The American Journal of Engineering and Technology
85
https://www.theamericanjournals.com/index.php/tajet
TYPE
Original Research
PAGE NO.
85-91
10.37547/tajet/Volume07Issue08-10
OPEN ACCESS
SUBMITED
24 July 2025
ACCEPTED
27 July 2025
PUBLISHED
12 August 2025
VOLUME
Vol.07 Issue 08 2025
CITATION
Yurii Shevchuk. (2025). Risk Management and Compliance Strategies for
Legacy IT Infrastructure. The American Journal of Engineering and
Technology, 7(8), 85
–
91.
https://doi.org/10.37547/tajet/Volume07Issue08-10
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Risk Management and
Compliance Strategies for
Legacy IT Infrastructure
Yurii Shevchuk
Senior Full Stack Engineer and Cybersecurity Specialist, DataArt
New York, United State
Abstract:
This article examines strategies for risk
management and regulatory compliance in the context
of modernizing legacy IT infrastructure. The relevance of
the topic arises from the growing need to integrate new
technological solutions in environments characterized
by limited flexibility, high maintenance costs, and
technical debt
—
factors that contribute to operational,
technical, and regulatory risks. The study analyzes the
current state of legacy infrastructure, identifies key
threats
and
vulnerabilities,
and
develops
methodological
approaches
including
modular
migration, AI-driven analytics, the adoption of cloud
technologies, and the integration of advanced security
measures to ensure compliance with regulatory
frameworks such as GDPR and HIPAA.
The scientific novelty lies in proposing a new perspective
on integrating strategic planning, change management,
and modern security technologies to reduce operational
costs and improve business resilience. This perspective
emerged through a critical review of existing literature.
The author’s hypothesis suggests that a comprehensive
approach to modernizing legacy IT infrastructure
—
grounded in modular transition to cloud solutions and
automated security monitoring
—
will lead to a reduction
in risk and an increase in the efficiency of business
processes.
The findings of this study may be of interest to IT
management professionals, risk and compliance
officers, and researchers seeking to integrate the latest
analytical
methods
into
the
evaluation
and
modernization of inherited IT systems. The presented
material is also expected to be useful to other scholars
developing theoretical models for managing complex IT
environments, as well as to practitioners implementing
strategies for mitigating operational and regulatory risks
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amid continuous technological and legal transformation.
Keywords:
legacy IT infrastructure, risk management,
regulatory compliance, cloud technologies, AI-driven
analytics, modular migration, information security.
Introduction
The relevance of modernizing legacy IT infrastructure is
driven by the rapid development of digital technologies,
with the global digital transformation market having
grown from $469.8 billion in 2020 to $1,009.8 billion by
early 2025, reflecting an average annual growth rate of
16.5% [11]. Challenges related to technical debt, high
maintenance costs of outdated systems, and the
difficulty of integrating them with modern technologies
necessitate the development of effective risk
management and compliance strategies [1,2].
Contemporary literature on risk management and
compliance strategies for IT infrastructure is
characterized by a diversity of approaches that combine
issues of technical modernization, data governance, and
economic optimization. A number of studies, including
works by Adepoju A. H. et al. [1], Kambala G. [2],
Ponnusamy S., Eswararaj D. [9], and Eeti S., Renuka A.
[10], propose comprehensive frameworks for migrating
legacy systems to new architectural models, placing
particular emphasis on seamless integration, scalability,
and, as a result, the reduction of operational and
information risks. This group of authors demonstrates
that transitioning to cloud solutions and modern data
models not only upgrades infrastructure but also creates
mechanisms capable of meeting current security and
regulatory compliance requirements.
Another research focus centers on developing strategies
for data governance and quality. In this area, Adepoju A.
H. et al. [3] propose a model aimed at reducing data
redundancy and improving quality, directly impacting
the mitigation of risks related to data breaches and
errors. The study by Mishra S., Komandla V., and Bandi
S. [7] introduces new paradigms in big data
management through the concepts of Data Fabric and
Data Mesh, which support more efficient data
integration into corporate processes. Similarly,
Machireddy J. R., Rachakatla S. K., and Ravichandran P.
[8] justify the use of artificial intelligence and machine
learning for building analytical systems that enable data-
driven strategy development while maintaining
compliance.
Equally significant is the technology-driven approach to
IT environment transformation. Khang A. and Sivaraman
K. A. [4] examine tools and applications in the fields of
big data, cloud computing, and the Internet of Things
(IoT), illustrating how these technologies support the
modernization of legacy systems and the creation of
more flexible infrastructure. Likewise, Onoja J. P., Ajala
O. A., and Ige A. B. [5] explore the potential of artificial
intelligence to transform community engagement
processes. Although their focus is on social
development, their work suggests that such innovative
approaches can be adapted to enhance compliance and
risk management within IT systems.
Finally, the economic aspects of modernization are
examined in the work of Abbey A. B. N. et al. [6], where
economic frameworks are developed to optimize
procurement strategies in both public and private
sectors. The approach, based on the optimization of
resource allocation, aims to reduce financial and
operational risks associated with the transition from
legacy systems to modern IT solutions.
Statistical data illustrating the dynamics of digital
transformation are provided in source [11], published on
the enterpriseappstoday website. Sources [11, 12],
whose information is published on the websites: netwrix
and businessstudio, were used in analyzing the ways of
calculating the effectiveness of using risk management
strategies in ensuring compliance for legacy IT
infrastructure.
A review of the presented works reveals inconsistencies
in how the prioritization of different aspects of risk
management is evaluated. Some authors emphasize
technological modernization and the development of
migration frameworks, while others focus on data
governance and analytical solutions. At the same time,
the economic dimension remains insufficiently
integrated with technical aspects, pointing to the need
for interdisciplinary approaches in the development of
comprehensive strategies. Furthermore, issues related
to information security, regulatory compliance, and the
integration of compliance requirements into migration
processes remain underexplored, despite their critical
importance
in
contemporary
IT
infrastructure
management.
The aim of the study is to justify strategies for risk
management and regulatory compliance in the
modernization of legacy IT infrastructure.
The scientific novelty lies in proposing a new perspective
on integrating strategic planning, change management,
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and modern security technologies to reduce operational
costs and improve business resilience, as identified
through literature analysis.
The author’s hypothesis suggests that implementing a
comprehensive strategy
—
based on modular transition
to cloud solutions, systematic risk management, and
strict adherence to regulatory standards
—
can reduce
operational costs and enhance business resilience even
during significant technological renewal of legacy IT
infrastructure.
The research applies a methodology that includes
qualitative analysis of academic and professional
literature as well as the use of analytical tools to
evaluate the effectiveness of the proposed measures.
1. Analysis of the condition of legacy IT infrastructure
and its associated risks
Modern organizations operating on legacy IT systems
face a range of technical, operational, and regulatory
challenges resulting from technical debt, limited
flexibility, and difficulties integrating such systems with
modern technologies. These systems are often built on
outdated programming languages and platforms (e.g.,
COBOL, mainframes) and are characterized by low
scalability, high maintenance costs, and limited capacity
for functional upgrades [1,3]. Their rigidity makes
adaptation to current business processes problematic,
leading to reduced operational efficiency and increased
business risk.
Legacy systems are frequently the result of years of
accumulated functionality layered onto outdated
architectural foundations. As a result, they are defined
by the following characteristics:
Technical debt and limited flexibility.
Systems
developed decades ago typically have rigid and complex
architectures, making adaptation to evolving business
requirements difficult [1].
Integration challenges.
The absence of modern
interfaces and data exchange standards creates barriers
to integrating with newer applications, resulting in
fragmentation of the IT environment.
High maintenance costs.
Legacy systems demand
significant financial and human resources to remain
operational, as component replacement or architectural
overhaul is often expensive and resource-intensive [2].
To better understand how these risks affect legacy IT
infrastructure, Table 1 outlines the key risk indicators.
Table 1. Key Risk Indicators for Legacy IT Infrastructure [1]
Indicator
Description
Business impact
Technical debt
Accumulated technological obsolescence
limiting scalability and upgrades
Reduced performance, increased
downtime
Limited
flexibility
Inflexible architecture hindering the
implementation of new features and
integration
Delays in innovation, inability to respond
quickly to market changes
Integration
issues
Lack of modern APIs and data exchange
standards
Fragmented IT environment, difficulties
in system-to-system data synchronization
High
maintenance
costs
Significant expenditures to support and
update outdated systems
Decreased profitability, reallocation of
resources from growth to maintenance
Information
security risks
Insufficient data protection, lack of
modern encryption and access control
Increased vulnerability to cyberattacks,
risk of confidential data breaches
Regulatory non-
compliance risk
Inability to update systems in line with
evolving standards and regulations
Legal penalties, fines, reputational
damage
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In turn, in order to calculate the annual expected
damage before and after the implementation of risk
management measures, the following formula should be
used:
𝐴𝐿𝐸 = 𝑆𝐿𝐸 × 𝐴𝑅𝑂 (1)
,
where:
SLE (Single Loss Expectancy) is the expected loss
from a single incident;
ARO (Annual Rate of Occurrence) - expected
frequency of incident occurrence per year [12].
Thus, legacy IT infrastructure represents a collection of
systems characterized by high levels of technical debt,
limited flexibility, and integration challenges. These
features generate substantial risks at both technical and
operational levels and lead to additional regulatory
complications. A comprehensive assessment of the state
of outdated systems and identification of key risks are
essential prerequisites for the development of effective
risk management and compliance strategies, which, in
turn, support business resilience amid rapidly evolving
technological environments.
2. Risk management strategies for modernization and
migration
The use of AI-driven analytics and cloud technologies
enhances the accuracy of risk forecasting and facilitates
timely adjustment of management strategies [2]. The
initial stage involves a comprehensive assessment of
existing risks using tools for analyzing technical debt,
identifying system dependencies, and evaluating the
potential impact of each component on the overall
performance of the IT infrastructure. This process
includes the following core steps:
System mapping and grouping.
Identifying and
documenting the architectural features of legacy
systems allows organizations to determine their
relevance to business processes, forming the basis for
prioritizing migration tasks [1].
Risk forecasting using AI-driven analytics.
The
application of predictive models to evaluate the effects
of migration processes enables the early detection of
potential bottlenecks and real-time adjustment of
migration strategies [5,8].
Phased migration approach.
Dividing the overall
migration effort into smaller, manageable stages
reduces the likelihood of system failures and allows for
iterative
feedback
and
adjustments
at
each
implementation phase.
Implementation
of
monitoring
and
control
mechanisms.
Continuous tracking of performance
metrics, security compliance, and regulatory alignment
enables swift responses to emerging issues [1,7].
To improve risk management efficiency, it is
recommended to integrate these measures into a
unified strategic model that encompasses the technical,
organizational,
and
regulatory
dimensions
of
modernization.
This
approach
enables
rapid
identification of vulnerabilities, minimizes risk impacts
on operational activity, and reduces the cost of
maintaining outdated infrastructure.
In the context of risk management during the migration
of legacy systems, several strategies supported by
research can be outlined [1,2]:
Modular migration and refactoring.
Applying a phased
transition, in which systems are decomposed into
independent modules, reduces the impact of changes
on the entire infrastructure. This method lowers the risk
of system-wide failures and simplifies integration with
newer technologies [1].
AI-driven forecasting and prioritization.
Machine
learning algorithms are used to assess component
criticality and predict their impact on performance,
enabling the creation of an optimized migration plan
with minimal risk [2].
Cloud solutions and API integration.
The adoption of
modern cloud platforms allows for scalable flexibility,
while the use of APIs facilitates seamless integration
between legacy and modern systems, significantly
reducing operational risks [2,9].
Strategic planning and change management.
Engaging
key stakeholders, delivering training programs, and
implementing continuous monitoring systems help
reduce internal resistance and increase organizational
adaptability.
For clarity and organization, these approaches are
summarized in Table 2.
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Table 2. Key risk management strategies for legacy system migration [1,2,5,10]
Strategy
Description
Advantages
Modular migration
and refactoring
Decomposing the system into independent
modules followed by phased updates
Reduces the impact of changes on
the
entire
system,
simplifies
integration
AI-driven
forecasting
and
prioritization
Applying machine learning algorithms to
assess component criticality and predict risks
Increases the accuracy of risk
assessment, enables rapid response
to potential issues
Cloud
solutions
and API integration
Implementing cloud platforms and modern
APIs to integrate new and legacy systems
Scalability
flexibility,
reduced
operational
costs,
improved
interoperability
Strategic planning
and
change
management
Developing a clear migration plan with
stakeholder involvement, staff training, and
ongoing monitoring
Minimizes resistance to change,
ensures continuity of business
processes
In order to calculate the efficiency of security
investment, the following formulas should be used:
𝑅𝑂𝑆𝐼 =
𝐴𝐿𝐸
𝑏𝑒𝑓𝑜𝑟𝑒
− 𝐴𝐿𝐸
𝑎𝑓𝑡𝑒𝑟
−𝐶
𝑖𝑛𝑣𝑒𝑠𝑡
𝐶
𝑖𝑛𝑣𝑒𝑠𝑡
(2),
where:
𝐴𝐿𝐸
𝑏𝑒𝑓𝑜𝑟𝑒
–
annual expected damage before
the implementation of measures.
𝐴𝐿𝐸
𝑎𝑓𝑡𝑒𝑟
–
annual expected damage after
implementation of measures.
𝐶
𝑖𝑛𝑣𝑒𝑠𝑡
–
costs of implementation of risk
management measures.
If the obtained result is positive, it indicates that the
costs are recouped by reducing losses [12].
Also as part of the assessment it is necessary to calculate
the risk reduction ratio, which can be calculated using
the following formula:
𝐴 = 𝑃 × 𝐼 (3),
where:
P
–
maximum probability found in the list of risk
causes.
I
–
the maximum force of influence found in the
list of risk consequences [13].
The
implementation
of
comprehensive
risk
management
strategies
—
encompassing
modular
migration, AI-driven analytical assessment, integration
of cloud-based solutions, and proactive change
management
—
helps mitigate the negative impact of
migration processes on business operations. These
approaches not only reduce technical and operational
risks but also contribute to regulatory compliance,
which is a key factor in maintaining IT infrastructure
resilience in the context of ongoing technological
evolution.
3. Ensuring regulatory compliance and integrating
security measures
Modernizing legacy IT infrastructure entails not only
updating technological components but also ensuring
compliance with current regulatory standards and
integrating advanced security measures. The transition
to next-generation architectures requires a holistic
approach to data protection, access management, and
continuous security monitoring, particularly relevant in
the face of escalating cyber threats and tightening
regulatory frameworks such as GDPR, HIPAA, and
ISO/IEC 27001 [1,4].
One of the core challenges of legacy systems is their
inability to adapt to modern cybersecurity and
regulatory requirements. These systems often lack
support for contemporary encryption methods, access
control, and audit capabilities, which increases the risk
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of data breaches and regulatory violations. Accordingly,
modernization initiatives must integrate mechanisms
that enable:
Continuous security monitoring and auditing.
Ongoing
system surveillance and regular audits facilitate timely
detection of vulnerabilities and allow for migration
adjustments as needed.
Encryption and access control integration.
The use of
advanced
encryption
algorithms
and
the
implementation of multi-factor authentication and role-
based access control systems reduce the risk of
unauthorized data access.
Compliance automation.
Leveraging cloud-based
solutions with built-in compliance functions (e.g.,
automated log collection and analysis, regular security
policy updates) reduces operational risks and minimizes
the human factor [1,2].
Incorporating these measures is increasingly critical in
light of modern legal and security standards. The
implementation of advanced security controls not only
protects an organization’s informational assets but also
ensures transparency during the migration process,
enhancing trust among clients and partners [1].
The following table summarizes key measures for
ensuring regulatory compliance and integrating security
mechanisms during the modernization of legacy
systems.
Table 3. Measures to ensure compliance with regulatory requirements and integration of security measures
[1,6]
Measure
Description
Advantages
Continuous
monitoring and
auditing
Deployment of automated monitoring
systems, routine auditing, and log analysis
to detect security issues
Enables rapid identification and
resolution of vulnerabilities, increases
process transparency
Modern
encryption
methods
Utilization of AES, RSA, and other advanced
encryption standards for protecting data in
transit and at rest
Ensures high-level data protection,
reduces the risk of breaches
Multi-factor
authentication
and access control
Implementation of MFA systems and role-
based access control for safeguarding
critical systems
Reduces likelihood of unauthorized
access, enhances system change
management
Automated
compliance
systems
Integration of cloud platforms with built-in
policy updates, compliance reporting, and
regulatory controls
Ensures legal compliance, lowers
operational costs associated with
manual compliance
Thus, ensuring regulatory compliance and integrating
security measures constitute a multifaceted process
that requires a systematic approach when modernizing
legacy IT infrastructure. The implementation of
advanced
encryption
technologies,
multi-factor
authentication, and automated monitoring systems
enables the reduction of information security risks while
maintaining adherence to regulatory requirements. This
approach supports the development of a resilient and
secure IT environment capable of effectively sustaining
business processes in a rapidly evolving technological
landscape.
Conclusion
The analysis of legacy systems revealed their lack of
flexibility and high maintenance costs, which result in
operational and informational risks. The study proposed
strategies to mitigate these risks, including modular
migration, the use of AI algorithms for forecasting and
prioritization, as well as the adoption of cloud solutions
and modern data protection methods such as
encryption and multi-factor authentication.
Despite the findings, the study presents limitations
related to industry-specific contexts. It is recommended
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that future research expand the empirical base by
incorporating data from diverse sectors and conducting
long-term monitoring of the effectiveness of the
proposed measures. The results confirm the hypothesis
that a comprehensive and integrated approach to
modernizing legacy IT infrastructure leads to a
significant reduction in risk, enhances business
resilience, and creates competitive advantages in a
rapidly changing technological environment.
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