Design Challenges for GDPR RegTech
Paul Ryan
, Martin Crane
and Rob Brennan
Uniphar PLC, ADAPT Centre, School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland
Keywords: GDPR, Compliance, Accountability, Data Protection Officer, RegTech.
Abstract: The Accountability Principle of the GDPR requires that an organisation can demonstrate compliance with the
regulations. A survey of GDPR compliance software solutions shows significant gaps in their ability to
demonstrate compliance. In contrast, RegTech has recently brought great success to financial compliance,
resulting in reduced risk, cost saving and enhanced financial regulatory compliance. It is shown that many
GDPR solutions lack interoperability features such as standard APIs, meta-data or reports and they are not
supported by published methodologies or evidence to support their validity or even utility. A proof of concept
prototype was explored using a regulator based self-assessment checklist to establish if RegTech best practice
could improve the demonstration of GDPR compliance. The application of a RegTech approach provides
opportunities for demonstrable and validated GDPR compliance, notwithstanding the risk reductions and cost
savings that RegTech can deliver. This paper demonstrates a RegTech approach to GDPR compliance can
facilitate an organisation meeting its accountability obligations.
In May 2018, the European Union (EU) introduced
the GDPR. This regulation brought a high level of
protection for data subjects, but also a high level of
accountability for organisations (Buttarelli 2016).
The GDPR principle of accountability requires that a
data controller must be able to demonstrate their
compliance with the regulation (GDPR Recital 74).
This requires an organisation “to act in a responsible
manner, to implement appropriate actions, to explain
and justify actions, provide assurance and confidence
to internal and external stakeholders that the
organisation is doing the right thing and to remedy
failures to act properly” (Felici, 2013).
Organisations can be complex entities,
performing heterogeneous processing on large
volumes of diverse personal data, potentially using
outsourced partners or subsidiaries in distributed
geographical locations and jurisdictions. A challenge
to complying with the accountability principle of the
GDPR for organisations is demonstrating that these
complex activities and structures are meeting their
regulatory obligations. The organisation must
implement appropriate policies, procedures, tools and
mechanisms to support their accountability practices
(Felici, 2013).
Many organisations appoint a Data Protection
Officer (DPO) to assist in this process. Bamberger
describes the role as “the most important regulatory
choice for institutionalising data protection”
(Bamberger, 2015). In practice the DPO is the early
warning indicator of adverse events when processing
personal data within the organisation (Drewer, 2018).
The DPO must have “professional qualities and, in
particular, expert knowledge of data protection law
and practices” (GDPR Art 37). This challenging role
requires the DPO to monitor compliance and advise
the organisation accordingly. The DPO acts
independently of the organisation to assess and
monitor the consistent application of the GDPR
regulation and to ensure that the rights and freedoms
of data subjects are not compromised (Article 8, EU
charter). The role of DPO encompasses a dynamic
motion of policy generation, staff training, business
process mapping and review, compliance record
keeping, audit, data protection impact assessments,
and compliance consultations (Drewer, 2018). The
constant pace of business change allied with evolving
Ryan, P., Crane, M. and Brennan, R.
Design Challenges for GDPR RegTech.
DOI: 10.5220/0009464507870795
In Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020) - Volume 2, pages 787-795
ISBN: 978-989-758-423-7
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
legal interpretations require constant vigilance on the
part of the DPO and create additional challenges for
accountability. Fundamentally, it is the organisation,
and not the DPO, that must be able to demonstrate
that it is meeting the threshold that is the
accountability principle.
There are many solutions available to DPOs and
organisations to help meet this challenge of
demonstrating compliance to the accountability
principle. This paper will evaluate the range of
available tools, such as: privacy software solutions
from private enterprise vendors, maturity models and
regulator self- assessment tools. Despite the many
GDPR compliance tools available, this paper will
highlight that the majority fail to meet the
accountability principle. Most are not supported by
published methodologies or evidence for their
validity or even utility. They lack the ability to
integrate or be integrated with other tools and the
level of automation and innovation in this space has
also been limited.
In contrast, RegTech has emerged as a framework
for automating regulatory compliance in the Financial
Industry. The “Global Financial Crisis (GFC)” of
2008 prompted financial regulators to introduce new
compliance regulations (Johansson, 2019), resulting
in significant compliance challenges and compliance
costs for organisations due to the complexity of these
regulations. Strong data governance and mapping
regulatory compliance provisions into software code
(Bamberger, 2009) to facilitate regulatory
compliance has been enabled by developments such
as process automation, the digitising of data, the use
of semantic methods and machine learning
algorithms. RegTech uses such tools to efficiently
deliver compliance and risk reports in integrated
toolchains. The evolution of RegTech has shown that
information technology can be used to support
automated or semi-automated regulatory monitoring
and reporting of compliance (Arner, 2017).
This paper proposes challenges for realising a
RegTech approach to GDPR compliance whereby
organisations leverage modern information
technology to improve the organisational and external
visibility of their GDPR compliance level. This
approach requires automated data collection from
relevant sources throughout the organisation and
monitoring via GDPR compliance evaluation
functions that could provide interoperable and
machine-readable compliance metrics or reports for
the organisation, suggested compliance actions and
root cause analysis of compliance issues, using
agreed data quality standards such as ISO8000.
The role of monitoring, analysing and reporting
the GDPR compliance status in an organisation is the
task of the DPO. A RegTech approach to GDPR
compliance could provide the DPO with the ability to
track organisational compliance progress, identify
areas of compliance weakness and benchmark their
performance against other organisations. This would
greatly enhance an organisation’s ability to
demonstrate and improve compliance and thus meet
the GDPR accountability requirement.
Section 2 will discuss the accountability principle
and what it means in practice to an organisation and
the challenges they face to meet the accountability
principle. The role of the DPO, and their part in
compliance will be discussed in detail from the
perspective of a practising DPO. Section 3 reviews
the current approaches to GDPR compliance and
critiques the many available offerings such as private
enterprise software solutions, maturity models and
self-assessment checklists. Section 4 examines the
financial Industry to see how RegTech is enhancing
compliance using data driven solutions. Section 5
describes the challenges that must be faced in
developing the next generation of GDPR compliance
tools based on RegTech and documents the
requirements that a DPO would require in such tools.
Section 6 will introduce a proof of concept where a
Data Protection Regulators self- assessment checklist
has been utilised based on RegTech best practice, to
provide a simple efficient method to demonstrate
GDPR compliance and meet the requirements of the
accountability principle.
In this section, this paper will discuss what the
accountability principle of the GDPR means to
organisations. The paper will look at the challenges
that organisations are facing with demonstrating that
they are meeting these obligations and it will discuss
the role of the DPO in this process.
The Anglo-Saxon word “Accountability” has a
broadly understood meaning of how responsibility is
exercised and how it is made verifiable (Article 29
Working Party, 2010). Accountability can be viewed
to be an expression of how an organisation displays
“a sense of responsibility—a willingness to act in a
transparent, fair and equitable way” (Boven’s, 2007)
and “the obligation to explain and justify conduct’
(Boven’s, 2007). The GDPR accountability principle
requires a data controller “implement appropriate and
ICEIS 2020 - 22nd International Conference on Enterprise Information Systems
effective measures to put into effect the principles and
obligations of the GDPR and demonstrate on request”
(Article 29 Working Party, 2010). In 2018 the Centre
for Information Policy Leadership (CIPL) developed
accountability-based data privacy and governance
programs to encompass the key elements of
accountability as described in Fig 1.
Figure 1: The Accountability Wheel Universal Elements
of Accountability (CIPL, 2018).
In practice, this can be viewed as “setting privacy
protection goals based on criteria established in law,
self-regulation and best practices and vesting the
organisation with the responsibility to determine
appropriate, effective measures to reach these goals”
(CIPL, 2018). This is quite a challenging task for a
data controller when you are dealing with a
substantial legal text like the GDPR. There is a “lack
of awareness of their obligations and duties in relation
to personal data protection, it is urgent to define a
methodology to be able to comply with the GDPR”
(Da Conceicao Freitas, 2018).
In theory, the GDPR provides for certification
methods in article 42 and 43 of the GDPR to assist a
controller in demonstrating compliance. However, in
practice this has proven to be a challenge for
organisations as the European Union has not
approved any Certification body to certify
compliance (Lachaud 2016). In fact, there are views
being expressed that the GDPR certification process
cannot be successful. (Lachaud,2016).
Many organisations appoint a DPO to assist with
their GDPR compliance, however it is important to
note that the demonstration of compliance obligations
ultimately rests with the controller (organisation) and
not the DPO. The role of DPO within the
organisation covers a wide range of tasks as
prescribed in Article 39 of the GDPR. The main tasks
are to monitor, inform and advise the controller or
processor regarding compliance with the GDPR, to
provide advice such as data protection impact
assessments, to provide training and awareness
raising and to co-operate with and act as a contact
point for the supervisory authority.
The role of DPO requires a broad set of skills in
GDPR legal compliance, and a detailed knowledge of
business processes (Drewer,2018). The DPO works
with numerous stakeholders such as data subjects,
employees, processors and regulators and provides
consultancy and guidance on business processes. The
role involves a broad spectrum of activities from
maintaining a register of processing activities to
dealing with data breaches, to completing data
protection impact assessments. The DPO must have
visibility of all activities and monitor and report
compliance to the highest level in the organisation
(see Fig.2). The DPO is in essence “privacy on the
ground” (Heimes, 2016), in that the DPO is the early
warning system for GDPR compliance within the
organisation (Drewer, 2018). The challenge for the
DPO is how to demonstrate that the organisation is
accountable and can demonstrate GDPR compliance.
Main contact
with regulator
Data Processors
Data Protection
International Data
Cloud storage Data Breach
Business Process:
Risk impact assessments
Privacy by design
Register of
Policies and
Advise &
Figure 2: The breadth and complexity of the role of Data
Protection Officer (Source Author).
This section discusses the broad range of tools and
methods that are available to DPO’s to demonstrate
the GDPR compliance of their organisation.
Design Challenges for GDPR RegTech
3.1 Private Enterprise Software
There has been a call for tools and methods to assist
organisations in meeting their GDPR compliance
obligations (Piras, 2019). This is being met by large
financial investments by venture capital companies
with over $500 million invested in privacy related
start-ups around the world in 2017 (IAPP, 2019)
There are over 263 vendors offering privacy software
tools to organisations (IAPP, 2019). These software
solutions come in many forms ranging from simple
questionnaires and templates to solutions that focus
on individual aspects of compliance for GDPR such
as website scanning for use of cookies. The main
categories of these privacy tools are as follows (IAPP,
Activity Management control and monitor
access to personal data
Assessment Managers - automate different
functions of a privacy program, locating risk gaps,
demonstrating compliance
Consent managers - help organizations collect,
track, demonstrate and manage users’ consent.
Data discovery determine and identify personal
data held
Data mapping solutions - determine data flows
throughout the enterprise.
De-identification pseudonymisation tools
Secure Internal Enterprise communications
Data Breach Incident response solutions
Privacy information managers - provide latest
privacy laws around the world.
Website scanning catalogue cookies
Table 1: Privacy software tools, number of vendors per
category (IAPP 2019).
Privacy Product Category
No. of Vendors
offering this
Activity Monitoring
Assessment Manager
Consent Manager
Data Discovery
Data Mapping
De Identification/Pseudonymity
Enterprise Communications
Incident Response
Privacy Information Manager
Website Scanning
Whilst there are a variety of privacy software
solutions being offered by vendors, as displayed in
Table 1 “there is no single vendor that will
automatically make an organization GDPR
compliant'' (IAPP 2018). In fact, most solutions on
offer from private enterprise cover 3 or less
categories, see Figure 3.
Figure 3: No. of privacy product categories offered by no.
of vendors.
An accountability framework requires a
comprehensive approach to compliance across the
organisation. Whilst these software solutions go some
way towards the demonstration of compliance, the
author has identified several weaknesses in these
private enterprise software solutions, as follows:
They are not supported by published
methodologies or evidence to support their
validity or even utility
Many of these solutions are stand - alone in that
they lack inter-operability with other GDPR
compliance systems and hence cannot easily be
assembled into toolchains providing
comprehensive compliance reports and metrics,
quality improvement processes or data analytics
such as root cause analysis
They focus on manual or semi-automated
assessment approaches that are labour intensive,
rely on domain experts and are not driven by
quantitative operational data that is increasingly
being generated by organisations
They are created by private enterprise and are
based on an interpretation of the regulation,
rather than being developed with the input of the
These solutions offer a starting point for GDPR
compliance for an organisation however the lack of
academic rigour or formal regulatory input and the
inability to connect and build tool chains inhibits
these solutions. The use of data driven inputs from
heterogeneous sources and the mapping of business
processes using agreed semantic standards would
improve inputs to the evaluation tool. This would
remove subjectivity and improve the quality of the
outputs. GDPR compliance software must avoid the
1 2 3 4 5 6 7 8 9 10
ICEIS 2020 - 22nd International Conference on Enterprise Information Systems
“pitfalls of a fragmented Tower of Babel approach.”
(Butler, 2018). The best of breed software point
solution products could be used to feed a global
evaluation tool to optimise and organise the outputs
using agreed semantics.
3.2 Maturity / Capability Models
Capability Maturity Models have been used for
compliance monitoring for many years (Humphrey,
2002). The American Institute of Certified Public
Accountants privacy maturity model (AICPA, 2011)
was used to gain an understanding of an
organisation’s privacy compliance standing. It used
a set of questions referred to as “generally accepted
privacy principles” in the form of 73 measurable
criteria. It gauged compliance along an axis of five
maturity levels from ad hoc to optimized. The
drawbacks of this methodology as a measure of
compliance to the GDPR are that it predates the
GDPR and would therefore need updating to reflect
the new regulation. The more recent IAPP Maturity
Framework (2019) develops a series of checklists
built through “collaboration between a team of highly
experienced privacy and security professionals,
lawyers and regulators.” Both solutions provide
visualisations of compliance on an axis and are an
indicative measure of compliance. However, they do
have a number of drawbacks as follows:
They are labour intensive and dependant on
highly skilled labour/domain experts
They are prone to human subjectivity, bias and
They are infrequently updated
The measures chosen utilise a selection of
questions and checklists that require academic
They are not suitable as part of an automated
process and quality improvement toolchain
Whilst these maturity models are indicative of an
organisations GDPR compliance position, the
limitations outlined prevent these tools from
developing any further without automation. Once
automated, the lack of reporting and interoperability
standards mentioned in the last section become
3.3 Self-assessment Checklists from
Regulatory Authorities
Several data protection supervisory authorities have
provided self-assessment checklists and
accountability toolkits to assist organisations to
prepare for GDPR. These come in the form of a series
of questions and check-lists and are designed to assist
the organisation in checking their compliance level.
These toolkits are devised to provide broad coverage
of all the principles of the GDPR. Just like maturity
models these checklists provide an overview of
compliance, however the main drawbacks of these
tools for GDPR compliance are that they are
fundamentally high-level self-assessments tools and
are generic by nature and lack depth. Like maturity
models, they rely on qualitative input of users, and
they lack input or output interoperability with other
solutions. However, the key benefit of these
checklists and toolkits are that they have been
developed by regulators, unlike maturity models and
private enterprise software solutions, which have
been developed independently.
In this section this paper will look at the emergence
of RegTech as a solution to compliance challenges in
the financial industry. RegTech can be defined as “the
use of technological solutions to facilitate compliance
with, and monitoring of regulatory requirements”
(Colaert, 2017). The financial crisis of 2008 brought
about a significant increase in new compliance
legislation. (Butler, 2019). The emergence of
RegTech came about for the following reasons
(Arner, 2017):
Enhanced compliance requirements
Developments in data science and Artificial
Cost of compliance
Regulators efforts to enhance the efficiency
of supervisory tools
The key drivers for the RegTech technological
solutions have been to make compliance reporting
simple, easy and efficient and to reduce the risks of
individual errors or liability and to build automated
systems to facilitate legal compliance. RegTech has
the potential to enable organisations to use business
data to enhance better decision making and quickly
identify non- compliances (Butler, 2019).
When we look at RegTech solutions we see
compliance technology software spanning a wide
breadth from its simplest form such as automated
reporting or dashboard views to complex tools for
carrying out specific regulatory functions (Colaert,
2017). Some examples of RegTech solutions are in
Design Challenges for GDPR RegTech
the area of anti-money laundering where large
financial deposits can be automatically detected and
reported to a compliance officer, thus reducing the
risk of human error in the form of an inattentive staff
member. Similarly, Markets in Financial Instruments
(MiFID) tests, help organisations to determine what
level of investment advice must be given to a
customer based on the results of an automatically
processed questionnaire (Colaert, 2017). Again, this
solution helps an organisation to reduce errors and
meet its legal obligations through process automation.
These solutions remove the need for human
intervention and make compliance less complex.
RegTech tools are being used to leverage data from
existing operational information systems and seek to
provide agile solutions to improve compliance
visibility, through the automation of mundane
compliance tasks and reduce risk to the organisation
(Colaert, 2017).
The foundation of compliance has been to prevent
identify, respond to and remedy risk. (Deloitte,
2016). RegTech solutions are being developed to
meet these regulatory requirements, but also to
modernise compliance and generate a measurable
value proposition to the organisation. RegTech
solutions enhance the basics of compliance through
enhanced data integration, the use of automation,
predictive analytics and strategic process alignment
(Deloitte, 2016).
The role of the supervisory authority has arguably
been transformed by RegTech (Arner, 2017). The
regulator not only has access to periodic or real-time,
fine-grained compliance reports, and the incremental
improvements in compliance but they are promoting
the design of a regulatory framework able to
dynamically adapt to new rules and regulations
(Arner, 2017).
The contrast between the innovation in this space
and the GDPR compliance tools discussed above
suggests that the use of a RegTech approach applied
to the GDPR would yield significant benefits to
DPO’s, organisations and regulators. It may even
side-step the crisis in GDPR certification schemes by
providing automated transparent accountability that
regulators can query and analyse without recourse to
a slow third-party certification service. This blend of
technology can yield significant benefits for
organisations (Arner, 2017).
In this section, this paper takes the learning from
RegTech as described in section four and proposes a
RegTech approach to GDPR compliance. This design
takes it’s learning from RegTech where common
design protocols and agreed semantic standards
(Butler, 2019) are used to integrate new heterogenous
tools to provide the organisation with the necessary
information to monitor, evaluate and report
compliance (See Fig. 4). This approach allows for
new tools to be integrated seamlessly. The RegTech
approach to compliance seeks to automate data inputs
to reduce human errors and remove subjectivity. The
use of common standards, protocols and semantics
facilitates a flexible, nimble and agile and cost-
effective approach to compliance. The next
generation of GDPR compliance tools need to
consider a RegTech approach to meet their
accountability obligations.
Figure 4: A RegTech approach to GDPR compliance
(Source Author).
In this section we describe a prototype GDPR high-
level evaluation tool that has been developed based
upon the developments in RegTech, outlined in
section 5. The tool is an open-source high level
GDPR compliance evaluation methodology that has
been based on a self-assessment checklist created by
a data protection regulator. It has been developed to
measure the GDPR compliance level in an
organisation. The evaluation tool was developed from
the Irish Data Protection Commission self-
assessment checklist which segmented the GDPR
into 8 regulatory sections and posed 54 questions in
Monitor and Evaluate and Report
Data Processing activities
GDPR Tools
Ease of
integration for
new data
Cost effective
agile and flexible
ICEIS 2020 - 22nd International Conference on Enterprise Information Systems
total. The tool is designed to be a layered information
delivery system that provides information and
insights so that the DPO can measure, monitor and
manage business performance more effectively, and
address accordingly (Eckerson, 2010).
The evaluation tool provides three layers of data as
displayed in Fig. 5. The top level being a graphical
overview of compliance for monitoring and reporting
purposes, the second layer being the dimensional data
that provides a view of each aspect of the GDPR and
the final layer that being the detail of each GDPR
compliance area.
Figure 5: GDPR Evaluation dashboard overview
(Eckerson, 2010).
It is planned that the tool will use the W3C
Community group’s data protection vocabulary
(Pandit, 2019) to describe the context using explicit
semantics and the W3C Data Cube vocabulary to
represent the time series of measurements across the
different GDPR aspects or dimensions (Cyganiak,
2014). This development involved taking the self-
assessment checklist and transforming it into an
evaluation tool which was populated by a sample
organisation each month for six months in total. The
overall GDPR compliance monthly score for the
organisation for each month is displayed in figure 6.
This information gives the DPO a high-level view of
compliance for the organisation.
Figure 6: Compliance Trend for Sample organisation.
The results from the evaluation tool can be also
viewed by GDPR regulatory section to analyse how
the organisation is performing in the various aspects
of GDPR compliance, thus providing enhanced
visibility to the DPO. In table 2 the organisation is
fully compliant in accuracy and retention but is only
50% compliant regarding data breaches. The data can
be examined to another sub-level to provide the detail
by GDPR aspect. Table 2 breaks out the Data Breach
aspect and provides the granularity that a DPO needs
to provide feedback to the controller to drive actions
and improve the compliance of the organisation.
Table 2: Compliance score per regulatory area.
In the sample organisation the DPO can identify
the non-compliant areas as identified in table 3 and
take the necessary actions to resolve.
Table 3: Non-compliance results for Data Breach.
Areas of
This approach has demonstrated the use of a
RegTech approach to GDPR compliance using a
simple cost-effective method. It has utilised
questions that have been created by regulatory
authorities themselves so they could serve as a strong
platform for the assessment of compliance. The
evaluation tool meets the requirement of being
comprehensive in that it covers the breadth of the
GDPR and is informative in that it provides specific
scores into GDPR areas requiring focus. The
evaluation process is repeatable in that it can be run
at intervals to generate compliance trends. The results
Analyse GDPR
Drill down to detail of
each compliance aspect
Design Challenges for GDPR RegTech
yielded specific and relevant scores that can be used
to drive corrective actions. The use of data driven
inputs from heterogeneous sources and the mapping
of business processes into the evaluation tool using
agreed semantic standards would remove qualitative
user inputs and would improve inputs to the
evaluation tool. This would remove subjectivity and
improve the quality of the outputs.
Organisations are accountable for the demonstration
of their compliance with the GDPR regulation. We
have seen that the available compliance tools go some
way to achieving this goal, but each have their
shortcomings. A RegTech approach to GDPR
compliance has shown that the use of technology to
improve compliance monitoring and reporting can be
achieved when flexible, agile, cost effective,
extensible and informative tools are combined. The
opportunities to further develop GDPR compliance
tools exists if agreed semantic standards (Butler,
2019) are developed to automate processes and
remove subjectivity from data inputs. We conducted
a proof of concept to demonstrate the application of
some of these RegTech approaches to GDPR
Compliance. A GDPR compliance tool was
developed to monitor and analyse organisational
compliance that yielded a GDPR compliance output
for an organisation. The compliance report that was
generated from the evaluation tool can be used to
identify GDPR areas where the organisation is not
compliant, to trend their progress towards GDPR
compliance over time and to benchmark performance
versus other organisations. The DPO can use the
results to direct resources to areas of non-compliance
and improve their score, thus reducing the risk of
GDPR fines. We have shown that a RegTech
approach to GDPR can enable an organisation to meet
its obligations to comply with the accountability
This work is partially supported by Uniphar PLC.,
and the ADAPT Centre for Digital Content
Technology which is funded under the SFI Research
Centres Programme (Grant 13/RC/2106) and is co-
funded under the European Regional Development
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