinion

Option 3: Peer review (free)

Another startup founder
Industry compliance group
Online community (with caution)

What they'll check:

Documentation completeness
Control effectiveness
Policy accuracy
Audit readiness

Time: 2-3 days
Cost: $5,000-$10,000 (consultant) or $0 (peer review)

Day 22-23: Publish Required Disclosures

Goal: Make required information publicly available.

If NYC LL144 applies:

Publish bias audit results (once audit complete):

[ ] Create /careers/bias-audit page
[ ] Include: audit date, auditor name, AEDT vendor, impact ratios
[ ] Make publicly accessible (no login)
[ ] Keep published for 6 months after audit date

Example:

Bias Audit Summary - 2026

Audit Date: January 15, 2026
Auditor: Smith Consulting LLC
AEDT Vendor: Built in-house using OpenAI API

Impact Ratios (Selection Rates):
- Male: 1.00 (reference group)
- Female: 0.85 (no disparate impact)
- White: 1.00 (reference group)
- Black/African American: 0.82 (no disparate impact)
- Hispanic/Latino: 0.88 (no disparate impact)
- Asian: 0.95 (no disparate impact)

All impact ratios exceed 0.80 threshold per EEOC guidelines.

If GDPR applies:

Update privacy policy:

[ ] Publish updated privacy policy
[ ] Add cookie banner (if needed)
[ ] Implement consent mechanisms
[ ] Add data subject rights form

Time: 1 day
Cost: $0 (internal)

Day 24-25: Train Your Team

Goal: Ensure everyone understands compliance requirements.

Who to train:

Engineering (logging, monitoring, controls)
Product (AI design, transparency, human oversight)
Sales (customer compliance questions)
Marketing (accurate AI claims, FTC compliance)
HR (if using AI in hiring)
Customer support (handling AI-related inquiries)

Training topics:

What AI systems we use
Which laws apply to us
What we're required to do
What controls we've implemented
How to handle compliance questions
When to escalate to legal/compliance

Format:

1-hour all-hands presentation
Department-specific deep dives (30 min each)
Written documentation (compliance wiki)
Quarterly refreshers

Time: 2 days (prep + delivery)
Cost: $0 (internal)

Day 26-27: Set Up Ongoing Monitoring

Goal: Ensure compliance doesn't slip after initial sprint.

Set up:

Quarterly compliance reviews:

[ ] Review AI inventory (any new systems?)
[ ] Check bias monitoring (any flagged groups?)
[ ] Review incident log (any AI failures?)
[ ] Update policies (any law changes?)
[ ] Refresh team training

Automated monitoring:

[ ] Bias monitoring dashboard (check monthly)
[ ] Logging alerts (if logging stops)
[ ] Audit reminders (bias audit due in 12 months)
[ ] Policy review reminders (quarterly)

Compliance calendar:

Monthly:
- Review bias monitoring dashboard
- Check logging and monitoring systems

Quarterly:
- Compliance team meeting
- Review AI inventory
- Update policies if needed
- Team training refresher

Annually:
- Commission bias audit (NYC LL144)
- SOC 2 audit (if applicable)
- Legal policy review
- Compliance program assessment

Time: 1-2 days (setup)
Cost: $0 (internal)

Day 28-29: Document Everything

Goal: Create compliance documentation for auditors, investors, customers.

Create:

Compliance Program Documentation:

[ ] AI inventory (all systems)
[ ] Applicable laws and requirements
[ ] Controls implemented
[ ] Policies and procedures
[ ] Training records
[ ] Audit reports (bias audit, DPIA, etc.)
[ ] Incident log (AI failures, overrides)

Vendor Compliance Package (for customer requests):

[ ] AI governance overview
[ ] Security controls (SOC 2 report when available)
[ ] Bias audit results (if applicable)
[ ] Privacy policies
[ ] Data Processing Agreement template

Investor Due Diligence Package:

[ ] Compliance program summary
[ ] Applicable laws and compliance status
[ ] Audit reports
[ ] Legal opinions (if any)
[ ] Risk assessment

Time: 2 days
Cost: $0 (internal)

Day 30: Launch & Communicate

Goal: Announce compliance program internally and externally.

Internal announcement:

Subject: AI Compliance Program Launch

Team,

I'm excited to announce the launch of our AI Compliance Program.

What we've accomplished:
✅ AI inventory complete (3 systems documented)
✅ Bias audit commissioned (results in 6 weeks)
✅ GDPR DPIA complete
✅ SOC 2 Type I in progress
✅ Policies updated and published
✅ Logging and monitoring implemented
✅ Team trained on compliance

Why this matters:
- Protects us from $1M+ in potential penalties
- Unlocks $600K in blocked enterprise deals
- Passes investor due diligence
- Demonstrates our commitment to responsible AI

What's next:
- Quarterly compliance reviews
- Ongoing monitoring
- Continuous improvement

Thank you to everyone who contributed to this effort.

[Founder/CEO]

External communication (if appropriate):

Blog post: "Our Commitment to AI Compliance"
Customer email: "AI Compliance Update"
Investor update: "Compliance Milestone Achieved"

Update sales materials:

Add compliance section to pitch deck
Update security questionnaire responses
Add compliance badges to website (when available)

Time: 1 day
Cost: $0 (internal)

Budget Summary

| Item | Cost | Timeline | |------|------|----------| | Legal counsel (project-based) | $10,000-$20,000 | Days 6-30 | | Bias audit (if NYC LL144) | $15,000-$50,000 | Days 8-9 (4-8 weeks to complete) | | GDPR DPIA (consultant) | $10,000-$30,000 | Days 8-9 (2-4 weeks to complete) | | SOC 2 scoping (audit firm) | $5,000-$10,000 | Days 8-9 (scoping only) | | Compliance consultant (validation) | $5,000-$10,000 | Days 20-21 | | Engineering time (internal) | $0 | Days 11-17 | | Total | $45,000-$120,000 | 30 days |

Note: SOC 2 full audit ($30K-$75K) not included in 30-day budget (takes 6-12 months).

Success Metrics

After 30 days, you should have:

✅ AI inventory complete
✅ Applicable laws identified
✅ Critical gaps addressed
✅ Required audits commissioned
✅ Policies updated and published
✅ Controls implemented (logging, monitoring, human oversight)
✅ Team trained
✅ Ongoing monitoring established
✅ Documentation complete

Risk reduction:

Penalty exposure: $1M+ → $0 (compliant)
Lost deals: $600K → $0 (can answer compliance questions)
Investor concerns: High → Low (documented program)

ROI: 24x-36x (avoid $1M+ in penalties and lost revenue for $45K investment)

Common Obstacles & Solutions

Obstacle 1: "We don't have $45,000"

Solutions:

Option 1: Prioritize critical gaps ($10,000-$20,000)

Legal review only (skip consultant)
DIY DPIA (skip consultant, use template)
Commission only required audits (bias audit if NYC LL144)
Defer SOC 2 until you have enterprise deals

Option 2: Phased approach (spread over 90 days)

Month 1: Legal + bias audit ($25K)
Month 2: DPIA + controls ($10K)
Month 3: SOC 2 scoping + validation ($10K)

Option 3: Raise compliance as part of fundraise

Include compliance costs in fundraise budget
Show investors you're addressing risk proactively

Obstacle 2: "We don't have engineering resources"

Solutions:

Option 1: Hire contractor ($5,000-$10,000)

1-2 week engagement
Implement logging, monitoring, controls
Train your team on maintenance

Option 2: Use compliance platforms

Vanta, Drata, Secureframe (SOC 2 automation)
HAIEC (AI-specific compliance)
$2,000-$5,000/month

Option 3: Simplify scope

Implement basic logging only (1 day)
Defer advanced monitoring (bias dashboard)
Add more controls later

Obstacle 3: "Auditors are booked for months"

Solutions:

For bias audits:

Get on waitlist immediately
Check multiple auditors
Consider academic researchers (faster availability)
Document that you've commissioned audit (shows good faith)

For SOC 2:

Start with gap assessment (can do yourself)
Implement controls while waiting for auditor
Use compliance platform to automate evidence collection

Obstacle 4: "Laws keep changing"

Solutions:

Build adaptable foundation:

Core controls (logging, monitoring) apply to all laws
Documentation practices scale to new requirements
Quarterly reviews catch new regulations

Stay informed:

Subscribe to regulatory updates
Join compliance communities
Follow HAIEC blog (we track changes)

Next Steps

If you're ready to start:

Download 30-Day Checklist - Printable version
Run Self-Audit - Identify your gaps (15 min)
Run Law Finder - Which laws apply (2 min)
Book Consultation - Get expert help (30 min, free)

If you need help:

Schedule Demo - See HAIEC platform
Talk to Sales - Enterprise compliance program
Read: Common Pitfalls - Avoid mistakes

If you want to learn more:

Read: What is AI Compliance - Comprehensive guide
Read: Why Compliance Matters - Business case
Read: Which Laws Apply - Determine requirements

Frequently Asked Questions

Can we really get compliant in 30 days?

Yes, for initial compliance. But some things take longer:

Bias audit: 4-8 weeks (but you can commission in 30 days)
SOC 2: 6-12 months (but you can start in 30 days)
DPIA: 2-4 weeks (can complete in 30 days if prioritized)

30 days gets you:

Critical gaps addressed
Audits commissioned
Controls implemented
Policies updated
Team trained

Then you maintain and improve over time.

What if we're already non-compliant?

Start immediately. Here's why:

Penalties accrue daily (NYC LL144 is $500-$1,500 per day)
Good faith matters - Showing you're fixing it reduces penalties
Self-disclosure may help - Some jurisdictions offer cure periods

Don't wait. Every day of delay increases your exposure.

Do we need to hire a compliance person?

Not for 30-day sprint. You can do this with:

Founder/executive sponsor
Legal counsel (external)
Engineering lead
Compliance consultant (optional)

Consider hiring if:

Multiple regulations apply
Selling to regulated industries
Rapid growth (need to scale compliance)
Post-Series A ($1M-$10M ARR)

What if we pivot after getting compliant?

Core compliance infrastructure applies regardless:

Logging and monitoring
Privacy policies
Security controls
Documentation practices

What might change:

Specific audits (e.g., bias audit if you pivot away from hiring)
Industry-specific requirements

Cost to pivot: Minimal if foundation is solid.

Disclaimer

This is educational content, not legal advice. AI compliance requirements vary by jurisdiction, industry, and specific use case. Consult qualified legal counsel for advice specific to your situation.

HAIEC provides compliance tools and educational resources but is not a law firm and does not provide legal advice.

Last Updated: January 23, 2026
Next Review: April 23, 2026
Regulatory Sources:

NYC Local Law 144 (2021)
Colorado SB24-205 (2024)
EU AI Act (Regulation 2024/1689)
GDPR (Regulation 2016/679)

Questions? Contact us or book a free consultation.