How technology can strengthen accountability and trust in connected products
Why RegTech Matters Now for AI-Enabled Systems
Artificial intelligence and connected devices are reshaping how products are designed, tested, deployed, and governed. Across jurisdictions, regulatory obligations covering data protection, AI governance, cybersecurity, product safety, digital markets, consumer rights, and sector-specific assurance increasingly intersect within the same product lifecycle. For manufacturers and service providers, the central challenge is no longer understanding individual laws, but managing a layered and constantly evolving regulatory environment. RegTech provides the technical infrastructure to structure, automate, and evidence this complexity. The question for manufacturers and service providers is no longer ‘are we compliant?’ but ‘can we prove it, continuously, across jurisdictions?’ Regulatory Technology (RegTech) has moved from a niche FinTech term to a core part of AI governance.
The global RegTech market was valued around 12 billion USD in 2023 and is projected to reach almost 86 billion USD by 2032, with an annual growth rate above 23 percent. At the same time, AI-powered RegTech can reduce compliance costs by 30 to 50 percent while improving speed and accuracy. As the IMF’s Tobias Adrian puts it, AI in RegTech and supervisory technology ‘has improved compliance quality and reduced costs,’ but also introduces new risks that require stronger oversight. The challenge is to use RegTech to turn legal intent into operational reality, without outsourcing judgment to the machine.
The Compliance-by-Design Journey for AI Products
For AI-enabled, connected systems, compliance is a design discipline, not a final checkbox. Most organisations follow five recurring steps:
- Law identification: which laws, regulations, standards and guidelines apply to this product, its data flows, and markets?
- Applicability mapping: how do articles and clauses map to specific system components, features, and risks?
- Control derivation: which technical, organisational, and documentation controls implement those obligations?
- Validation and monitoring: are interpretations defensible, risks assessed, and updates tracked over time?
- Evidence and oversight: can we show regulators, auditors, and users what was done, by whom, and why?
These steps are knowledge-intensive but highly structured, which is exactly where RegTech and emerging Regulatory Intelligence (RI) tools can help.
Where RegTech Adds Real Value in 2025
RegTech does not automate compliance in a magical way. What it does well is structure, standardise, and operationalise the compliance workflow. Recent research and industry report findings highlight four major value areas:
- AI-assisted regulation mapping. AI and NLP can be used to scan regulatory texts, cluster similar provisions, and highlight overlaps between, for example, AI, data protection, cybersecurity, and product safety rules.
- Obligation extraction and control structuring. Natural language processing can scan legal texts for obligation phrases (‘the provider shall…’) and group them into themes like transparency, documentation, human oversight, or cybersecurity. Experts then refine these into control sets.
- Workflow and evidence management. Case studies from Gridlines and SymphonyAI show AI-supported platforms tracking owners, evidence, and status of controls, while offering real-time monitoring instead of quarterly audits.
- Continuous regulatory monitoring. 4CRisk.ai’s ‘Horizon Scan’ uses AI agents to track changes across more than 2,500 regulatory sources, creating curated rulebooks and applicability assessments in minutes rather than months.
IRIS Business underlines that AI, blockchain, and structured data standards are now central to RegTech, enabling regulators and firms to move towards real-time reporting, tamper-proof audit trails, and integrated ESG oversight.
Regulatory Intelligence: The Next Layer Above Classical RegTech
In 2025, a new layer has crystallised above traditional RegTech: Regulatory Intelligence (RI). 4CRisk.ai defines RI as the systematic collection, analysis, and dissemination of regulatory information, including the prediction and interpretation of future changes. It goes beyond individual tools like KYC or AML to support:
- proactive compliance and horizon scanning
- market access strategies
- product design aligned with future rules
- multi-jurisdictional policy impact analysis
RI platforms increasingly rely on specialised language models (SLMs) trained on curated regulatory corpora, not generic chatbots. 4CRisk.ai reports that such models and AI agents can deliver key tasks up to 50 times faster than manual methods, including mapping new regulations to internal controls and identifying overlaps or gaps. This shift matters for AI-enabled systems. When the AI Act, CRA, Data Act and national cybersecurity laws are all changing in parallel, RI becomes the layer that keeps design teams informed, early enough to adapt architecture rather than retrofitting controls.
What Can Be Encoded and What Must Stay Human?
A useful way to frame RegTech for AI-enabled products is to distinguish between computable tasks and irreducibly human judgment.
Table 1. Encoding Legal Intent for AI-Enabled Systems Sample Analysis
| Regulatory Domain | What RegTech / Regulatory Intelligence Can Operationalise | What Requires Human Judgment |
|---|---|---|
| AI Governance and Model Transparency | Creating structured documentation for models, tracking data use, maintaining traceability, organising risk-related information | Determining the final risk category, assessing the sufficiency of human oversight, evaluating fairness or bias impacts |
| Data Protection and Privacy | Tracking user permissions, enforcing data-retention rules, mapping personal-data flows, detecting when a privacy assessment may be required | Deciding the lawful basis for processing, interpreting necessity and proportionality, managing sensitive-data scenarios |
| Cybersecurity and Digital Resilience | Identifying known software weaknesses, monitoring updates, recording security incidents, organising evidence for security reviews | Assessing severity of security issues, determining acceptable risk, prioritising security fixes |
| Product Safety and Quality Management | Automating technical documentation, collecting test evidence, monitoring product performance over time | Setting safety thresholds, making ethical trade-offs, defining fail-safe behaviour |
| Data Access, Portability and Interoperability | Logging third-party access, checking system-to-system compatibility, enforcing data-sharing rules in a structured way | Judging fairness of data-sharing conditions, managing competition-sensitive boundaries |
| Consumer Protection and Transparency | Generating user disclosures, creating explanation summaries, detecting potentially harmful interface patterns | Deciding what counts as meaningful transparency, evaluating whether user harm or manipulation is occurring |
| Ethics and Societal Impact | Producing structured ethical-review checklists, generating diversity and inclusion metrics, documenting ethical considerations | Assessing real fairness impacts, judging societal implications, making value-based decisions |
Financial-sector evidence backs this picture. A-Team Insight’s survey of seven AI-powered RegTech newcomers shows:
- Hawk: AI achieving around 90 percent precision in AML transaction alerts, with fewer false positives.
- Lucinity reduces AML investigation workloads by up to 70 percent through 'Human AI' case triage.
- Greenomy automates ESG reporting against EU taxonomies for hundreds of institutions.
The pattern is consistent: data-heavy, repetitive tasks are increasingly computable. Interpretation, proportionality, and ethics remain human.
How Leading Research Frames the Challenge
Academic and policy research is converging on a similar message: the core problem is not writing more laws, but implementing the ones we already have.
- RegTech4AI at Maastricht University explicitly focuses on making the AI Act and GDPR ‘work in practice’ through RegTech methods, not by proposing new legal frameworks.
- The FRIL lab at the University of Strathclyde stresses that explainability in RegTech is essential: automated prompts must show the underlying clause and reasoning to be trusted by auditors and regulators.
- IRIS Business emphasises structured data and standardisation, arguing that real-time regulatory reporting will depend on convergence on common models such as XBRL taxonomies and the Common Domain Model.
- Gridlines and SymphonyAI describe 2025 as the moment when AI in RegTech moves from hype to production, through focused use cases such as continuous KYC, behavioural analytics, and agentic AI modules for quality control and risk insight.
Tobias Adrian at the IMF adds a systemic lens: AI-driven RegTech and supervisory technology can enable ‘closed-loop’ ecosystems where credit decisions, compliance checks, market surveillance, and remedial actions are all mediated by communicating AI systems. That efficiency, he notes, comes with new channels for systemic risk, including cyber-attacks on AI models and homogeneity of risk assessments.
From Legal Text to Live Assurance
We can visualise the role of RegTech and RI in two simple diagrams.
Fig 1.0 The compliance lifecycle illustrates the five core stages that translate legal requirements into operational practice for AI-enabled and connected products. Each stage is iterative and interconnected, ensuring that compliance is continuously embedded into system design, development, deployment, and post-market monitoring.
The first four stages are partially automated through RegTech and RI, the final stage is explicitly marked as ‘Human-led’.
Fig 2.0 This pipeline illustrates how regulatory language is transformed into actionable compliance tasks within AI-enabled systems. A legal clause is first interpreted and converted into an obligation tag, which is then expanded into a structured requirement suitable for engineering teams. RegTech tools can generate automated checks or prompts linked to the relevant documentation, but the final stage human sign-off ensures contextual judgment, proportionality, and accountability remain central to the compliance process.
RI engines sit at the ‘obligation tag’ and ‘structured requirement’ stages, RegTech platforms implement checks and evidence management. Technology structures and accelerates the work, but human sign-off remains the anchor.
A Concrete Example: Encoding a Legal Clause into a Machine-Readable Check
To illustrate how legal intent becomes computable, consider a well-known requirement from AI governance frameworks: high-risk AI systems must maintain automatic logging that is ‘sufficiently detailed’ to ensure post-hoc traceability. A RegTech or RI system would typically break this down as follows:
- Legal Clause : High-risk AI systems shall be designed so that their operation is automatically logged, to the extent that it is technically feasible and appropriate to the intended purpose.
- Obligation Tag: AI_HR_Logging_Traceability
- Structured Requirement: System must generate immutable logs recording: input received, timestamp, model version, decision output, exception states, and human-override triggers.
- Automated Check: Log file exists
Log entries contain mandatory fields
Log retention policy ≥ required minimum
Logs are cryptographically verifiable
Alerts created if fields missing - Linked Evidence: Test logs, DevOps pipeline outputs, SBOM entries, audit trails, architecture diagrams.
- Human Sign-off: A senior engineer or risk owner validates whether the level of detail is ‘sufficiently appropriate’ for the specific system context.
This example shows how RegTech tools structure and accelerate work without removing the expert judgment needed to interpret proportionality and purpose.
What Must Remain Human
Despite impressive 2025 capabilities, some tasks cannot be safely automated:
- interpreting legal purpose and proportionality
- setting risk appetite and ethical baselines
- assessing fairness and societal impact
- deciding when to override automated outcomes
- designing organisational accountability
The IMF cautions that AI in RegTech and SupTech introduces new privacy, bias, and cyber risks, including poisoning and evasion attacks on AI models and leakage of sensitive data from training sets. IMF These are precisely the areas where human governance, second line challenge, and board-level oversight must remain strong.
Practical Implementation Challenges
Despite the rapid maturity of RegTech and RI capabilities, organisations consistently encounter three friction points:
- Legacy System Integration: Compliance-by-design works best when system artefacts (logs, tests, controls, SBOMs) are accessible through APIs. Many older systems lack the interfaces needed to feed machine-readable compliance data, requiring bridging layers or partial manual uploads.
- Data Quality and Inconsistency: RegTech automation is only as strong as the underlying configuration data: inconsistent naming conventions, undocumented architectural changes, or missing logs can trigger false alerts or incomplete mappings.
- Cultural Resistance in Compliance Teams: Some teams fear automation will replace judgment. In practice, the opposite is true RegTech reduces administrative burden but still requires expert interpretation. Successful adoption often depends on framing RegTech as augmentation rather than replacement.
Highlighting these challenges adds realism without weakening the central message: that RegTech is essential, but must be adopted thoughtfully.
Path Forward for AI-Driven Product Organisations
The long-term vision is not ‘law as code’, but law with code: legal principles expressed in ways that machines can help manage, and humans can still fully understand. Following are the key points for organizations having AI enabled systems:
- Build internal maps that link legal intent → design principles → controls → evidence for every AI-enabled feature or system component.
- Use structured templates and audit logs that document why a requirement applies, not just the requirement itself.
- Pilot AI-supported tools for horizon scanning, regulatory change monitoring, AI Act documentation support, ESG reporting, vulnerability tracking, and evidence completeness checks.
- Integrate RegTech and Regulatory Intelligence capabilities directly into engineering backlogs, QA workflows, and design reviews, instead of keeping them in a compliance silo.
- Connect live system data events, logs, SBOMs, test results, incidents — into a structured regulatory dashboard that maintains traceability.
- Move toward semantic compliance layers that allow regulators, auditors, and internal risk teams to access near real-time, machine-readable assurance.
Applicability for SMEs and Scale-Ups
Although many RegTech case studies come from large financial institutions, the underlying principles are increasingly accessible to small and mid-sized AI product companies. Three pragmatic entry points are emerging:
- Lightweight Open-Source Tooling: SMEs can use open-source solutions for obligation tagging, SBOM generation, vulnerability scanning, and log validation without enterprise infrastructure.
- Modular Cloud-Based RegTech Services: Many vendors now offer narrow, API-first services (e.g. horizon scanning, evidence completeness checks) that do not require a full GRC ecosystem.
- Shared Taxonomies and Templates: Regulatory taxonomies created by research groups (e.g., RegTech4AI) or standards bodies provide ready-made structures that allow smaller organisations to operationalise compliance without legal heavy lifting.
This lowers the barrier for smaller innovators, enabling compliance-by-design without large teams or budgets.
Conclusion
RegTech for AI-enabled systems is no longer an abstract idea. It is a rapidly maturing ecosystem of tools, standards, and research programmes aimed at one core problem: making complex law workable in real products. The evidence from 2025 is clear:
- AI-powered RegTech can cut costs and false positives while improving detection and documentation.
- Regulatory Intelligence is emerging as a distinct layer that helps organisations stay ahead of change, not just react to it.
- Supervisory authorities are adopting AI as well, which makes transparency, explainability, and robust governance non-negotiable.
Used wisely, RegTech does not replace human experts. It gives them structured, evidence-ready systems that reflect the law’s intent and make that intent auditable. For AI-enabled systems that depend on trust, this shift from ‘compliance as burden’ to ‘compliance as assurance’ may become one of the most important competitive advantages of the decade.
