How technology can strengthen accountability and trust in connected products
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.
For AI-enabled, connected systems, compliance is a design discipline, not a final checkbox. Most organisations follow five recurring steps:
These steps are knowledge-intensive but highly structured, which is exactly where RegTech and emerging Regulatory Intelligence (RI) tools can help.
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:
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.
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:
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.
A useful way to frame RegTech for AI-enabled products is to distinguish between computable tasks and irreducibly human judgment.
| 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:
The pattern is consistent: data-heavy, repetitive tasks are increasingly computable. Interpretation, proportionality, and ethics remain human.
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.
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.
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.
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:
This example shows how RegTech tools structure and accelerate work without removing the expert judgment needed to interpret proportionality and purpose.
Despite impressive 2025 capabilities, some tasks cannot be safely automated:
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.
Despite the rapid maturity of RegTech and RI capabilities, organisations consistently encounter three friction points:
Highlighting these challenges adds realism without weakening the central message: that RegTech is essential, but must be adopted thoughtfully.
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:
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:
This lowers the barrier for smaller innovators, enabling compliance-by-design without large teams or budgets.
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:
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.