Implementing EU AI Act: A Practitioner’s Path

Table of Contents

1. Why EU AI Act Compliance Matters Now
2. Understanding the EU AI Act Risk Classification
3. Building Your Compliance Operating Model
4. Evidence Patterns and Documentation
5. Tooling and Automation for Audit Readiness
6. Review Cadence and Governance
7. Sector-Specific Implementation Pathways
8. Common Pitfalls and How to Avoid Them
9. Getting Started: Your 90-Day Roadmap

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Why EU AI Act Compliance Matters Now

The EU AI Act is no longer a future concern—it’s operational law. The regulation entered into force in August 2024, and phased implementation timelines are already underway. If your organisation operates in the EU, processes EU citizen data, or serves EU customers, you need a compliance strategy that works in practice, not just in theory.

Unlike GDPR (which focuses on data rights and privacy), the EU AI Act is a risk-based regulatory framework that applies to AI systems themselves. It classifies AI applications into risk tiers—prohibited, high-risk, general-purpose, and minimal-risk—and imposes corresponding compliance obligations. For mid-market AI companies, the stakes are material: non-compliance can result in fines up to €30 million or 6% of annual global turnover, whichever is higher.

The practical challenge isn’t understanding the regulation in isolation. It’s translating regulatory text into operational controls, evidence collection, and audit-ready processes that your engineering and product teams can actually execute without grinding development velocity to a halt.

This guide walks you through the practitioner’s path: how to assess your AI systems against the regulation, build evidence patterns that auditors expect, implement tooling that scales, and establish a review cadence that keeps you compliant without becoming a compliance bureaucracy.

Understanding the EU AI Act Risk Classification

The Four-Tier Risk Model

The EU AI Act divides AI systems into four risk categories. Your compliance obligations depend entirely on which tier your system sits in. Getting this classification right is your first critical task.

Prohibited AI Systems are a small but non-negotiable category. These include:

• Real-time biometric identification systems in public spaces (with narrow law enforcement exceptions)
• Social credit scoring systems that restrict fundamental rights
• Subliminal manipulation designed to distort behaviour
• Exploitation of children or vulnerable persons

If you’re building any of these, stop. The regulation prohibits them outright. There is no compliance pathway. This is a go/no-go decision.

High-Risk AI Systems are where most mid-market AI companies need to focus effort. The AI Act Implementation timeline specifies that high-risk systems must comply by August 2025. High-risk systems include:

• Biometric identification and categorisation (including emotion recognition)
• Critical infrastructure control systems
• Education and vocational training (assessment, profiling, assignment)
• Employment and labour management (recruitment, promotion, termination, working conditions)
• Access to essential services (credit, housing, utilities, healthcare)
• Law enforcement and judicial decision-making
• Migration, asylum, and border control
• Exploitation and abuse detection

If your system falls into any of these categories, you must implement a full compliance regime: risk assessments, quality management, human oversight protocols, documentation, and transparency measures. This is the heavy lift.

General-Purpose AI (GPAI) Systems are large language models and foundation models that aren’t specifically designed for high-risk use but can be deployed in high-risk contexts. Providers of GPAI models (like OpenAI, Anthropic, or Meta) have their own obligations around transparency, documentation, and code of practice compliance. If you’re using GPAI models as a component in your application, you inherit some transparency and disclosure obligations. The compliance burden here is lighter than high-risk, but not zero.

Minimal-Risk AI Systems include most traditional machine learning applications and narrow automation tools. These have no specific compliance obligations under the AI Act, though they may fall under other regulations (GDPR, sector-specific rules, etc.).

Practical Classification Exercise

Start by mapping your current and planned AI systems against these tiers. Create a simple spreadsheet:

• System Name: The product or feature
• Primary Use Case: What it does
• Risk Category: Which tier it falls into (if any)
• Rationale: Why you classified it that way
• Owner: Which team is responsible
• Compliance Status: Not started, in progress, complete

Be conservative in your classification. If a system could plausibly fall into a higher-risk category, classify it there. It’s easier to de-risk a classification later than to discover mid-audit that you misclassified a high-risk system as minimal-risk.

Building Your Compliance Operating Model

Governance Structure

Compliance at scale requires clear ownership and decision rights. You need three roles:

The AI Governance Owner (often a Chief Risk Officer, Head of Legal, or Chief Operating Officer) sets policy, owns audit relationships, and makes final go/no-go decisions on new AI systems. This person is accountable to the board.

The AI Compliance Lead (often a dedicated hire or fractional resource) translates policy into operational requirements, manages documentation, tracks evidence collection, and coordinates reviews. They’re the connective tissue between engineering, product, and governance.

The Technical Leads (engineering, data science, product) implement controls, collect evidence, and participate in reviews. They own the day-to-day compliance execution.

If you don’t have dedicated compliance resources, this is where fractional support becomes material. Many mid-market companies bring in a fractional CTO or compliance specialist to establish the operating model, train the team, and then step back to an advisory cadence. This avoids hiring a full-time compliance officer (which can be expensive) while ensuring you don’t build compliance theatre.

Policy Framework

You need written policies that cover:

1. AI System Classification Policy: How you assess and classify AI systems against the EU AI Act risk tiers
2. Risk Assessment Policy: How you conduct impact and risk assessments for high-risk systems
3. Data Quality and Governance Policy: How you ensure training data is appropriate, documented, and auditable
4. Human Oversight Policy: How you ensure meaningful human review before high-risk decisions
5. Transparency and Disclosure Policy: What information you provide to users and regulators
6. Incident Response Policy: How you detect, investigate, and report AI system failures
7. Third-Party AI Component Policy: How you manage compliance when using AI services, models, or libraries from external vendors

These don’t need to be lengthy legal documents. A 2-3 page policy per topic, written in plain language, is sufficient. The goal is clarity and operationalisation, not comprehensive legalese.

Roles and Responsibilities Matrix

Create a RACI matrix (Responsible, Accountable, Consulted, Informed) that maps compliance activities to roles. Example:

Activity	Engineering	Product	Compliance Lead	Governance Owner
System Classification	R	C	A	I
Risk Assessment	R	C	A	I
Training Data Audit	R	I	C	I
Human Oversight Design	R	R	C	I
Documentation	R	C	A	I
Incident Investigation	R	C	R	A
Regulatory Reporting	I	I	R	A

This prevents ambiguity and ensures accountability. Post it on your wiki or compliance portal.

Evidence Patterns and Documentation

What Auditors Actually Look For

When regulators or third-party auditors review your AI Act compliance, they’re not looking for perfect systems. They’re looking for evidence that you:

1. Know what you’re building: You’ve classified your AI systems, assessed their risks, and documented your reasoning
2. Built it responsibly: You’ve implemented controls proportionate to the risk tier, and you can show how those controls work
3. Can prove it works: You have evidence (logs, metrics, test results) that your controls are functioning as designed
4. Can respond to problems: You have incident detection, investigation, and remediation processes, with examples of how they’ve been used
5. Can explain it: You can articulate to a non-technical regulator how your system works, what it decides, and why

The evidence patterns below are what actually passes audit.

High-Risk System Documentation

For each high-risk AI system, maintain:

1. System Description Document

• What the system does (plain language, no jargon)
• Who it affects (users, subjects, beneficiaries)
• What decisions or recommendations it makes
• How those decisions are used in the real world
• What happens if the system fails

2. Risk Assessment Report

• Identification of potential harms (discrimination, privacy violation, safety risk, etc.)
• Likelihood and severity of each harm
• Mitigation controls you’ve implemented
• Residual risk after controls
• Approval sign-off from governance owner

3. Data Quality and Provenance Documentation

• Where training data came from
• How you validated it for bias, completeness, and accuracy
• What data quality metrics you track
• How you detect and respond to data drift
• Examples of test cases and their results

4. Human Oversight Protocol

• When and how humans review AI decisions
• What information is presented to the human reviewer
• How the human can override or reject the AI recommendation
• Training and qualification requirements for reviewers
• Logs of human decisions and override rates

5. Performance and Fairness Metrics

• Accuracy, precision, recall across relevant subgroups
• Bias metrics (disparate impact, demographic parity, etc.)
• How you measure fairness for your specific use case
• Trends over time
• How you respond to performance degradation

6. Transparency and Disclosure Materials

• Information provided to users about AI involvement
• How users can contest or appeal AI decisions
• Where to report problems or concerns
• Privacy notices specific to the AI system

7. Change Log and Versioning

• Model versions and deployment dates
• What changed between versions
• Why the change was made
• Retraining and revalidation evidence
• Rollback procedures if issues are discovered

Documentation Tools and Workflow

You don’t need a separate compliance system. Use what you already have:

• Confluence or Notion: Store policy documents, risk assessments, and human oversight protocols
• GitHub or GitLab: Version control for model code, training pipelines, and configuration
• Data catalogues (like Collibra or Alation, or open-source alternatives): Document data sources, transformations, and quality metrics
• Experiment tracking (like MLflow or Weights & Biases): Log model versions, hyperparameters, and performance metrics
• Incident tracking (like Jira or Linear): Record AI system failures, investigations, and remediation
• Spreadsheets or databases: Track system classifications, risk assessments, and control implementation status

The key is that documentation lives close to the code and data, not in a separate compliance filing cabinet. This increases the chance that engineers will actually maintain it.

Tooling and Automation for Audit Readiness

Compliance-as-Code Approach

Instead of manual compliance checking, automate what you can. This reduces human error and creates audit trails automatically.

Model Card Generation: Tools like Hugging Face Model Cards or custom scripts can auto-generate system descriptions, performance metrics, and limitation statements directly from your model registry. This ensures documentation stays current with your actual models.

Data Lineage and Governance: Implement data lineage tracking so you can answer “where did this training data come from?” in seconds, not weeks. Tools like Apache Atlas (open-source) or commercial platforms like Collibra integrate with your data pipelines and create automatic lineage graphs.

Bias Detection in CI/CD: Integrate bias and fairness testing into your continuous integration pipeline. Tools like Fairlearn, AI Fairness 360, or Seldon Core can run fairness checks on every model deployment and flag regressions automatically. If a model shows increased disparate impact on a protected group, the pipeline fails and alerts the team.

Incident Detection and Logging: Implement structured logging for all AI system decisions. Log the input, the model’s decision, the human’s decision (if applicable), and the outcome. This creates the audit trail automatically. Use a structured logging library (like Python’s structlog) so logs are machine-readable and queryable.

Example:

{
  "timestamp": "2024-01-15T10:23:45Z",
  "system_id": "credit-risk-v3.2",
  "input_hash": "a1b2c3d4",
  "model_decision": "approve",
  "confidence": 0.87,
  "human_reviewed": true,
  "human_decision": "approve",
  "human_override": false,
  "outcome_30days": "repaid_on_time",
  "fairness_metrics": {"disparate_impact_ratio": 0.98}
}

With this structure, you can query “how many decisions did humans override last month?” or “what’s the disparate impact ratio for female applicants?” without manual investigation.

Vanta and SOC 2 / ISO 27001 Integration

If you’re pursuing SOC 2 or ISO 27001 compliance via Vanta, integrate your AI compliance evidence into the same framework. Many compliance controls overlap:

• Access controls (who can access training data, model code, and production systems)
• Change management (how model changes are reviewed and approved)
• Incident response (how you detect and respond to AI system failures)
• Data security (encryption, retention, deletion of training data)
• Documentation and evidence (maintaining audit trails)

Vanta can help you collect evidence for these shared controls automatically, reducing the overhead of managing parallel compliance regimes. This is particularly valuable if you’re pursuing both AI Act compliance and security certification for enterprise deals.

Monitoring and Alerting

Once your AI system is in production, you need continuous monitoring for:

• Performance degradation: Is accuracy, precision, or recall declining?
• Bias drift: Are fairness metrics moving in the wrong direction?
• Data drift: Is the input distribution changing, suggesting the model is being used in a new context?
• Anomalous decisions: Is the model making unusual recommendations that might indicate a bug or adversarial input?
• Human override rates: Are humans overriding the model more frequently, suggesting loss of trust?

Tools like Evidently AI, WhyLabs, or Arize provide dashboards and alerts for these metrics. Set up alerts that trigger when thresholds are breached—e.g., if accuracy drops below 85%, or if the override rate exceeds 20%, or if disparate impact ratio falls below 0.8.

When an alert fires, you have a documented process: investigate the root cause, determine if the system should be retrained or taken offline, implement the fix, and log the incident. This is audit-ready incident response.

Review Cadence and Governance

Quarterly Compliance Review

Every quarter, convene your AI governance owner, compliance lead, and technical leads to review:

1. New AI systems: Any new systems classified as high-risk since the last review?
2. Risk assessment updates: Have risks changed? Do assessments need updating?
3. Control effectiveness: Are the controls we implemented actually working? Do we have evidence?
4. Incidents and near-misses: What went wrong? What did we learn? What did we fix?
5. Fairness and performance metrics: Are systems performing as expected across all user groups?
6. Regulatory changes: Have there been updates to the AI Act, sector-specific guidance, or enforcement actions that affect us?
7. Third-party dependencies: Are the AI services and models we depend on still compliant? Have their terms changed?
8. Documentation gaps: Is our documentation current and accurate?

Document the outcomes of each review in a compliance report. This becomes your evidence that you’re actively managing AI compliance, not just building systems and hoping for the best.

Annual Compliance Audit

Once a year, conduct a deeper audit. This can be internal or external (we recommend external for credibility with regulators and enterprise customers).

The audit should:

• Validate your system classifications against the regulation
• Test your risk assessments for completeness and accuracy
• Review a sample of high-risk systems in detail (documentation, controls, evidence)
• Test your incident detection and response processes
• Validate that your monitoring and alerting actually work
• Interview stakeholders to understand how compliance is actually being managed day-to-day
• Identify gaps and recommend remediation

An external audit typically costs €5K–€20K depending on scope and your industry. It’s an investment, but it gives you credible evidence that you’re compliant, and it identifies gaps before regulators do.

Incident Review and Learning

When something goes wrong—a model makes a discriminatory decision, data is mishandled, a human overseer misses a critical error—you need a documented process:

1. Detect: Monitoring alerts you to the problem
2. Isolate: Take the system offline if necessary to prevent further harm
3. Investigate: Root cause analysis. What happened? Why?
4. Remediate: Fix the underlying issue (retrain the model, fix the data, improve oversight)
5. Validate: Test that the fix works
6. Deploy: Return the system to production
7. Communicate: Inform affected parties (users, regulators, customers) as required
8. Document: Record the incident, investigation, and remediation for audit purposes
9. Learn: Update your policies, controls, or training based on what you learned

Keep an incident log. Include:

• Date and time
• System affected
• Description of the incident
• Root cause
• Remediation taken
• Time to resolution
• Regulatory notification (if required)

Over time, this log shows regulators that you’re actively managing risk, not ignoring problems.

Sector-Specific Implementation Pathways

Financial Services

If you’re deploying AI in credit decisions, investment advice, or fraud detection, you’re almost certainly in the high-risk category. Beyond the EU AI Act, you also need to comply with APRA CPS 234, ASIC RG 271, and AUSTRAC regulations if you operate in Australia.

Key controls:

• Credit decisions: Explainability is critical. Regulators want to understand why the system approved or rejected a loan. Use SHAP values or similar techniques to generate explanations
• Fraud detection: Bias is a major concern. Ensure your model doesn’t discriminate against certain customer segments
• Investment advice: Suitability is essential. The system must consider the customer’s financial situation, goals, and risk tolerance
• Data quality: Financial data is often incomplete or inconsistent. Document your data cleaning and validation processes

Healthcare

AI in healthcare (diagnosis support, treatment recommendations, patient monitoring) is high-risk. You also need to comply with Privacy Act 1988 and My Health Record regulations if you operate in Australia.

Key controls:

• Clinical validation: Before deploying a diagnostic AI system, conduct clinical trials to validate accuracy and safety
• Human oversight: Medical professionals must review AI recommendations before they affect patient care
• Data privacy: Patient data is highly sensitive. Implement strong encryption, access controls, and retention policies
• Transparency: Patients have a right to know that an AI system is involved in their care
• Liability: Ensure you have clear protocols for who is responsible if the AI makes a mistake

Recruitment and Employment

AI used in hiring decisions, performance evaluation, or termination is high-risk. Beyond the EU AI Act, you may also face employment law and anti-discrimination regulations.

Key controls:

• Bias testing: Before using an AI system to screen candidates, test it for bias against protected groups (gender, age, ethnicity, disability)
• Transparency: Candidates have a right to know they’re being evaluated by an AI system
• Human review: AI should support hiring decisions, not replace them. Humans must review and approve recommendations
• Appeals process: Candidates should be able to contest or appeal AI-based decisions
• Data retention: Don’t keep candidate data longer than necessary

Aerospace and Defence

If you’re deploying AI in defence manufacturing or critical infrastructure, you face ITAR, DSGL, and DISP constraints. These regulations restrict what data you can use and where your systems can run.

Key controls:

• Data sovereignty: Ensure training data and model weights are stored and processed in approved jurisdictions
• Access controls: Restrict access to AI systems to authorized personnel only
• Audit trails: Maintain detailed logs of who accessed what, when, and why
• Encryption: Use strong encryption for data in transit and at rest
• Compliance certification: Obtain certifications (like ITAR compliance or DISP accreditation) before deploying systems

Common Pitfalls and How to Avoid Them

Pitfall 1: Misclassifying Systems as Lower-Risk Than They Are

The Problem: You classify a system as minimal-risk to avoid compliance overhead, but it actually falls into the high-risk category. When regulators audit you, they discover the misclassification and assume you’re deliberately evading the regulation.

How to Avoid It:

• Use a conservative classification approach. If a system could plausibly be high-risk, classify it there
• Document your classification reasoning. If you later need to defend the classification to a regulator, you want evidence that you thought carefully about it
• Get a second opinion. Have your compliance lead or an external advisor review classifications for subjective cases
• Revisit classifications quarterly. As your system evolves, its risk profile might change

Pitfall 2: Building Compliance Theatre Instead of Real Controls

The Problem: You create extensive documentation and policies, but they don’t reflect what your engineering team actually does. When auditors test your controls, they discover the gap. This is worse than having no documentation—it looks like you’re deliberately hiding the truth.

How to Avoid It:

• Involve engineers in policy design. Policies that engineers didn’t help create are policies they won’t follow
• Keep policies simple and practical. A 2-page policy that everyone understands is better than a 20-page policy that no one reads
• Automate what you can. Compliance that requires manual work is compliance that will be skipped
• Test your controls. Don’t assume they work—verify they actually do what you claim
• Update documentation when reality changes. If your process evolves, update the policy to match

Pitfall 3: Ignoring Bias and Fairness

The Problem: You build an AI system that works well on average but discriminates against certain groups. This violates the AI Act’s fairness requirements and exposes you to regulatory action and litigation.

How to Avoid It:

• Test for bias before deployment. Use tools like Fairlearn or AI Fairness 360 to identify disparities
• Define fairness for your use case. Different applications require different fairness metrics. For hiring, you might use demographic parity. For credit, you might use equalized odds
• Monitor fairness continuously. Set up alerts that trigger if fairness metrics degrade
• Respond quickly. If bias is detected, retrain the model or take it offline. Don’t wait for a regulator to tell you there’s a problem
• Document your approach. Show that you’ve thought carefully about fairness and have processes to manage it

Pitfall 4: Treating High-Risk Systems Like Minimal-Risk

The Problem: You deploy a high-risk system (e.g., a credit decision AI) with minimal human oversight, poor documentation, and no fairness monitoring. When the system makes discriminatory decisions, you have no evidence that you tried to prevent it.

How to Avoid It:

• Implement human oversight proportionate to risk. For high-risk systems, humans should review decisions before they’re implemented
• Maintain detailed documentation. For high-risk systems, you need risk assessments, data quality reports, fairness metrics, and incident logs
• Monitor continuously. High-risk systems need real-time monitoring for performance, bias, and anomalies
• Test thoroughly. Before deploying a high-risk system, conduct rigorous testing for accuracy, bias, and robustness
• Get approval. High-risk systems should be approved by your governance owner before they go live

Pitfall 5: Depending on Third-Party AI Services Without Understanding Their Compliance Status

The Problem: You use a third-party AI service (like an API from a GPAI provider or a pre-trained model) without understanding its compliance obligations or limitations. When regulators ask how your system meets the AI Act requirements, you can’t answer because you don’t control the underlying model.

How to Avoid It:

• Audit third-party AI components. Before using an external model or service, understand its training data, performance characteristics, and limitations
• Get compliance documentation. Ask vendors for risk assessments, fairness reports, and transparency documentation
• Maintain control. If possible, fine-tune models or wrap them with additional controls (like human oversight) to ensure they meet your compliance requirements
• Document dependencies. Keep a registry of all third-party AI components, their versions, and their compliance status
• Monitor for changes. If a vendor updates their model or changes their terms, reassess your compliance status

Getting Started: Your 90-Day Roadmap

Weeks 1–2: Assessment and Planning

Objective: Understand where you are today and what you need to do.

Activities:

1. Map your AI systems: List every AI system you operate or plan to operate. Include internal tools, customer-facing features, and third-party services
2. Classify each system: Determine whether each system is prohibited, high-risk, general-purpose, or minimal-risk
3. Identify gaps: For each high-risk system, identify what compliance activities are missing (risk assessment, data quality documentation, human oversight, fairness monitoring, etc.)
4. Assign ownership: Designate a compliance lead and ensure each system has an engineering owner
5. Create a roadmap: Prioritize which systems to address first. Start with systems that are already in production and serving customers

Deliverable: A spreadsheet listing all systems, their classifications, compliance gaps, and a prioritized roadmap.

If you don’t have internal capacity, this is a good time to bring in a fractional compliance advisor or CTO to help with assessment and planning. A 1-2 week engagement (AU$10K–AU$20K) can save you months of false starts.

Weeks 3–6: High-Risk System Deep Dives

Objective: For each high-risk system, build the compliance foundation.

Activities:

1. Risk Assessment: Conduct a formal risk assessment. Identify potential harms, assess likelihood and severity, design mitigations
2. Data Quality Audit: Document where training data came from, validate it for bias and completeness, establish data quality metrics
3. Human Oversight Design: Define when and how humans review AI decisions. Document the process and train reviewers
4. Fairness Testing: Test the model for bias across protected groups. Establish fairness baselines and monitoring
5. Documentation: Write system descriptions, model cards, and transparency materials

Deliverable: For each high-risk system, a complete compliance file with risk assessment, data documentation, fairness report, and human oversight protocol.

This is intensive work. For 2–3 high-risk systems, expect 4–6 weeks of engineering effort. If you’re short on capacity, bring in external support to accelerate.

Weeks 7–9: Tooling and Automation

Objective: Automate compliance evidence collection so compliance becomes continuous, not episodic.

Activities:

1. Implement monitoring: Set up dashboards and alerts for performance, bias, and anomalies
2. Structured logging: Implement structured logging for all AI decisions. Ensure you can query decisions, overrides, and outcomes
3. Incident tracking: Set up a process for detecting, investigating, and logging incidents
4. Fairness automation: Integrate fairness testing into your CI/CD pipeline
5. Documentation automation: Set up automated generation of model cards and system descriptions

Deliverable: Dashboards showing compliance status in real-time. Alerts configured for key metrics. Incident tracking system operational.

Weeks 10–12: Governance and Review

Objective: Establish ongoing governance so compliance is maintained, not just achieved once.

Activities:

1. Policy finalization: Finalize your AI governance policies and get sign-off from leadership
2. Team training: Train your engineering and product teams on policies and processes
3. First compliance review: Conduct your first quarterly compliance review. Review all systems, update classifications, assess control effectiveness
4. External audit: Commission an external audit of your high-risk systems. Get credible evidence that you’re compliant
5. Incident response drill: Run a simulated incident (e.g., a model that exhibits unexpected bias). Test your detection and response processes

Deliverable: Signed policies. Trained team. First compliance review report. External audit report. Incident response drill results.

Beyond 90 Days: The Ongoing Operating Model

After the initial 90 days, compliance becomes a steady-state activity:

• Weekly: Monitor dashboards for alerts. Investigate and respond to incidents
• Monthly: Review incident logs and fairness metrics. Update documentation if systems change
• Quarterly: Compliance review with governance owner, compliance lead, and technical leads. Update risk assessments, review control effectiveness
• Annually: External audit. Comprehensive review of all systems and policies. Board-level reporting

This cadence ensures you stay compliant without becoming a compliance bureaucracy. The key is automation—the more you automate evidence collection, the less manual work compliance requires.

Practical Next Steps

You now have a framework for implementing EU AI Act compliance. Here’s how to move forward:

If you have the capacity internally: Start with the 90-day roadmap. Begin with assessment and planning (weeks 1–2). Prioritize your high-risk systems and build compliance evidence methodically.

If you’re short on capacity: Bring in external support for the assessment and deep dives (weeks 1–6). This accelerates your timeline and brings external perspective on risk classification and control design. Once the foundation is built, your team can manage ongoing compliance with light advisory support.

If you’re unsure about your AI system classifications: Take a free AI Readiness Test to understand where you stand. Or book a 30-minute call with our Sydney-based AI advisory team to discuss your specific systems and compliance requirements.

If you need structured assessment: Consider a two-week AI Quickstart Audit (AU$10K, fixed scope). We’ll assess your AI systems, classify them against the EU AI Act, identify compliance gaps, and give you a prioritized roadmap for the next 90 days.

If you’re also pursuing SOC 2 or ISO 27001 compliance: Integrate your AI compliance evidence into your security audit via Vanta. Many controls overlap, and managing them together reduces overhead.

The EU AI Act is now operational law. Compliance isn’t optional. But it doesn’t have to be painful. With the right framework, tooling, and governance, you can build AI systems that are both powerful and compliant—and you can prove it to regulators, customers, and your board.

Start this week. Your 90-day roadmap is in front of you. Pick the first activity (assessment and planning) and get your team moving. The sooner you start, the sooner you’re compliant.