If you work in compliance today, it can feel like the ground is constantly moving under your feet.
Regulatory change is accelerating across every major jurisdiction, from GDPR, DORA, MiCA and NIS2 in Europe, to privacy, cybersecurity and sector-specific rules in North America, to fast-evolving ESG and sustainability reporting mandates globally. On top of that, industry bodies and supervisory authorities are issuing new guidance, expectations and enforcement trends at a pace that’s hard to track, let alone operationalise.
The pressure on compliance teams is rising on all fronts:
- Cost: more reporting, more controls, more audits.
- Complexity: overlapping regimes, cross-border operations, and new thematic risks such as AI, third-party risk, and climate.
- Oversight: regulators expect timely, evidence-backed responses, digital audit trails, and demonstrable governance.
Legacy systems and manual workflows were not designed for this environment. Static spreadsheets, email threads, and siloed case-management tools make it difficult to maintain a single source of truth, respond quickly to new obligations, or provide the level of transparency regulators now assume.
In this context, AI and automation are no longer “nice to have”. They are becoming survival tools.
The most effective compliance functions in 2026 and beyond will be those that blend deep domain expertise with modern technology. They will use AI to augment, not replace, the judgement of experienced professionals, giving teams the scale, speed and visibility needed to stay ahead of change rather than constantly reacting to it.
This guide explores how modern compliance teams are using AI and technology to adapt, what it takes to do this safely, and how organisations can build a 2026 roadmap that is both regulator-ready and future-proof.
What Modern Compliance Teams are Struggling with
Before we talk about solutions, it’s worth being honest about where most organisations are today. Very few compliance leaders feel they have “cracked” the technology challenge.
Fragmented Data Across Systems
Compliance-relevant data often lives in dozens of places: transaction systems, HR platforms, vendor tools, email, shared drives, ticketing tools, and legacy databases. A simple question, “Show me all the controls and evidence that relate to this obligation” can turn into a multi-week exercise: exporting CSV files, reconciling IDs, and chasing colleagues for missing documents.
In a regulatory investigation or tight audit window, that fragmentation turns into real risk.
Manual Monitoring, Reporting and Audit Preparation
Many compliance teams still rely on manual sampling in Excel, copy-pasting into PowerPoint, and ad-hoc scripts built by a helpful person in IT five years ago. Periodic monitoring (e.g. quarterly reviews) makes it hard to detect issues early, and audit preparation becomes a crunch period where teams scramble to assemble and validate evidence.
It’s not unusual to see senior compliance professionals spending a large portion of their time on data wrangling rather than risk analysis.
Rising Expectations from Regulators
Regulators increasingly expect:
- Auditability: clear records of who did what, when, and based on which data.
- Traceability: the ability to trace a report or decision back to underlying evidence and controls.
- Explainability: especially where models, automation or AI are involved.
Ad-hoc tools and undocumented workflows make it difficult to demonstrate this level of control, even when teams are doing the right thing in practice.
Interpreting and Operationalising Regulatory Updates
Compliance staff are bombarded with regulatory updates: consultation papers, technical standards, FAQs, supervisory statements, enforcement actions and speeches that subtly shift expectations.
Reading, interpreting, and mapping these changes to internal policies and controls is cognitively demanding and time-consuming. It’s easy to miss something, duplicate effort across teams, or over-correct in one area while under-addressing another.
Growing Cost of Non-Compliance
The financial impact of getting it wrong is increasing: higher fines, remediation programmes, remediation consultants, and reputational damage that affects customer trust and market value.
At the same time, internal budgets are not unlimited. Compliance leaders are being asked to “do more with the same” or even less.
Skills Gap and Talent Shortages
There is a global shortage of professionals who understand both complex regulation and data/technology. Many compliance teams are rich in legal and policy expertise but dependent on other functions for data access, analytics or automation.
In a market where tech-savvy compliance professionals are in high demand, retaining and enabling your existing team becomes critical.
ESG and Sustainability Reporting
ESG regulations such as CSRD and SFDR introduce a new layer of complexity:
- Non-financial but highly data-driven metrics
- Multiple frameworks and standards
- The need to gather data from across the value chain, including suppliers
Manual collection and validation of ESG data is already stretching many teams beyond their limits.
Taken together, these realities explain why many organisations feel they are always catching up rather than proactively managing regulatory risk.
Why AI is Becoming Essential to Compliance (Not Optional)
Against this backdrop, AI is starting to change the game. The question for leading organisations is no longer “Should we use AI in compliance?” but “How do we use it safely, effectively and credibly?”
Accelerating regulatory text analysis
Natural language processing (NLP) models can rapidly process large volumes of regulatory text, consultation papers, rules, guidance, enforcement notices, and help teams:
- Identify which sections are likely to be relevant to their business
- Summarise key changes and obligations
- Compare new requirements to existing policies or controls
This doesn’t replace legal interpretation, but it dramatically reduces the time spent locating, reading and structuring information.
Reducing risk through anomaly detection and pattern recognition
Machine learning models can analyse transactional and behavioural data to spot unusual patterns that might indicate compliance issues, for example, AML-like red flags, unusual user access patterns, out-of-policy expense claims, or control failures.
Rather than manually checking samples, teams can focus their expertise on high-risk outliers surfaced by AI.
Enabling predictive and preventative compliance
With enough data and the right features, AI can help predict where issues are likely to arise, for instance, business units with a high probability of control gaps, vendors with elevated risk, or emerging trends in incidents that point to systemic weaknesses.
This shifts compliance from reactive “after the fact” reviews to preventative interventions.
Improving accuracy and consistency
AI systems excel at applying rules consistently once they are well-defined. They don’t get tired at 11pm during audit season, and they don’t accidentally skip a step in a long checklist. Used carefully, they can:
- Reduce clerical errors in data entry and validation
- Ensure policies are checked against the latest version of a regulation
- Maintain standard formats and terminology across reports
End-to-end workflow automation with AI agents
AI agents, systems that can plan, execute, and co-ordinate tasks across multiple tools, open the door to automating complex workflows, not just isolated steps. We’ll explore this in more detail later, but think of agents that:
- Monitor regulatory feeds, flag relevant changes, and draft impact assessments
- Collect evidence from multiple systems and assemble audit-ready dossiers
- Pre-populate sections of compliance reports for human review
Real-time monitoring at scale
Manual processes tend to be periodic. AI-powered systems can operate continuously, ingesting events and data streams in near real time. This makes it possible to detect issues sooner, respond faster, and maintain a live view of your compliance posture instead of relying on quarterly snapshots.
Critically, none of this removes the need for human oversight. The most robust models and agents are those that are supervised by experienced compliance professionals, with clear guardrails, checks and escalation paths.
The Core Technologies Modern Compliance Teams Should Adopt
A modern compliance function isn’t defined by a single tool. It’s an ecosystem, a stack of capabilities that work together to support governance, risk and compliance at scale. Below are the building blocks we see leading organisations adopting.
a) AI & Machine Learning
- NLP for regulatory text parsing: models that can ingest legal and regulatory documents, highlight obligations, classify content by topic or risk area, and surface relevant sections for a given product or geography.
- Document classification: automatically tagging policies, procedures, contracts and correspondence with the right categories (e.g. data protection, AML, HR, vendor risk), making retrieval and review easier.
- Risk scoring models: using data to prioritise entities (clients, vendors, processes, controls) by risk level, so teams can focus on what matters most.
- Predictive analytics: forecasting potential incidents or control failures based on historical patterns, business growth, or changes in the regulatory environment.
b) Automation & Workflow Orchestration
- Compliance task automation: from routine KYC document checks to periodic certifications and attestations, automation can eliminate repetitive, low-value work.
- Policy lifecycle management: workflows that manage drafting, review, approval, publication, training and attestations for policies and procedures.
- Automated evidence collection: pulling logs, screenshots, tickets and approvals from multiple systems into a structured evidence repository for audits.
c) Data Governance & Quality Management
AI and analytics are only as good as the data underneath them. Modern compliance teams need:
- A single source of truth for compliance data: a governed data model where obligations, controls, risks, incidents and evidence are linked.
- Structured models for regulatory data: the ability to track which rules apply to which entities, processes and geographies.
- Metadata and lineage tracking: knowing where data came from, how it has been transformed, and how it’s used in reports or models, a key requirement for auditability and model risk management.
d) GRC Platforms
Governance, Risk and Compliance (GRC) platforms provide the backbone of many modern compliance programmes:
- Risk registers and assessments
- Controls management and testing
- Issue and incident management
- Audit planning and follow-up
The most effective implementations integrate GRC platforms with AI-driven analytics and workflow tools, rather than treating them as isolated systems.
e) ESG & Sustainability Reporting Tools
For organisations in scope of CSRD, SFDR or similar frameworks, specialised ESG tooling is becoming essential:
- Data collection from multiple internal and external sources
- Calculation engines for emissions, diversity metrics and other KPIs
- Integrated reporting aligned to recognised standards
- Assurance-ready audit trails
Combining ESG tools with broader data governance and AI capabilities allows teams to validate data, detect anomalies and streamline assurance.
f) Secure Cloud Architecture
All of the above depends on a secure, compliant infrastructure foundation:
- Identity and access management with least-privilege principles
- Encryption at rest and in transit
- Segregation of environments (dev, test, prod)
- Logging and monitoring aligned to regulatory expectations
- Architecture patterns that support data residency, sovereignty, and sector-specific requirements
This is the layer where technology and compliance teams must work hand-in-hand.
Zartis typically engages with clients across this entire spectrum, helping them design secure cloud architectures, build AI-enabled applications and agents, and integrate them into their GRC and compliance processes in a way that stands up to regulatory scrutiny.
How AI Agents Can Transform Compliance Workflows
Traditional automation is good at repeatable, well-defined tasks. AI agents go a step further: they can interpret context, make decisions within predefined rules, and orchestrate work across several systems.
For compliance, this opens up powerful possibilities.
Intelligent Triage and Escalation
Agents can monitor streams of events, transactions, alerts, user actions, third-party risk feeds, and:
- Classify events by type and severity
- Cross-reference them with policies, customer profiles or historical cases
- Escalate high-risk items to human reviewers with a pre-filled summary
This reduces noise, shortens investigation times, and ensures that senior staff spend their time on genuinely complex issues.
Regulatory Intelligence
Instead of manually subscribing to multiple newsletters and websites, organisations can use agents that:
- Watch for changes on official regulatory portals and trusted sources
- Use NLP to extract key obligations and deadlines
- Map those changes to internal policies, business units or products
- Draft an initial impact assessment for human review
Drafting Reports and Documentation
Agents can assist with:
- First drafts of regulatory reports or responses, based on structured data and templates
- Summaries of long investigation files or case histories
- Cross-referencing evidence to specific control requirements
Humans remain responsible for final sign-off and legal interpretation, but agents remove a significant amount of manual assembly.
Pre-audit Checks
Before an internal or external audit, agents can:
- Check whether required evidence is present and up to date
- Flag missing attestations or overdue control tests
- Generate a readiness report indicating areas of confidence and concern
Safeguards for Regulated Environments
Of course, any AI deployment in a regulated context must include strong safeguards:
- Explainability: being able to show how an agent arrived at a conclusion or classification.
- Traceability: complete audit logs of model inputs, outputs and actions taken.
- Data boundaries: strict controls over what data agents can access, especially when using external models.
- Hallucination controls: techniques to reduce or detect unsupported statements, combined with human review for high-risk outputs.
- Role-based access: ensuring agents act within defined permissions and escalation thresholds.
At Zartis, when we design and implement AI agents for compliance scenarios, these safeguards are part of the architecture from day one, not an afterthought.
What Compliance Teams Must Get Right Before Implementing AI
Not every organisation is ready to jump straight into AI-enabled compliance. The most successful programmes share a few common foundations.
Data readiness
- Key data sources identified and connected
- Critical fields defined and understood
- Basic quality checks in place (consistency, completeness, timeliness)
AI will not magically fix poor data. If anything, it will amplify underlying issues unless those are addressed.
Clear Ownership and Governance
There must be clarity on:
- Who owns compliance models and agents
- Who is responsible for monitoring their performance and risk
- How changes and updates are managed
Many organisations extend existing model risk management or AI governance frameworks to cover compliance use cases.
Defined Risk Appetite and Controls
Compliance leadership, risk, and senior management must agree on:
- Which decisions can be automated, and to what degree
- When human review is mandatory
- How errors or false positives/negatives will be handled
This ensures AI augments rather than undermines your control environment.
Regulatory Expectations
Depending on your sector and geography, you may need to consider:
- Data protection requirements (e.g. GDPR)
- Sectoral rules (e.g. SOX for financial reporting, HIPAA for health data)
- Upcoming AI-specific regulations that touch on explainability and high-risk systems
Aligning your AI programme with these expectations from the start avoids costly redesigns later.
Human-in-the-Loop Oversight
For critical decisions, human oversight is non-negotiable. Well-designed systems:
- Make it easy for reviewers to see why an agent recommended an action
- Provide access to underlying data and evidence
- Capture reviewer feedback to improve models over time
Secure Integration with Legacy Systems
Few organisations have the luxury of starting from a blank slate. AI solutions must integrate with existing case-management tools, document repositories, and line-of-business systems without compromising security or stability.
Vendor Due Diligence
When using third-party AI tools or platforms, compliance teams should be closely involved in vendor selection, assessing:
- Data handling and residency
- Security certifications and controls
- Model governance practices
- Ability to provide audit trails and explanations
Zartis often partners with clients at this stage, assessing data readiness, designing the governance model, and helping evaluate vendors and architectures before any implementation begins.
Real Examples: What AI-Enabled Compliance Looks Like in Practice
To make this more concrete, here are a few anonymised scenarios we’ve seen in the market.
Automating ESG Validation and Reporting
A multinational organisation subject to CSRD built an AI-enabled pipeline that:
- Ingests ESG data from different internal systems and suppliers
- Uses rules and machine learning models to flag inconsistent or missing values
- Suggests corrections or queries for human review
- Automatically populates standard reporting templates
Result: shorter reporting cycles, fewer manual reconciliations, and greater confidence in data quality ahead of assurance.
Detecting Anomalies in Transaction Patterns
A financial services firm deployed anomaly-detection models on top of existing transaction monitoring rules. Models identify unusual behaviours that rules might miss, for example, new combinations of geography, products and customer segments.
Investigators use AI-generated summaries to quickly understand why a case was flagged, and their feedback helps refine the models over time.
Automating Policy Mapping to Regulatory Frameworks
A global company subject to multiple regulatory regimes used NLP to:
- Parse regulatory texts and map obligations to a structured taxonomy
- Tag internal policies and controls with the same taxonomy
- Highlight areas where controls did not fully cover obligations
This significantly reduced the time required for gap analyses when new rules were introduced.
AI-Assisted Case Triage
In a large case-management system handling whistleblowing, conduct, and other incidents, an AI model classifies incoming cases, suggests priority levels, and recommends the most relevant policy references.
Case handlers retain full control but benefit from faster triage and a clearer starting point for investigation.
Simplifying Documentation for Audits
Before external audits, AI agents compile evidence packages: pulling documents, logs and approvals from integrated systems and organising them by control and audit requirement.
What previously took weeks of manual effort now takes days, with humans focused on validation rather than basic assembly.
Organisational Impact: What Happens When Compliance Modernises
When compliance teams successfully combine domain expertise with modern technology, the impact goes beyond individual processes.
- Faster audit cycles: evidence is easier to find, reports are generated more quickly, and issues are identified earlier.
- Lower cost of compliance: automation reduces repetitive manual work, freeing experts to focus on high-value analysis and advisory tasks.
- Reduced error rates: standardised workflows and automated checks cut down on clerical errors and inconsistencies.
- Scalability without headcount spikes: as the organisation grows, AI and automation absorb much of the additional workload.
- Stronger risk posture: real-time monitoring and predictive analytics make it easier to spot emerging issues and act early.
- Increased trust from regulators and stakeholders: transparent processes, better documentation, and responsive remediation build credibility.
- From reactive to proactive: compliance shifts from being perceived as a “policing” function to a strategic partner that enables safe growth and innovation.
For many organisations, this transformation also improves employee experience. Talented compliance professionals would rather spend their time advising the business and solving complex problems than copying data between spreadsheets.
How to Get Started: A Practical Roadmap
Every organisation starts from a different point, but a structured approach helps reduce risk and build momentum.
Assess your regulatory landscape and pain points
Map out the key regimes, obligations and upcoming changes. Identify where your team feels the most pressure today, e.g. monitoring, reporting, audits, ESG.
Evaluate current data and workflows
Understand where relevant data lives, how it flows, and which tools are involved. Identify manual steps, bottlenecks and duplicated effort.
Identify AI-ready opportunities
Look for use cases that:
- Have enough data to train or configure models
- Are high volume and repeatable
- Carry manageable risk if something goes wrong (with humans still in the loop)
Design a compliance-tech architecture
Define how AI, automation, GRC platforms, ESG tools and data governance will fit together. Consider security, integration with legacy systems, and regulatory expectations from day one.
Pilot, test and validate
Start small with one or two well-scoped pilots. Use strong success criteria, including accuracy, efficiency, user satisfaction and regulatory comfort.
Scale gradually with controls and governance
As pilots succeed, extend them to more teams, regions or processes, while maturing your AI governance, monitoring and documentation.
Embed continuous improvement
Treat AI-enabled compliance as an evolving capability, not a one-off project. Capture feedback, refine models, and keep pace with regulatory change.
Zartis often partners with clients across this journey, from early assessment and architecture design through to building AI agents, integrating platforms, and supporting long-term operations.
Closing Thought: What Compliance Looks Like by 2025–2030
Looking ahead, a few things seem clear.
AI will be embedded, not optional. Regulators will expect organisations to manage digital risk with digital tools.
Compliance will move from policing to enablement. The teams that succeed will be those that help the business innovate safely, not just say “no”.
Risk functions will become more tech-led. Data, engineering and AI expertise will sit alongside legal and policy skills in modern compliance teams.
Digital audit trails will be assumed. Paper-based or ad-hoc processes will increasingly be seen as red flags.
The organisations that thrive in this environment will be those that act early, combining strong governance with practical experimentation, and treating AI not as a buzzword but as a carefully governed capability.
If you’re exploring AI or compliance automation, our team can help you design and build a secure, scalable roadmap tailored to your regulatory environment, one that gives your compliance function the tools it needs to stay ahead of constant change.
Build Compliance Systems That Are Secure, Scalable and Future-Ready
Modern compliance is no longer just about meeting today’s requirements, it’s about building the technological foundation to stay ahead of constant regulatory change. If your organisation is exploring AI-driven compliance tools, automated reporting systems, secure cloud architectures or a broader compliance-tech transformation, Zartis can help.
Our teams work with compliance, legal, ESG and risk-intensive organisations to design and build software that strengthens governance, reduces manual work, and supports audit-ready operations. Whether you need an AI agent to automate regulatory monitoring, a custom GRC platform, a secure data architecture, or end-to-end workflow automation, we bring deep engineering expertise combined with a strong compliance-first mindset.
- GRC & compliance platforms: Custom systems for risk registers, controls testing, incident management, and policy lifecycle automation.
- ESG & sustainability reporting tools: CSRD/SFDR data pipelines, validation engines, reporting dashboards and assurance-ready documentation.
- Data governance & secure cloud foundations: Compliance-grade architectures, data lineage, encryption, identity/access controls, and regulator-friendly transparency.
- AI agents for compliance: From pre-audit checks to document classification to anomaly detection, built with guardrails, explainability, and traceability.
If you’re ready to accelerate your compliance transformation, safely, responsibly and with scalable technology, we’d love to start a conversation. Let’s explore how Zartis can support your compliance transformation.