# Integrating MLSecOps in the Biotechnology Industry 5.0

Naseela Pervez1 Alexander J. Titus1,2,3 Naseela Pervez1 and Alexander J. Titus
1,2,3 In Vivo Group, Los Angeles, USA  Information Sciences Institute,
University of Southern California, Los Angeles, USA  Iovine and Young Academy,
University of Southern California, Los Angeles, USA  *Address all
correspondence to<EMAIL_ADDRESS>
###### Abstract

Biotechnology Industry 5.0 is advancing with the integration of cutting-edge
technologies like Machine Learning (ML), the Internet Of Things (IoT), and
cloud computing. It is no surprise that an industry that utilizes data from
customers and can alter their lives is a target of a variety of attacks. This
chapter provides a perspective of how Machine Learning Security Operations
(MLSecOps) can help secure the biotechnology Industry 5.0. The chapter
provides an analysis of the threats in the biotechnology Industry 5.0 and how
ML algorithms can help secure with industry best practices. This chapter
explores the scope of MLSecOps in the biotechnology Industry 5.0, highlighting
how crucial it is to comply with current regulatory frameworks. With
biotechnology Industry 5.0 developing innovative solutions in healthcare,
supply chain management, biomanufacturing, pharmaceuticals sectors, and more,
the chapter also discusses the MLSecOps best practices that industry and
enterprises should follow while also considering ethical responsibilities.
Overall, the chapter provides a discussion of how to integrate MLSecOps into
the design, deployment, and regulation of the processes in biotechnology
Industry 5.0.

###### keywords:

Artificial Intelligence, Machine Learning, Internet of Things, Security
Operations, Biotechnology

##  Chapter 0 Integrating MLSecOps in the Biotechnology Industry 5.0

Integrating MLSecOps in the Biotechnology Industry 5.0

###  1 Introduction

Industry 5.0 has revolutionized the way humans interact with various
industrial sectors [1], from human-machine collaboration to creating more
human-centric experiences. With a growing emphasis on sustainability, ethical
responsibilities, and involvement of social sciences in industry [2], there
has been a growing awareness in big data, manufacturing, Internet of Things
(IoT), digitalization, and production sectors. Industry 5.0 has witnessed the
launch of humanistic technologies and products like human-approved high
automation self-driven cars that leverage artificial intelligence (AI), which
have been rolled out in various cities [3], IoT-based contact-less shopping
experiences that seemed like need-of-the-hour during the pandemic [4], and
remote healthcare systems [5].

The biotechnology sector is as relevant to Industry 5.0 as microprocessors are
to computers. With every industrial revolution, the biomanufacturing sector
has undergone constructive development. Industry 5.0 is expected to
revolutionize the adaptation of robotic automation in manufacturing industries
to provide a more humanistic experience while achieving high yields on
production [6]. As the biotechnology industry supports critical sectors like
food, agriculture, healthcare, pharmaceuticals, and energy, companies must
stay current with industry standards. Maintaining integrity, security, proper
regulations, and ethical policies remain paramount to ensure responsible
development and use of biotechnologies across industries [7]. Massive amounts
of data in various biotechnology sectors (e.g. bioengineering, biodefense,
forensics) have resulted in the deployment of technological solutions - Big
Data, IoT, and AI in biotechnology. AI in biotechnology ranges from the use of
machine learning (ML) models for predictive analytics [8] to employing
generative AI techniques for accelerating discoveries with high computational
power [9]. AI, combined with IoT and Big Data, has resulted in an increasing
presence of biotechnology data in cyberspace. To protect the data, we need to
ensure that the AI models employed are secured.

Cloud technologies, which are centralizing data, have increased the threat of
attacks [10]. There have been surveys and studies that have discussed the
vulnerabilities of traditional cybersecurity practices [11, 12], and ML
techniques have been created to enable more secure cyberspace. These tools are
being developed to combat the growing threats to previously established
cybersecurity techniques. Industry 5.0 has introduced state-of-the-art (SOTA)
human-centric industry standards like employing robotic automation in
manufacturing and production sectors, using IoT and AI-enabled healthcare
systems, and employing generative AI in research and development. This has led
to the need for developing better security measures including malware
detection, network security, and intrusion detection; all crucial for the
current industrial revolution. Machine Learning Security Operations (MLSecOps)
integrates ML techniques to combat threats in the technology and digital space
and highlights the cybersecurity vulnerabilities of deployed ML models. It is
a promising approach to enhance cybersecurity [13] and promote a more secure,
less vulnerable cyberspace for various industrial sectors. Machine learning
has a proven ability to forecast future risks and threats, recognize threat
patterns, and detect anomalies [14]. These capabilities have led to the
development of several ML-based cybersecurity pipelines [15, 16] in various
industrial sectors.

The presence of biotechnology data in cyberspace and the digitization of labs
and biomanufacturing equipment have increased the industry’s threat of
attacks. The biotechnology sector must protect itself from cyber threats and
cyber-attacks while also adopting the revolutionized approaches of Industry
5.0. Adopting MLSecOps to develop modern and effective cybersecurity measures
to make the biotechnology industry less vulnerable and less prone to attacks
will pave the way for digitalized biotechnology sectors (e.g. bioengineering,
biosynthesis, biomanufacturing) In this chapter, we aim to provide an overview
of existing vulnerabilities in the biotechnology Industry 5.0, integrating
MLSecOps to secure the biotechnology Industry 5.0, and enabling decision
makers to understand how ML techniques can be employed to secure the threat
ecosystem of the biotechnology Industry 5.0, and how cybersecurity best
practices can protect the rapidly expanding number of ML tools in production.
Our focus is to provide an analysis and discussion of MLSecOps in the
biotechnology Industry 5.0.

###  2 Understanding the Biotechnology Industry 5.0

Biotechnology Industry 5.0 aims to employ advanced AI techniques to create
more personalized products. Involving humans more in industrial processes
while harnessing the power of advanced technology enables the industry to
benefit from the best of human intelligence as well as advanced computation.
Digitalization was introduced in industries during the third industrial
revolution in the 1950s. This era was a catalyst for the biotechnology
industry with the discovery of recombinant DNA which resulted in the
development and production of new proteins, antibiotics, and modern-day
synthetic biology [17]. This was a significant step for the drug discovery
sector in particular. The biotechnology industry has continued to digitize
ever since. The commercialization of the first biotechnology drug, human
insulin, which was developed using genetically engineered bacteria [18]
transformed the biotechnology industry from a niche industry into an economic
engine. The intersection of biotechnology with modern technological
discoveries like IoT, AI, and automation, and biotech sectors such as
synthetic biology, genomics, and bioinformatics have accelerated research and
development, manufacturing, and production industries across major economic
sectors. With the biotechnology Industry 5.0, there is the potential for
customization and personalization of biotechnology products and services (i.e.
personalized medicine) powered by AI/ML, robotics, and IoT. This will not only
improve the quality of human life in various aspects but it will also have a
major impact on corporate growth.

The role of emerging technology in the biotechnology industry is an area of
interest for the decision-makers of numerous sectors [19] and governments. In
this section, we will highlight the key technologies and their impact on the
biotechnology industry. We have briefly mentioned in section 1 the amount of
data present in the biotechnology industry and how that has promoted the
integration of the latest computational tools into the industry. Every sector
of the biotechnology industry has an increasingly large amount of data that is
used to draw meaningful insights, or that provides crucial insights for
decision-making. This data needs to be stored and accessed by multiple
organizations, either working on similar products or independent of each
other. One of the advancements of Industry 4.0, was the centralization of data
using cloud computing technologies. Cloud computing has enabled industries not
only to store huge amounts of data cost-effectively but the data can also be
accessed remotely by people in an organization. With the development of cloud
technologies, data like genomics, biomarkers, and clinical data could be
stored easily at low storage and maintenance costs [20]. Data is considered a
valuable asset to an organization, and companies have been collecting data for
decades. With the higher computational power provided by cloud computing to
study and analyze the data, the data is worth billions of dollars in today’s
world [21]. The data in the biotechnology industry has been used for a
plethora of sectors - genetic engineering, personalized medications,
regulatory compliance, sustainability, and identifying patterns in genomic
data, among others.

Artificial Intelligence (AI) is used to develop computer systems that can
process and recognize patterns in data, draw statistical inferences, and
interact with the physical world through mechanisms such as IoT devices [22].
In biotechnology, AI is being employed to support decision-making in the
development of smart and intelligent systems. Machine Learning (ML) is a sub-
discipline of AI that employs statistical methods for drawing inferences from
data. ML models have been used to detect COVID-19 virus from MRI’s [23],
optimize manufacturing processes [24], analyze crop growth patterns in
agriculture [25], and gain insights from gene data [26, 27]. Robotic
automation of biotech labs has resulted in a reduction in human errors,
improving safety, enabling faster clinical translations, and increasing the
rates of experimentation [28]. ML and deep learning (DL) techniques have
furthered the development of biotech industries since Industry 4.0. Recent
developments in AI, like generative AI, explainable AI, and large language and
vision models (LLMs) have opened up applications of AI/ML in the biotechnology
industry.

Environmental factors play a crucial role in the biotechnology sector. For
example, precipitation and sunlight play a significant role in the agriculture
sector in determining crop yield and temperature is a major factor for drug
production. Keeping these environmental factors under control requires human
intervention. Internet of Things (IoT) was introduced in the biotech industry
to reduce human labor and to control environmental factors. There is a study
that has proposed an end-to-end web application-based framework that
integrates embedded Arduino systems with Abiotic sensors to monitor, measure,
and analyze environmental factors like temperature, humidity, luminance,
audio/noise, and CO levels and precision manufacturing is a rapidly growing
market [29].

Additionally, IoT devices are integrated into healthcare systems for medical
devices and remote patient monitoring [30] which has promoted patient care
from the comfort of their homes. One of the recent breakthrough developments
in IoT devices is the wearable device. Wearable technology and digitalization
of healthcare systems can promote patient engagement and involvement in their
healthcare plan [31], and these devices are used for daily monitoring of
physical factors like heartbeat, oxygen levels, and human activity.

Industrially, the use of IoT devices in various sectors of the biotechnology
industry has enabled the expansion of biomanufacturing capacity, giving a
boost to the bioeconomy [32].

This section has briefly described the effect various technologies have on
various biotechnology industries. The biotechnology Industry 5.0 focuses on
developing a more humanistic approach to utilizing these technologies. IoT
devices in combination with AI can unlock powerful capabilities like
personalization, automation, context awareness, enhanced forecasting, and
predictive maintenance. Data being at the center of all this creates targets
and vulnerabilities in the biotechnology Industry 5.0. The subsequent sections
will discuss the threat landscapes, safety regulations, and enabling AI/ML
techniques to ensure a secure biotechnology framework.

###  3 The Role of MLSecOps in the Biotechnology Industry 5.0

The biotechnology Industry 5.0 combines digital technology with biotechnology.
Industry 5.0 has made breakthrough solutions to complex solutions - automatic
therapeutic systems [33], anti-aging research [34], cancer treatment research
[35], lab-grown organs [36], among the most impactful ones. These complex
experiments have led to data being at the center of the biotechnology research
and development sector [37]. There is a persistent tension in biotechnology -
threat and safety. Like any other industry, ML algorithms are being applied to
develop efficient solutions. Section 2 provides an overview of how ML has been
used to architect various design solutions to biotechnology problems. Section
4 will dive into threats in the biotechnology industry. It will explain where
security breaches can occur and how they affect the implementation of digital
technology in the biotechnology sector. This section is a discussion on the
role of ML in security operations in the biotechnology Industry 5.0. This
section provides two distinct views:

  1. 1.

Employing ML techniques to secure and protect the biotechnology industry
against cyber threats.

  2. 2.

Securing the machine learning models against attacks employing cyber
principles.

Machine learning (ML) has been employed to enhance the performance of various
traditional technologies. Autonomous vehicles are a great example of this.
Although ML algorithms play a major role in product development, they have
promising applications in other sectors like deployment and security.

Genome data have been growing at an unprecedented pace [38] and are used in
various industries - forensic investigations, healthcare, and biotechnology
being primary users. Preserving genome data is crucial and one of the most
important tasks currently at hand in the biotechnology Industry 5.0. The
current legal frameworks aim to protect genome data from malicious attacks but
the data is still the target of existing threats [39]. Encrypting genome data
while sharing and accessing it provides some protection against attacks, but
encryption algorithms are prone to being attacked themselves by hackers to
gain access to data [40, 41]. Genome data, if hacked can be manipulated and
used for harmful activities. ResNetAct is a deep learning model based on the
pre-trained ResNet model that has been fine-tuned to encrypt genome data [42].
AI/ML algorithms have also been successful in detecting threats and ransomware
detection [43]. AI/ML algorithms have shown promising performance on the
classification task of the ransomware samples [44], and deep learning
architectures have been employed to detect Trojan attacks on encrypted DNA
data. The supply chain is a also crucial sector of the biotechnology Industry
5.0 and directly affects every aspect of the bioeconomy [45], from
transportation to industrial machinery in labs. It even extends to automating
fault detection and building resilient fault detection systems that keep
highly sensitive research and manufacturing equipment safe.

ML techniques have been integrated into fault detection pipelines in
automobile supply chain frameworks. Statistical ML algorithms like linear
discriminant analysis and Naive Bayes algorithm have shown promising results
in classifying the condition of electric drive trains in supply chain [46]. ML
algorithms are also employed for monitoring and assessing bioprocesses in the
biotechnology Industry 5.0. [47]. Biotechnology Industry 5.0 has a network of
software in various sectors [48] and everything from running experiments to
carrying out visualizations and maintaining lab inventory all have different
software today. These software are prone to attacks and have vulnerabilities
in code that make them less secure. Large language models (LLMs) have
showcased promising results in identifying vulnerabilities in code [49] which
can be employed in securing the software architecture.

Healthcare and pharmaceuticals are major contributors to the bioeconomy. The
application of ML to diagnostic procedures has been booming. However,
developing these solutions while keeping data privacy and protection in mind
is crucial. Federated machine learning algorithms have been used for
developing secure medical imaging diagnostic solutions [50]. The
bioinformatics sector of the biotechnology Industry 5.0 is also vulnerable to
attacks, and biometric recognition systems are particularly impactful if
breached, as these systems rely heavily on AI/ML algorithms. The biotechnology
Industry 5.0 is emerging in developing robust authentication systems by
training advanced ML models on real-time data (e.g developing an object
recognition system on real-time humans rather than images of humans which
might classify a human sticker on a road as a pedestrian [51]) as well as
using ML for continuous authentication of mobile devices based on behavioral
biometrics [52].

Throughout the chapter, there have been examples showcasing the use of ML in
combination with IoT devices in the biotechnology Industry 5.0. These models
are used for an array of solutions in the biotechnology industry, including
predictive analysis for crop yield [53], classification of genome data [54],
enhance manufacturing of biotechnology products [55], management of
biotechnology labs [56], and more. Like other components of Industry 5.0, the
ML models are also vulnerable. ML models are like intellectual property. If
one gets access to data for gene therapies, emulating results or altering the
therapy is a concern for genetic research and development [57], and there have
been studies carried out to identify the vast threat landscape in ML models
[58]. MLSecOps in the biotechnology Industry 5.0 not only uses ML techniques
for security operations but also protects the AI and ML models that are used
to design solutions or products. ML models, particularly modern AI
applications, require huge computational power for training. Most of the
training of these huge models is done on outsourced cloud platforms, and they
are increasingly being subjected to malicious training [59] which can lead to
drift in the expected results. The above-mentioned paper has carried out 2
experiments - 1. handwritten digits and 2. street sign detector, which reflect
that the model performs well on user data but badly on some inputs that might
be attacker chosen e.g. a situation where red light can be classified as
green. One of the breakthroughs in Industry 5.0 has been the use of language
models in all sectors. The large language models (LLMs) are used for
automating production, customer service, as well as automating instruction
manuals in manufacturing industries. However, these models are prone to
attacks. Data theft is one of the major concerns and is one of the reasons for
skepticism to applying ML models in the biotechnology Industry 5.0 [60].

Frameworks have been developed to identify and mitigate backdoor triggers in
language models. One of the ways through which the model training can be
protected against malicious training samples is to train a second model to
recognize if the triggers are classified as safe targets [61]. Preventing
corrupted samples from training subsets has shown impressive results in
securing the language models and building the confidence to employ these
secure language models in production lines of supply chain framework for
various industries. Bias in machine learning models is also an active research
area. These biases can be reflected in the results produced by ML systems.
Integration of ML techniques in the healthcare industry has reflected biases
in the electronic health records of the patients [62]. Identifying and
reducing these biases is crucial to make the ML systems trustworthy in
biotechnology Industry 5.0. There have been solutions developed to tackle the
problem of model bias in the healthcare sector of biotechnology Industry 5.0,
where focusing on data handling for ML systems has been shown to reduce these
biases in radiology data [63]. Another approach to identifying the biases and
risks of ML models is to perform behavioral analysis of the model on outlier
data points and keep track of the model performance for any abnormalities
[64]. Analyzing model metrics and behaviors over time enables users to
understand if there are any biases or alterations in models that affect the
model performances (e.g. if a classifier’s accuracy decreases over time or
classifies input wrong, it indicates that the model has been altered).
Monitoring frameworks in biotechnology Industry 5.0 have huge applications and
requirements to catch any abnormal ML model behaviors early on.

In this section, we have discussed the role of MLSecOps and its application in
the biotechnology Industry 5.0. We have provided an overview of how ML systems
can be used to protect biotechnology Industry 5.0 frameworks. This section
also emphasizes the importance of securing the ML frameworks and
infrastructures employed in biotechnology Industry 5.0. The remainder of the
chapter focuses on identifying the threats in the biotechnology Industry 5.0
and discussing regulations and ethical responsibilities that should be
fulfilled while designing infrastructure and solutions for various sectors in
the biotechnology Industry 5.0.

###  4 Threat Landscape in Biotechnology Industry 5.0

Biotechnology Industry 5.0 can lead to progressive research as well as product
development in the biotech sectors. This is crucial for the bioeconomy. We
have discussed the biotechnology sectors and the effect of the fifth
industrial revolution on the biotechnology industry in section 2. Although
employing combinations of modern technologies like advanced IoT devices and ML
models that can outperform human expertise has resulted in smart industries
[65], it has also exposed biotechnology data to threats. Section 2 has also
introduced cloud technologies in biotechnology Industry 5.0. The presence of
huge amounts of data on these centralized systems and the growing reliance on
production ML models has increased the vulnerability to cyber-attacks. This
section covers the biotechnology industry’s cyber threats as well as
sophisticated cyber attacks. We will provide references to case studies that
are crucial in the cybersecurity space in biotechnology Industry 5.0.
Recognizing threats specific to biotechnology Industry 5.0 will help
stakeholders make informed decisions on securing the industry and introducing
security measures in biotech sectors.

####  1 Cyber Threats Specific to Biotech: Identifying unique vulnerabilities.

There is variability in the data in biotechnology Industry 5.0, which requires
unique and specific attention to these vulnerabilities. Digital DNA has wide
applications in retail, healthcare and forensic departments [66]. Encoding DNA
efficiently into digital form opens the horizons to employing AI/ML algorithms
on this data for various downstream tasks (DNA sequencing, DNA genotyping, and
DNA matching ) [67, 68]. Sensors and IoT devices have become a part of our
livelihoods. Homes have light sensors for energy conservation [69], cars have
motion sensors to detect real-time vehicle motion [70] which can be employed
in self-driving cars, and thermal and humidity sensors have been used to check
for overheating in buildings [71]. Similarly, the use of IoT devices in
various biotechnology sectors like agriculture, healthcare industries, and
biomanufacturing can create end-to-end cost-effective, and high-efficiency
systems. Wireless Body Area Network - a network of IoT devices or sensors as
wearable devices as well as implants, have revolutionized remote healthcare,
making patient real-time monitoring systems proactive [72]. This can have
promising results in developing healthcare systems for patients, such as those
with dementia, that maintain a balance between the risks of symptoms like
wandering while also assuring autonomy to the patients [73]. In addition to
healthcare systems, there have been studies and reports in the agriculture and
food domain carried out for multiple use cases [74]. The data from these
implants and IoT devices are used to create dashboards for tracking and
management purposes, ML architectures for drawing out various inferences or
stored in the cloud to be accessed by stakeholders. This makes the data from
IoT devices vulnerable and also poses a risk of breaching the privacy of the
stakeholders involved. Biometrics like fingerprints and facial recognition
systems are used in defense and law enforcement industries as well as other
industries and unauthorized access to biometric data can aid bad actors, such
as terrorist organizations, and create a risk to an entire nation [75].
Securing biometric data like facial features, iris patterns, speech data, etc
is of national interest and there have been attempts to secure this data [76].
Digital ID cards like driver’s licenses as well as state IDs have made storing
and producing these cards an easy task. Banking services have automated their
identification system using AI-enabled technologies. This means an AI model
will access your driver’s license to check for authenticity. ID verification
using AI has been viewed as a hazard to human privacy. A biometric data breach
can lead to identity theft, deepfakes, and possible criminal activities.
Biotechnology Industry 5.0 has to ensure that the data in various sectors of
the industry is secure and protected against malicious attacks.

####  2 Advanced Persistent Threats (APTs) and Biohacking: Addressing
sophisticated cyber attacks.

The COVID-19 pandemic witnessed the need for protecting the biotechnology
Industry 5.0. Pandemics and epidemics pose a risk to a broad swath of society.
Advanced Persistent Threats (APTs) can be described as malicious activities
that are hard to uncover and remain undetected. In the biotechnology industry,
one example of APTs is illegally manufacturing and selling some variants of
approved drugs [77]. The under-the-radar behavior of APTs allows them to
remain undetected for longer amounts of time. In the past, there have been
reports of APTs attacking various sectors of the biotechnology industry.
Biotechnology Industry 5.0 being more than ever present in cyberspace makes it
prone to APTs. Currently, various APTs have been reported to be active. One of
them is APT41, suspected to have originated in China and targets biotechnology
sectors like the pharmaceutical industry for financial gains [78]. The
healthcare industry has always been a threatened sector of the biotechnology
industry. Case studies have been carried out that showcase recent developments
to make the threats more evasive by Wekby (also known as APT18) - a famous APT
in the healthcare sector [79].

In this section, we have provided a picture of the biotechnology Industry 5.0
data that could be under attack and needs to be secured and protected. We have
also given a brief on the progression of APTs and biohacking in the
biotechnology Industry 5.0. Now that we have a deeper understanding of the
biotechnology-specific threat landscape, we can focus on how developing and
adopting practices that avoid these landscapes. The rest of the chapter will
focus on how MLSecOps can be integrated into biotechnology Industry 5.0 to
enhance privacy and security. We aim to discuss how MLSecOps can be employed
for cybersecurity while ensuring that we maintain regulatory adherence and
consider social and ethical responsibility.

###  5 Regulatory Frameworks and Compliance

The biotechnology Industry 5.0 has millions of consumers throughout the world.
It has an approximate value of $2.9 trillion impact on the US economy and
provides 2.1 million jobs in the United States [80]. The biotechnology sectors
include agriculture, healthcare, pharmaceuticals, energy, and many sectors
that have a direct impact on consumers. The products of the biotechnology
industry must meet standards of quality, privacy, and security [81].
Regulatory frameworks are established to standardize and streamline production
and manufacturing processes and prevent any security or privacy breaches. In
the previous sections, we discussed how ML and AI are integrated into the
security operations of the biotechnology Industry 5.0. We have discussed the
current threats to the biotechnology Industry 5.0 in section 4. Throughout the
chapter, we have discussed how IoT in combination with ML and AI can help
personalize the biotechnology Industry 5.0. However, enterprises are
apprehensive about rolling out AI-based products to the public [82]. These
products must achieve complete compliance with regulations. In this section,
we provide an overview of the current regulatory frameworks, challenges in
compliance frameworks, and standardization of regulatory and compliance
frameworks globally, keeping biotechnology Industry 5.0 as a focus point.

####  1 Current Regulatory Landscape: Overview of existing cybersecurity and
biotech regulations.

Artificial Intelligence integration and the growing digitization in the
biotechnology Industry 5.0 have made the industry vulnerable to cyber attacks.
To ensure the safety of the industry as well as to ensure that consumers’
privacy stays intact, the biotechnology Industry 5.0 has to comply with 2
major groups of regulations - biotechnology regulations and cybersecurity
regulations. Here we discuss what regulations fall under these that the
biotechnology Industry 5.0 has to comply with. Biotechnology regulatory
frameworks include the sectors of the food industry, drug production, energy
industry, etc. The National Academies of Sciences Engineering and Medicine
[83] has provided an overview of existing regulations in these sectors. The
food and drug industries are very large consumer markets. They directly affect
children, adults, and seniors equally. In the past, there have been mishaps
related to the food industry. In 2015, there was a multi-state outbreak of
high-risk infection Listeriosis in the United States that was directly linked
to Blue Bell Creameries products [84]. There have been reports of 23 deaths
among patients who are using a non-approved anti-gout drug [85]. Developing
and adapting the regulatory frameworks in the food and drug industry is
crucial. The Food and Drug Administration (FDA) is a prominent regulatory body
of the food and drug industry in biotechnology [86]. FDA regulates and
approves products like food, drugs, medical devices, vaccines, cosmetics,
tobacco, etc. Biotechnology Industry 5.0 will see a boom in technologically
advanced products, especially medical devices. For these devices to be
approved for public use, they must comply with FDA regulations. FDA also
regulates gene therapies which are crucial to the biotechnology Industry 5.0.

While building products that comply with regulatory frameworks is essential,
there are also regulatory frameworks that oversee the production process.
Bioprocessing and biomanufacturing industries release substances into the
environment. Global greenhouse gas emissions have led to climate change which
affects terrestrial as well as aquatic organisms. Wastes from the
manufacturing sector of the biotechnology Industry 5.0 include biological
waste like pathogens, toxins from drug production, heavy metals, hazardous
chemicals, and greenhouse gases. Industry 5.0 needs to ensure sustainability
while developing high-efficiency products. The Environmental Protected Agency
(EPA) ensures proper waste disposal, regulates the production of hazardous
gases and chemicals, and regulates the environmental release of pathogens
[87]. The supply chain management of biotechnology Industry 5.0 needs to
ensure that all the processes comply with EPA regulations.

Agriculture is one of the largest sectors of the economy globally. The
agriculture sector of biotechnology has prospered and developed into an
economic asset for every nation. However, there are regulatory frameworks that
the sector has to comply with to ensure safety and protection. US Department
of Agriculture [88] overlooks the agriculture industry and regulates farming,
food and nutrition, forestry, opioid production, and organic produce
frameworks. The USDA also ensures that it complies with EPA regulations and
maintains the climate conditions.

Having discussed some of the major biotechnology regulatory frameworks, some
cybersecurity frameworks are crucial to the biotechnology Industry 5.0.
Medical devices and monitoring products have been growing more than ever.
Remote patient monitoring is a prospective advancement in biotechnology
Industry 5.0. However, these systems should be safe and secure for the
patients. Health Insurance Portability and Accountability Act (HIPAA) [89]
ensures that healthcare industry protects the patient information. HIPAA
ensures that the healthcare products released into the market safeguard the
patients’ privacy. Business owners who step into the healthcare sector should
ensure HIPAA compliance. Online payments and transactions have made our
everyday lives easy. Any purchases can be made online without any hassle using
your card. However, card information needs to be protected. Biotech labs and
healthcare businesses make use of online transactions. However, the payment
gateways must secure the card information. Payment Card Industry Data Security
Standard (PCI DSS) [90] ensures the protection of card information data while
making online payments. All enterprises in the biotechnology Industry 5.0 need
to comply with this standard to ensure consumer trust. The National Institute
of Standard and Technology (NIST) Cybersecurity Framework [91] provides
guidelines to enterprises, industries, and government agencies to minimize
cybersecurity risk. The framework ensures a high level of protection against
cyber attacks. Complying with NIST cybersecurity regulations will ensure that
advanced technology can be employed in biotechnology Industry 5.0 with more
confidence, fewer apprehensions, and fewer vulnerabilities. As we transition
into Industry 5.0, the need for trustworthy, safe, and secure AI practices is
needed more than ever. The US government has released executive orders that
outline the emerging policies for the safe and secure use of AI [92]. The
executive order has mentioned practices and policies that ensure the safe use
of AI by developers as well as protect the public from AI-enabled attacks.
This is truly the need of the hour as we see AI being used in all dimensions
of industries and integrated into public use at a scale that hasn’t been
witnessed before.

####  2 Compliance Challenges: Adapting to fast-evolving biotech advancements.

Although the above-mentioned regulatory bodies ensure security and safety,
complying with these regulations has put many enterprises and industries in a
tight spot. From product development to product release, complying with so
many regulations requires a separate legal team, abundant documentation, and
iterating through the process over and over again. Enterprises, especially
small business owners, believe that following so many regulations is counter-
productive and costs them money as well as labor [93]. The study further
presented the views of how small business owners have a hard time making
decisions to comply with regulations due to the absence of behavioral
heuristics taken into account while designing these frameworks. The regulatory
frameworks differ from country to country. And so does the expense and
documentation of implementing these frameworks. Business owners in the United
States have reported higher legal costs and delayed economic activity or
profit to get a compliance clearance from the government as opposed to other
economically established nations [94].

A key example is the discovery and development of the ground-breaking tool,
CRISPR, for gene modification. CRISPR has potential applications in
agriculture to cater to global food demand and human healthcare to develop
treatment plans for genetic diseases and cancer [95], among many other areas.
CRISPR combined with AI technologies will transform medicine in the next
decade, and while CRISPR was first published in 2012, the first evidence of
CRISPR being fruitful in gene therapies was identified in under a decade [96].
However, CRISPR has only just been approved in December 2023 by the FDA for
sickle cell disease [97]. There is a major opportunity for CRISPR, and other
emerging biotechnologies, to have an impact as long as regulation modernizes
alongside it.

Many technologies discussed in this chapter utilize IoT and AI sectors like
machine learning, deep learning, and reinforcement learning. However, getting
these products to production lines has been a struggle for biotech companies
because they do not meet the current regulatory standards. Although there are
compliance challenges currently in biotechnology Industry 5.0, there is no
doubt that Industry 5.0 and its ground-breaking developments are here to stay.
Transitioning from Industry 4.0 to Industry 5.0 relies on the decision making
of policymakers to set standards for regulatory frameworks [98].

####  3 International Standards: The role of global cooperation in standard
setting.

A common refrain notes that the world is a small place. We can now travel from
one side of the world to another in a matter of a few hours. Standardization
has been implemented in measuring distances, heights, and weights for many
centuries. Now that businesses are globally releasing their products, they
have to make sure that their product meets the regulations of each country
they appear in. This costs the business owners time, money, and labor.
Introducing technology in all industries has challenged global
standardization. Technology has had a global impact on standardization
policies [99], with nations largely developing their own standards.
Standardization ensures interoperability, which means that a product developed
in Italy can benefit the people of the United States, and vice versa, equally
and unified standards would be beneficial in the biotechnology industry.
Standardization maintains consistency in the industry while also ensuring that
consumers are protected and there is transparency between the industry and
consumers [100].

We now have a good understanding of how biotechnology Industry 5.0 differs
from biotechnology Industry 4.0 and what are the potential threats in the
biotechnology Industry 5.0. We have also discussed how ML can be implemented
in the security operations of biotechnology Industry 5.0. This section gives
an overview of the existing regulations and how Industry 5.0 has compliance
challenges with these regulations. We have also briefly highlighted the
importance of maintaining global standards. In the remainder of the chapter,
we will present a perspective of how can MLSecOps be integrated into
biotechnology Industry 5.0 while complying with the regulations, and the
ethical and social responsibilities of Industry 5.0. We will also provide a
perspective of what goes into decision-making for designing policies and
regulations for Industry 5.0 focusing more on biotechnology Industry 5.0.

###  6 MLSecOps Best Practices for Biotechnology Industry 5.0

Industry 5.0 is all about personalizing products and making industrial
processes human-centric. In section 2 and section 3, we have discussed the
role of machine learning in Industry 5.0 focusing on the biotechnology sector.
The chapter has highlighted the threat landscape in biotechnology Industry 5.0
in section 4 which is one of the most crucial aspects of employing the latest
technology. Having discussed the role of machine learning in MLSecOps and the
current threat landscape in biotechnology Industry 5.0, as well as the major
regulatory bodies in section 5, this section aims to discuss the MLSecOps
practices that can be employed in biotechnology Industry 5.0. In this section,
we aim to discuss how MLSecOps can be integrated into Industry 5.0 while also
complying with current regulatory frameworks. In section 3 we have discussed
the role of ML in MLSecOps in two aspects - protecting the machine learning
systems used in Industry 5.0 and employing machine learning to protect the
Industry 5.0 products. In this section as well we will discuss the MLSecOps
practices keeping the two aspects of machine learning in MLSecOps for
biotechnology Industry 5.0.

####  1 Risk Assessment and Management

Risk assessment and management is a major part of all sectors. Building
products with risk analysis and management best practices ensures that the
complexities in manufacturing, production, and usage have been considered
before the products are commercialized [101]. Biotechnology Industry 5.0
includes sectors like informatics, pharmaceuticals, healthcare, and
agriculture. These sectors all have crucial data and information that needs to
be protected against threats. The products in Industry 5.0 employ machine
learning techniques in various processes which makes Industry 5.0 more
vulnerable [102]. Risk assessment and management ensure that these products
protect the privacy of the enterprise, industries, as well as end users. The
healthcare sector in biotechnology Industry 5.0 deals with high risks of
phishing, cloud configuration errors as well as web application-based attacks,
among others. With the increased use of modern technologies like IoT, cloud,
and artificial intelligence, healthcare data is increasingly online. There
have been studies that have emphasized the need to build modern security risk
assessment frameworks for healthcare in Industry 5.0 [103]. During the
COVID-19 pandemic, biotechnology sectors like healthcare, pharmaceutical
companies, and government departments for public safety faced increased
security breaches and cyber attacks [104] which emphasized the need to
implement robust access control. Employing more robotics and automation in
manufacturing in Industry 5.0 can also lead to vulnerabilities like data theft
and security breaches which need to be addressed [105]. With the increased
amount of data sharing in the advanced solutions developed in Industry 5.0
using AI, there is an increased risk to the data [106] and hence developing
frameworks to assess and manage these risks is crucial to the success of
Industry 5.0.

Besides the risks and vulnerabilities in the digital landscape, the affects of
AI and technology also pose risk in the physical system. Supply chain and
manufacturing sectors of the biotechnology Industry 5.0 deal with production,
manufacturing as well as waste disposal in the biotech industry. Biotechnology
Industry 5.0 sectors, mainly the pharmaceutical industry, is a major producer
of heavy metals. With rapid advancement in applying technologies to supply
chain management, the production and manufacturing of pharmaceuticals have
skyrocketed. There is a need to assess the risk and take steps towards
mitigating and managing the risks from heavy metal release into the
environment [107]. AI algorithms have showcased promising results on the
detection of heavy metals like mercury, cobalt and chromium in blood and urine
samples [108]. Machine learning has been used to develop security assessment
and management frameworks for digital consumers and the energy industry [109,
110] which emphasizes the role of ML and AI in developing secure and robust
risk analysis systems in Industry 5.0.

In biotechnology Industry 5.0, one of the most vulnerable components is data.
Regulating the data access and implementing access controls on this crucial
data is of utmost importance. Electronic record compliance in FDA regulatory
frameworks emphasizes implementing access control in enterprises and
industries [111]. The pharmaceutical sector of the biotechnology industry has
been threatened and faced cyber attacks on multiple occasions [112]. There
have been multiple data breaches that have put the privacy of people as well
as the security of the population at high risk. It is, therefore, important to
build access-controlled secure systems in Industry 5.0.

####  2 Building Robust and Resilient Systems

The biotechnology industry has witnessed a substantial increase in technology
dependency over the decades. The role of modern technologies, especially AI,
has been growing exponentially in all biotechnology sectors [113]. Although ML
combined with IoT has led to some groundbreaking developments in biotechnology
sectors like biometrics, healthcare, and agriculture, building resilient
systems and adopting powerful ML techniques is an ongoing challenge. One of
the major issues in building resilient and robust systems in the biotechnology
Industry 5.0 is deploying the machine learning pipelines securely. This
involves considering any retraining of the models that might be required after
releasing into the production lines [114]. Since machine learning models might
need to be retrained on data continuously over time, some studies have
proposed robust frameworks for continuous deployments of these ML models
[115]. Biotechnology Industry 5.0 products that integrate IoT and AI models
depend on these robust frameworks for commercialization.

While protecting ML models is crucial, biotechnology Industry 5.0 can also
exploit the power of these models in developing robust and resilient systems.
Machine learning and deep learning techniques have been used to develop
efficient frameworks for anomaly and outlier detection [116, 117, 118]. Since
the advent of the manufacturing industry, having humans in the loop has
enhanced the reliability as well as efficiency of the products. Biotechnology
Industry 5.0 deals with real-world problems that are crucial and high-risk.
One example is during COVID-19 when production lines of vaccines were
threatened [119].

The ML models employed in agriculture, healthcare, pharmaceuticals, and supply
chain sectors of the biotechnology Industry 5.0 are also at a very high risk
for attacks. There have been studies that showcase the effectiveness of human-
in-loop for human-machine teaming (HMT) in the healthcare sector. The study
has effectively simulated the human-in-loop intelligent system in two case
scenarios - 1. emergency care provider using voice-based synthetic assistants
for MARCHE protocol scenario (massive hemorrhage) where it provides a step-
wise treatment plan with no adverse effect to the patient and 2. Medic
treating a patient follows the scenario in case 1 but involves extra steps of
history taking and assessing the patient’s airways where the caretaker takes
less time to understand the instructions and thus fastens the treatment plan.
This study has showcased how human-in-the-loop (HIL) can be used to enhance
the performance of HMT systems [120]. Another study has quantified the
effectiveness of HMTs and attempted to provide a benchmark to enhance human-
machine collaboration [121]. Keeping technology updated and current is another
important aspect of building resilient systems to ensure smooth operations.
AI/ML applications have been growing exponentially over the last decade.
Updating models and replacing these models with better and recent models is
crucial to developing systems that can prevail in the industrial revolutions.
Voice-based assistants are now based on Large Language Models (LLMs) as
opposed to Long Short Term Memory (LSTM) models which were quite heavily
employed until the introduction of the transformer architecture [122]. These
recently developed voice assistants are robust, reliable, and efficient and
have a promising future in the manufacturing, production, as well as
advertising sectors of Industry 5.0 [123]. Some of the guidelines and
practices discussed here for the development of robust and resilient systems
are crucial for the integration of ML in product development and enhancing the
security of current products in biotechnology Industry 5.0.

####  3 Monitoring and Incident Resolution

MLSecOps in biotechnology Industry 5.0 ensures privacy and safety in products
or services integrating machine learning with other technologies like IoT and
cloud computing. Developing and defining standards to use modern technologies
ensures standardization, global usage, and effective commercialization. So far
in this section, we have discussed risk assessment and management of ML
pipelines as well as practices that pave the way for the development of robust
and resilient systems in Industry 5.0. However, ML models are sensitive and
the results can easily fluctuate on outlier data points [124]. The
vulnerabilities of ML models make it crucial to include monitoring the models
in the practices of MLSecOps in Industry 5.0. This chapter has discussed how
the biotechnology Industry 5.0 deals with crucial data that affects the
economy, industry, as well as end users. One of the ways to make sure that the
ML models are resilient is to probe these models for weaknesses. Adversarial
Machine Learning (AML) is among the most effective techniques for checking ML
pipelines for attacks, threats, and vulnerabilities. AML has been used to test
the defense mechanism of ML techniques in IoT environments [125] and intrusion
detection systems [126, 127].

While probing the ML models is an effective technique to find weaknesses of
the developed systems in Industry 5.0, the models also need continuous
training and deployment as discussed in section 2. It is therefore important
that the models be monitored. Continuous monitoring of the ML integrated
system not only helps protect them against attacks but also helps identify any
drift or abnormalities in functioning. Machine learning has promising growth
in the supply chain management sector of biotechnology Industry 5.0.
Frameworks for continuous monitoring in the supply chain post-deployment have
been proposed [128]. In biotechnology Industry 5.0, we not only need to
monitor ML models but also the data. Any data alterations can lead to abnormal
behavior of ML models in an ecosystem. Therefore, monitoring the data to
prevent any data poisoning is also crucial in Industry 5.0, especially the
bioinformatics and genetic sectors of biotechnology Industry 5.0. Access
control over who can retrain and deploy models, checking the updated model for
performance drifts, and control over who can get access to data are some of
the techniques to handle and detect data poisoning [129]. Monitoring ML
ecosystems and implementing practices to deal with threats or attacks ensures
a robust, enduring, and safe biotechnology Industry 5.0.

This section has discussed effective MLSecOps practices that can be employed
to advance the biotechnology Industry 5.0 while ensuring privacy and security.
The section has also given an overview of the importance of monitoring ML
systems as well as the data used in AI/ML environments. Access control and
monitoring practices concerning ML integration ensure that the biotechnology
Industry 5.0 has a positive impact on the economy, strong research, and end-
user acceptance of innovative products. Towards the end of this chapter, the
focus is drawn toward ethical and social responsibilities in Industry 5.0 and
providing key points to assist decision-makers in developing compliance and
regulatory frameworks for biotechnology Industry 5.0.

###  7 Ethical Considerations and Social Responsibility

Biotechnology Industry 5.0 focuses on developing human-centric products. While
the latest technological advances like machine learning, natural language
processing models, and computer vision models are being integrated, Industry
5.0 has a goal of personalization. Personalizing medicine, workouts, meals,
etc. is advancing in all sectors of Industry 5.0. This chapter has outlined
how Industry 5.0 will witness the growth of integrating ML with other
technologies in all sectors. This will lead to biotechnology Industry 5.0
contributing more to the global economy. However, section 4 has provided an
analysis of the threats that are current in the biotechnology Industry 5.0.
Although, Industry 5.0 has laid out some regulations discussed in section 5
the biotechnology Industry 5.0 is still vulnerable. In this section, we will
highlight the importance of considering social responsibilities and ethical
considerations in biotechnology Industry 5.0.

####  1 Ethical Use of Biotech Data: Balancing innovation with privacy rights

The biotechnology industry has a plethora of data. Biometrics, genetic data,
patient notes, and medical device data are some of the most common examples of
data in the biotechnology industry. This industry is expected to grow by
13.96% approximately [130]. Data is a huge source of income and Industry 5.0
aims to build products for the end users that harness the power of ML
algorithms on this data. The data is crucial and needs to be secured while
being employed in the product development stage. Section 1 has discussed how
the data must be protected and secured while being used in ML algorithms as
well as while being stored in the cloud. There is however one more aspect to
this. The users must be aware that their data is being used. Many
biotechnology companies monetize their data by selling it to other companies
for product development [131]. This however raises a concern - Is the user
aware that their data is being sold?. There have been multiple reports of
private data being sold without the consent of users [132] and this puts the
privacy of individuals at risk. However, there have been introductions and
amendments to consumer data privacy acts globally that make sure that users
are aware of where their data goes [133, 134, 135, 136]. The users should not
only be aware of the fact that their data is being sold but there should be
transparency of the data activities. The user should have full information on
how the company is using the data internally and if the data is being provided
to external sources [137]. This builds trust among users and enterprises as
well as assures that the user is compliant with the data being used. To build
a balanced ecosystem of innovation and security the biotechnology Industry 5.0
needs to ensure that they are following cybersecurity best practices. This
minimizes the vulnerability of the ML models, software, and the data. Section
1 has discussed the cybersecurity regulations and policies. These regulatory
frameworks ensure consumer as well as industry protection and growth.

####  2 AI Ethics in Biotechnology: Addressing potential biases and ethical
dilemmas

The scope of this chapter is to focus on biotechnology Industry 5.0.
Personalization and developing technology that is human-centric has been the
goal of Industry 5.0 and integrating ML with other technologies like cloud
computing and IoT has made this goal achievable. However, ML algorithms are
vulnerable which makes them an easy target for malicious activities. MLSecOps
in biotechnology Industry 5.0 focuses on developing secure biotechnology
practices by securing ML algorithms as well as enhancing security by employing
ML models. This has been described in section 3. AI models perform based on
the data they are trained on. The biases of these data get transferred to the
models implicitly. The biometric sector of biotechnology Industry 5.0, for
example, has developed advanced facial recognition software. However, there
have been reports of racial bias in this software where they have failed to
recognize people of color [138]. Similarly, facial recognition systems also
exhibit demographic biases [139]. Gender bias in research and clinical trials
has been reported where male-centric research is given more funding compared
to female-centric research [140, 141]. There have been attempts to mitigate
these biases [142]. Addressing these issues in the biotechnology Industry 5.0
which includes the implicit bias in ML systems will result in a secure and
privacy-oriented Industry 5.0. Although, identifying the biases and mitigating
them in AI is an important step towards a more safe biotechnology Industry 5.0
it is crucial that regulatory frameworks be formed and the industry complies
with these frameworks in their practices. Section 1 has described the current
as well as emerging cybersecurity frameworks. These frameworks ensure
precautionary steps are taken and there is transparency between the end user
and the companies regarding how their data is being used. Taking
responsibility for one’s actions is a step towards empathy and improvement.
Biotechnology Industry 5.0, especially the healthcare sector, demands that not
only precautionary measures be taken but in case of any errors, it is
important that the industry takes responsibility and makes corrections of
these errors. Liability frameworks that consider the immense integration of AI
in healthcare and medicine ensure that there is a balance between innovation
and safety [143].

####  3 Social Implications: The impact of biotech advancements on society.

We are well aware that change incurs some impact. While we try to maximize the
positive impact of any development, there are always some negative impacts
associated with it. Similarly, with advanced technology being at the center of
the biotechnology Industry 5.0, we are looking at labor force change, where
reskilling and upskilling will be critical. With advancements in supply chain
automation in Industry 5.0, human jobs are expected to decelerate [144]. This
may be a point of concern for society where more and more automation can cut
out the jobs of humans and their livelihoods. Biotechnology Industry 5.0 must
focus on reskilling and upskilling the talented workforce, particularly in
areas where jobs will be impacted by automation. Besides this, another social
impact of Industry 5.0 can be the cost. With advanced medical devices in the
healthcare industry as well as AI-enabled gene therapies, the affordability of
the industry can decrease [145]. However, biotechnology Industry 5.0 focuses
on employing cutting-edge technology with a great focus on sustainability
[146]. This not only makes the products from biotechnology Industry 5.0 like
pharmaceuticals, healthcare, clinical trials, etc. affordable but also
promotes the bioeconomy.

Biotechnology Industry 5.0 possesses great potential to improve human life on
many fronts. The healthcare industry has displayed great potential at
integrating technology to improve patient care, remote patient monitoring, and
using voice-based assistants in surgical processes as mentioned in section 2.
Keeping societal well-being and ethical responsibilities in consideration,
biotechnology Industry 5.0 will boom at providing human-centric solutions to
current problems in all sectors. Understanding the vulnerabilities and the
role of MLSecOps in the biotechnology Industry 5.0, we have provided a
perspective of the ethical and social responsibilities in this section that
must be adhered to develop sustainable, robust, and resilient systems.

###  8 Conclusions

The integration of MLSecOps into the biotechnology Industry 5.0 is not just
beneficial but imperative for safeguarding against increasingly sophisticated
cyber threats. This chapter has underscored the critical vulnerabilities
within the industry and presented a case for adopting MLSecOps practices. By
embracing a proactive approach to cybersecurity, leveraging advanced ML
techniques, and adhering to regulatory and ethical standards, the
biotechnology sector can fortify its defenses and ensure the privacy and
security of its data and systems. Looking forward, the industry must continue
to evolve its security practices in tandem with technological advancements,
ensuring a resilient and robust defense mechanism that can adapt to new
challenges. In doing so, the biotechnology Industry 5.0 can achieve its full
potential, driving innovation while safeguarding the invaluable data at its
core.

### Abbreviations

[DMEM-FBS]

Machine Learning

Internet of Things

Artificial Intelligence

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