What is an AI audit? (complete guide for enterprise buyers)

Before deploying AI in production, you need an audit. Here’s what that actually means-and why it matters for Series B+ companies.

The simple definition

An AI audit is a systematic review of your AI systems to ensure they’re:

• Technically sound

• Legally compliant

• Ethically responsible

• Business-aligned

Think of it like a security audit, but for AI.

Who needs an AI audit

Enterprise buyers - Required by procurement teams

Regulated industries - Healthcare, finance, legal mandates

Companies with sensitive data - Privacy and security requirements

B2B SaaS selling to Fortune500 - Table stakes for enterprise deals

If you’re Series B+ and selling to enterprise, you’ll need this eventually.

What gets audited

1. Model audit

• Training data quality and sources

• Model architecture and performance

• Bias detection and mitigation

• Accuracy and reliability metrics

2. Data audit

• Data governance and lineage

• Privacy and security controls

• Compliance with regulations (GDPR, CCPA)

• Data quality and validation

3. Security audit

• Access controls and authentication

• Encryption (at rest and in transit)

• Vulnerability assessment

• Incident response procedures

4. Ethics audit

• Bias and fairness testing

• Transparency and explain ability

• Human oversight mechanisms

• Impact on stakeholders

5. Business audit

• ROI and business value

• Alignment with objectives

• Risk assessment

• Operational sustainability

Industry-specific requirements

Healthcare:

• HIPAA compliance verification

• Patient safety impact assessment

• Clinical validation requirements

Finance:

• SOC 2 compliance

• Model risk management framework

• Regulatory reporting capability

Legal:

• Confidentiality controls

• Conflict checking mechanisms

• Professional standards compliance

HR/Hiring:

• EEOC compliance verification

• Adverse impact analysis

• Human review in decision-making

DIY vs Hiring experts

DIY when:

• Early stage, limited budget

• Low-risk use cases

• Internal tools only

• You have compliance expertise in-house

Hire experts when:

• Series B+ with enterprise customers

• Regulated industry requirements

• High-stakes decisions (hiring, lending, healthcare)

• Preparing for major customer or investor due diligence

Typical timeline and cost

Basic audit:

• Timeline: 2-4 weeks

• Cost: $15K-30K

• Scope: Single AI system, basic compliance

Comprehensive audit:

• Timeline: 6-8 weeks

• Cost: $50K-100K

• Scope: Multiple systems, full compliance framework

Ongoing monitoring:

• Timeline: Continuous

• Cost: $5K-15K/month

• Scope: Regular audits, compliance tracking

At Islands, we’ve developed a streamlined 5-step framework that takes 2 weeks and costs a fraction of traditional audits (more on this in a future post).

The three questions that actually matter

When we audit AI systems, we focus on three core questions:

1. Is this AI system technically sound and production-ready?

• Does it work reliably?

• How does it handle edge cases?

• What’s the error rate?

• Can it scale?

2. Does it create measurable business value that justifies the cost?

• What’s the ROI?

• What metrics prove value?

• What are alternatives?

• Is it sustainable?

3. Are we compliant with relevant regulations and can we prove it?

• What regulations apply?

• How do we prove compliance?

• What documentation exists?

• What’s missing?

If you can answer yes to all three with evidence, you’re in good shape.

What an AI audit actually delivers

Documentation:

• Technical specifications

• Data governance policies

• Security controls

• Compliance reports

• Audit findings and recommendations

Remediation Plan:

• Identified gaps

• Priority rankings (high/medium/low)

• Specific action items

• Resource requirements

• Timeline estimates

Ongoing Monitoring:

• Key metrics to track

• Regular review cadence

• Alert thresholds

• Escalation procedures

Common audit findings

Based on 40+ audits we’ve conducted:

Most common issues:

• Insufficient documentation (90% of companies)

• No bias testing (75% of companies)

• Inadequate monitoring (70% of companies)

• Missing governance policies (65% of companies)

• No incident response plan (60% of companies)

The good news? These are all fixable.

The enterprise sales advantage

Here’s why this matters for B2B SaaS:

Without an AI audit:

• Enterprise procurement raises red flags

• Legal teams block deals

• Security reviews take 6+ months

• Lost deals to competitors who have audits

With an AI audit:

• Procurement moves faster

• Legal has fewer concerns

• Security reviews take 2-4 weeks

• Competitive advantage in enterprise deals

One client closed a $500K enterprise deal specifically because they had a completed AI audit. The competitor didn’t.

How to prepare

If you’re planning an AI audit:

Week before:

• Gather existing documentation

• Identify key stakeholders

• Prepare system access

• Compile performance metrics

During audit:

• Provide honest answers

• Share challenges openly

• Ask questions

• Take detailed notes

After audit:

• Prioritize findings

• Assign owners

• Set timelines

• Schedule follow-up

The bottom line

An AI audit isn’t just checkbox compliance. It’s your competitive advantage with enterprise buyers.

If you’re Series B+ and serious about enterprise sales, you’ll need this. Better to do it proactively than scramble when a big deal requires it.

Need an AI audit? Islands offers comprehensive AI audits for Series B+ companies. Visit islandshq.xyz/contact