Data security in AI systems

ENSURING DATA SECURITY IN
AI SYSTEMS
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As AI becomes deeply embedded in our everyday lives, the data fueling these
intelligent systems becomes more valuable than ever. However, along with
its increasing value come heightened risks. With AI systems having access to
vast amounts of sensitive data for tasks like business analytics and
personalized recommendations, safeguarding it has become increasingly
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important in today’s digital era. Data security is a major concern of current
times, the implications of which extend far beyond the IT department,
encompassing a broader scope of interest.
Data security in AI systems is not just about safeguarding information; it’s
about maintaining trust, preserving privacy, and ensuring the integrity of AI
decision-making processes. The responsibility falls not just on database
administrators or network engineers, but everyone who interacts with data
in any form. Whether creating, managing, or accessing data, every interaction
with data forms a potential chink in the armor of an organization’s security
plan.
Whether you are a data scientist involved in the development of AI
algorithms, a business executive making strategic decisions, or a customer
interacting with AI applications, data security a몭ects everyone. Hence, if you
are dealing with data that holds any level of sensitivity — essentially,
information you wouldn’t share with any arbitrary individual online — the
onus of protecting that data falls upon you too.
In this article, we will delve into the intricacies of data security within AI
systems, exploring the potential threats, and identifying strategies to
mitigate the risks involved.
Why is data security in AI systems a critical need?
Understanding the types of threats
The role of regulations and compliance of data security in AI
Principles for ensuring data security in AI systems
Techniques and strategies for ensuring data security
Best practices for AI in data security
Future trends in AI data security
Why is data security in AI systems a
critical need?
With advancements taking place at an unparalleled pace, the growth of

arti몭cial intelligence is impossible to ignore. As AI continues to disrupt
numerous business sectors, the importance of data security in AI systems
becomes increasingly important. Traditionally, data security was mainly a
concern for large enterprises and their networks due to the substantial
amount of sensitive information they handled. However, with the rise of AI
programs, the landscape has evolved. AI, speci몭cally generative AI relies
heavily on data for training and decision-making, making it vulnerable to
potential security risks. Many AI initiatives have overlooked the signi몭cance
of data integrity, assuming that pre-existing security measures are adequate.
However, this approach fails to consider the potential threat of targeted
malicious attacks on AI systems. Here are three compelling reasons
highlighting the critical need for data security in AI systems:
1. Threat of model poisoning: Model poisoning is a growing concern within AI
systems. This nefarious practice involves malicious entities introducing
misleading data into AI training sets, leading to skewed interpretations and,
potentially, severe repercussions. In earlier stages of AI development,
inaccurate data often led to misinterpretations. However, as AI evolves and
becomes more sophisticated, these errors can be exploited for more
malicious purposes, impacting businesses heavily in areas like fraud
detection and code debugging. Model poisoning could even be used as a
distraction, consuming resources while real threats remain unaddressed.
Therefore, comprehensive data security is essential to protect businesses
from such devastating attacks.
2. Data privacy is paramount: As consumers become increasingly aware of
their data privacy rights, businesses need to prioritize their data security
measures. Companies must ensure their AI models respect privacy laws and
demonstrate transparency in their use of data. However, currently, not all
companies communicate their data usage policies clearly. Simplifying privacy
policies and clearly communicating data usage plans will build consumer
trust and ensure regulatory compliance. Data security is crucial in preventing
sensitive information from falling into the wrong hands.

3. Mitigating insider threats: As AI continues to rise, there is an increased risk
of resentment from employees displaced by automation, potentially leading
to insider threats. Traditional cybersecurity measures that focus primarily on
external threats are ill-equipped to deal with these internal issues. Adopting
agile security practices, such as Zero Trust policies and time-limited access
controls, can mitigate these risks. Moreover, a well-planned roadmap for AI
adoption, along with transparent communication, can reassure employees
and o몭er opportunities for upskilling or transitioning to new roles. It’s crucial
to portray AI as an asset that enhances productivity rather than a threat to
job security.
Understanding the types of threats
As the application of arti몭cial intelligence becomes more pervasive in our
everyday lives, understanding the nature of threats associated with data
security is crucial. These threats can range from manipulation of AI models to
privacy infringements, insider threats, and even AI-driven attacks. Let’s delve
into these issues and shed some light on their signi몭cance and potential
impact on AI systems.
Model poisoning: This term refers to the manipulation of an AI model’s
learning process. Adversaries can manipulate the data used in training,
causing the AI to learn incorrectly and make faulty predictions or

classi몭cations. This is done through adversarial examples – input data
deliberately designed to cause the model to make a mistake. For instance,
a well-crafted adversarial image might be indistinguishable from a regular
image to a human but can cause an image recognition AI to misclassify it.
Mitigating these attacks can be challenging. Certain suggested protections
against harmful actions include methods like ‘adversarial training.’ This
technique involves adding tricky, misleading examples during the learning
process of an AI model. Another method is ‘defensive distillation.’ This
process aims to simplify the model’s decision-making, which makes it more
challenging for potential threats to 몭nd these misleading examples.
Data privacy: Data privacy is a major concern as AI systems often rely on
massive amounts of data to train. For example, a machine learning model
used for personalizing user experiences on a platform might need access
to sensitive user information, such as browsing histories or personal
preferences. Breaches can lead to exposure of this sensitive data.
Techniques like Di몭erential Privacy can help in this context. Di몭erential
Privacy provides a mathematical framework for quantifying data privacy by
adding a carefully calculated amount of random “noise” to the data. This
approach can obscure the presence of any single individual within the
dataset while preserving statistical patterns that can be learned from the
data.
Data tampering: Data tampering is a serious threat in the context of AI and
ML because the integrity of data is crucial for these systems. An adversary
could modify the data used for training or inference, causing the system to
behave incorrectly. For instance, a self-driving car’s AI system could be
tricked into misinterpreting road signs if the images it receives are altered.
Data authenticity techniques like cryptographic signing can help ensure
that data has not been tampered with. Also, solutions like secure multi-
party computation can enable multiple parties to collectively compute a
function over their inputs while keeping those inputs private.
Insider threats: Insider threats are especially dangerous because insiders
have authorized access to sensitive information. Insiders can misuse their

access to steal data, cause disruptions, or conduct other harmful actions.
Techniques to mitigate insider threats include monitoring for abnormal
behavior, implementing least privilege policies, and using techniques like
Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC)
to limit the access rights of users.
Deliberate attacks: Deliberate attacks on AI systems can be especially
damaging because of the high value and sensitivity of the data involved.
For instance, an adversary might target a healthcare AI system to gain
access to medical records. Robust cybersecurity measures, including
encryption, intrusion detection systems, and secure software development
practices, are essential in protecting against these threats. Also, techniques
like AI fuzzing, which is a process that bombards an AI system with random
inputs to 몭nd vulnerabilities, can help in improving the robustness of the
system.
Mass adoption: The mass adoption of AI and ML technologies brings an
increased risk of security incidents simply because more potential targets
are available. Also, as these technologies become more complex and
interconnected, the attack surface expands. Secure coding practices,
comprehensive testing, and continuous security monitoring can help in
reducing the risks. It’s also crucial to maintain up-to-date knowledge about
emerging threats and vulnerabilities, through means such as shared threat
intelligence.
AI-driven attacks: AI itself can be weaponized by threat actors. For
example, machine learning algorithms can be used to discover
vulnerabilities, craft attacks, or evade detection. Deepfakes, synthetic
media created using AI, are another form of AI-driven threats, used to
spread misinformation or conduct fraud. Defending against AI-driven
attacks requires advanced detection systems, capable of identifying subtle
patterns indicative of such attacks. Also, as AI-driven threats continue to
evolve, the security community needs to invest in AI-driven defense
mechanisms to match the sophistication of these attacks.

Unmatched data safety with LeewayHertz’s AI
development services
LeewayHertz follows stringent data security
measures at every step of the AI development
process, delivering robust and reliable solutions.
Learn More
The role of regulations and compliance of
data security in AI
Regulations and compliance play a crucial role in data security in AI systems.
They serve as guidelines and rules that organizations need to adhere to while
using AI and related technologies. Regulations provide a framework to follow,
ensuring that companies handle data responsibly, safeguard individual
privacy rights, and maintain ethical AI usage.
Let’s delve into some key aspects of how regulations and compliance shape
data security in AI systems:
Data protection: Regulatory measures like the General Data Protection
Regulation (GDPR) in the European Union and the California Consumer
Privacy Act (CCPA) in the United States enforce strict rules about how data
should be collected, stored, processed, and shared. Under these
regulations, organizations must ensure that the data they use to train and
run AI systems is properly anonymized or pseudonymized, and that data
processing activities are transparent and justi몭able under the legal
grounds set out in the regulations. Companies that violate these rules can
face heavy 몭nes, highlighting the role of regulation in driving data security
e몭orts.
Data sovereignty and localization: Many countries have enacted laws
requiring data about their citizens to be stored within the country. This can

pose challenges for global AI-driven services which may have to modify
their data handling and storage practices to comply with these laws.
Ensuring compliance can help prevent legal disputes and sanctions and
can encourage the implementation of more robust data security measures.
Ethical AI use: There’s an increasing push for regulations that ensure AI
systems are used ethically, in a manner that respects human rights and
does not lead to discrimination or unfairness. These regulations can
in몭uence how AI models are developed and trained. For example, AI
systems must be designed to avoid bias, which can be introduced into a
system via the data it is trained on. Regulatory compliance in this area can
help prevent misuse of AI and enhance public trust in these systems.
Auditing and accountability: Regulations often require organizations to be
able to demonstrate compliance through audits. This need for
transparency and accountability can encourage companies to implement
more robust data security practices and to maintain thorough
documentation of their data handling and AI model development
processes.
Cybersecurity standards: Certain industries, like healthcare or 몭nance,
have speci몭c regulations concerning data security, such as the Health
Insurance Portability and Accountability Act (HIPAA) in the U.S., or the
Payment Card Industry Data Security Standard (PCI-DSS) globally. These
regulations outline strict standards for data security that must be adhered
to when building and deploying AI systems.
Overall, regulatory compliance plays a fundamental role in ensuring data
security in AI. It not only provides a set of standards to adhere to but also
encourages transparency, accountability, and ethical practices. However, it’s
crucial to note that as AI technology continues to evolve, regulations will
need to keep pace to e몭ectively mitigate risks and protect individuals and
organizations.
Principles for ensuring data security in AI

systems
In the realm of Arti몭cial Intelligence (AI), data security principles are
paramount. Let’s consider several key data security controls, including
encryption, Data Loss Prevention (DLP), data classi몭cation, tokenization, data
masking, and data-level access control.
Encryption
Numerous regulatory standards, like the Payment Card Industry Data
Security Standard (PCI DSS) and the Health Insurance Portability and
Accountability Act (HIPAA), require or strongly imply the necessity of data
encryption, whether data is in transit or at rest. However, it’s important to
use encryption as a control based on identi몭ed threats, not just compliance
requirements. For example, it makes sense to encrypt mobile devices to
prevent data loss in case of device theft, but one might question the
necessity of encrypting data center servers unless there’s a speci몭c reason
for it. It becomes even more complex when considering public cloud
instances where the threat model might involve another cloud user, an
attacker with access to your instance, or a rogue employee of the cloud
provider. Implementations of encryption should, therefore, be dependent on
the speci몭c threat model in each context, not simply treated as a compliance
checkbox.
Data Loss Prevention (DLP)
Data Loss Prevention (DLP) is another important element in data security.
However, the e몭ectiveness of DLP often stirs up debate. Some argue that it
only serves to prevent accidental data leaks by well-meaning employees,
while others believe it can be an e몭ective tool against more malicious
activities. While DLP is not explicitly required by any compliance documents,
it is commonly used as an implied control for various regulations, including
PCI DSS and GDPR. However, DLP implementation can be a complex and
operationally burdensome task. The nature of this implementation varies
greatly based on the speci몭c threat model, whether it’s about preventing

accidental leaks, stopping malicious insiders, or supporting privacy in cloud-
based environments.
Data classi몭cation
Data classi몭cation is pivotal in AI data security, enabling the identi몭cation,
marking, and protection of sensitive data types. This categorization allows for
the application of robust protection measures, such as stringent encryption
and access controls. It aids in regulatory compliance (GDPR, CCPA, HIPAA),
enabling e몭ective role-based access controls and response strategies during
security incidents. Data classi몭cation also supports data minimization,
reducing the risk of data breaches. In AI, it improves model performance by
eliminating irrelevant information and enhances accuracy. Importantly, it
ensures the right protection measures for sensitive data, reducing breach
risk while preserving data integrity and con몭dentiality.
Tokenization
Tokenization enhances AI data security by replacing sensitive data with non-
sensitive ‘tokens’. These meaningless tokens secure data, making it unusable
to unauthorized individuals or systems. In case of a breach, tokenized data
remains safe without the original ‘token vault’. Tokenization also ensures
regulatory compliance, reducing the scope under regulations like PCI DSS.
During data transfer in AI systems, tokenization minimizes risk by ensuring
only tokens and not actual sensitive data, are processed. It helps maintain
privacy in AI applications dealing with sensitive data. It also allows secure
data analysis, transforming sensitive data into non-sensitive tokens without
altering the original format, ideal for AI models requiring training on large
sensitive datasets. Hence, tokenization is a powerful strategy in AI for
protecting sensitive data, ensuring compliance, reducing data breach risks,
and preserving data utility.
Data masking
Data masking is a security technique that replaces sensitive data with

scrambled or arti몭cial data while maintaining its original structure. This
method allows AI systems to work on datasets without exposing sensitive
data, ensuring privacy and aiding secure data analysis and testing. Data
masking helps comply with privacy laws like GDPR and reduces the impact of
data breaches by making actual data inaccessible. It also facilitates secure
data sharing and collaboration, allowing safe analysis or AI model training.
Despite concealing sensitive data, data masking retains the statistical
properties of the data, ensuring its utility for AI systems. Thus, it plays an
essential role in AI data security, regulatory compliance, and risk
minimization.
Data-level access control
Data-level access control is a pivotal security practice in AI systems, where
detailed policies de몭ne who can access speci몭c data and their permitted
actions, thus minimizing data exposure. It provides a robust defense against
unauthorized access, limiting potential data misuse. This method is
instrumental in achieving regulatory compliance with data protection laws
like GDPR and HIPAA. Features like auditing capabilities allow monitoring
data access and detecting unusual patterns, indicating potential breaches.
Furthermore, context-aware controls add another layer of security,
regulating access based on factors like location or time. In AI, it’s especially
useful when training models on sensitive datasets by restricting exposure to
necessary data only. Therefore, data-level access control is vital for managing
data access, reducing breach risks, and supporting regulatory compliance.
Techniques and strategies for ensuring
data security
This section will delve into the array of techniques and strategies essential
for bolstering data security in AI systems, ensuring integrity, con몭dentiality,
and availability of sensitive information.
AI model robustness

AI model robustness, in the context of data security, refers to the resilience
of an AI system when confronted with variations in the input data or
adversarial attacks intended to manipulate the model’s output. Robustness
can be viewed from two perspectives: accuracy (ensuring that the model
provides correct results in the face of noisy or manipulated inputs) and
security (ensuring that the model isn’t vulnerable to attacks).
Here are a few techniques and strategies used to ensure AI model
robustness:
Adversarial training: This involves training the model on adversarial
examples – inputs that have been intentionally designed to cause the
model to make a mistake. By training on these examples, the model learns
to make correct predictions even in the face of malicious inputs. However,
adversarial training can be computationally expensive and doesn’t always
ensure complete robustness against unseen attacks.
Defensive distillation: In this technique, a second model (the ‘student’) is
trained to mimic the behavior of the original model (the ‘teacher’), but with
a smoother mapping of inputs to outputs. This smoother mapping can
make it more di몭cult for an attacker to 몭nd inputs that will cause the
student model to make mistakes.
Feature squeezing: Feature squeezing reduces the complexity of the data

that the model uses to make decisions. For example, it might reduce the
color depth of images or round o몭 decimal numbers to fewer places. By
simplifying the data, feature squeezing can make it harder for attackers to
manipulate the model’s inputs in a way that causes mistakes.
Regularization: Regularization methods, such as L1 and L2, add a penalty
to the loss function during training to prevent over몭tting. A more robust
model is less likely to be in몭uenced by small changes in the input data,
reducing the risk of adversarial attacks.
Privacy-preserving machine learning: Techniques like di몭erential privacy
and federated learning ensure that the model doesn’t leak sensitive
information from the training data, thereby enhancing data security.
Input validation: This involves adding checks to ensure that the inputs to
the model are valid before they are processed. For example, an image
classi몭cation model might check that its inputs are actually images and
that they are within the expected size and color range. This can prevent
certain types of attacks where the model is given inappropriate inputs.
Model hardening: This is the process of stress testing an AI model using
di몭erent adversarial techniques. By doing so, we can discover
vulnerabilities and 몭x them, thereby making the model more resilient.
These are just a few of the methods used to improve the robustness of AI
models in the context of data security. By employing these techniques, it’s
possible to develop models that are resistant to adversarial attacks and that
maintain their accuracy even when they are fed noisy or manipulated data.
However, no model can ever be 100% secure or accurate, so it’s important to
consider these techniques as part of a larger security and accuracy strategy.
Secure multi-party computation

Secure Multi-party Computation (SMPC) is a sub몭eld in cryptography focused
on enabling multiple parties to compute a function over their inputs while
keeping those inputs private.
SMPC is a crucial method for ensuring data security in scenarios where
sensitive data must be processed without being fully disclosed. This could be
for reasons like privacy concerns, competitive business interests, legal
restrictions, or other factors.
Here is a simpli몭ed breakdown of how SMPC works:
Input secret sharing: Each party starts by converting their private input into
a number of “shares,” using a cryptographic method that ensures the
shares reveal no information about the original input unless a certain
number of them (a threshold) are combined. Each party then distributes
their shares to the other parties in the computation.
Computation: The parties perform the computation using the shares,
instead of the original data. Most importantly, they do this without
revealing the original inputs. Computation is generally done using addition
and multiplication operations, which are the basis for more complex
computations. Importantly, these operations are performed in a way that
preserves the secrecy of the inputs.
Result reconstruction: After the computation has been completed, the
parties combine their result shares to get the 몭nal output. Again, this is
done in such a way that the 몭nal result can be computed without revealing
any party’s individual inputs unless the predetermined threshold is met.
SMPC’s core principle is that no individual party should be able to determine
anything about the other parties’ private inputs from the shares they receive
or from the computation’s 몭nal output. To ensure this, SMPC protocols are
designed to be secure against collusion, meaning even if some of the parties
work together, they still can’t discover other parties’ inputs unless they meet

work together, they still can’t discover other parties’ inputs unless they meet
the threshold number of colluders. In addition to its use in privacy-preserving
data analysis, SMPC has potential applications in areas like secure voting,
auctions, privacy-preserving data mining, and distributed machine learning.
However, it’s important to note that SMPC protocols can be complex and
computationally intensive, and their implementation requires careful
attention to ensure security is maintained at all stages of the computation.
Moreover, SMPC assumes that parties will follow the protocol correctly;
violations of this assumption can compromise security. As such, SMPC should
be part of a broader data security strategy and needs to be combined with
other techniques to ensure complete data protection.
Di몭erential privacy
Di몭erential privacy is a system for publicly sharing information about a
dataset by describing the patterns of groups within the dataset while
withholding information about individuals. It is a mathematical technique
used to provide guarantees that the privacy of individual data records are
preserved, even when aggregate statistics are published.
Here is how di몭erential privacy works:
Noise addition: The primary mechanism of di몭erential privacy is the

addition of carefully calculated noise to the raw data or query results from
the database. The noise is generally drawn from a speci몭c type of
probability distribution, such as a Laplace or Gaussian distribution.
Privacy budget: Each di몭erential privacy system has a measure called the
‘epsilon’ (ε), which represents the amount of privacy budget. A smaller
epsilon means more privacy but less accuracy, while a larger epsilon
means less privacy but more accuracy. Every time a query is made, some
of the privacy budget is used up.
Randomized algorithm: Di몭erential privacy works by using a randomized
algorithm when releasing statistical information. This algorithm takes into
account the overall sensitivity of a function (how much the function’s
output can change given a change in the input database) and the desired
privacy budget to determine the amount of noise to be added.
Here is the core idea: When di몭erential privacy is applied, the probability of a
speci몭c output of the database query does not change signi몭cantly, whether
or not any individual’s data is included in the database. This makes it
impossible to determine whether any individual’s data was used in the query,
thereby ensuring privacy. Di몭erential privacy has been applied in many
domains including statistical databases, machine learning, data mining, etc. It
is one of the key techniques used by large tech companies like Apple and
Google to collect user data in a privacy-preserving manner. For instance,
Apple uses di몭erential privacy to collect usage patterns of emoji, while
preserving the privacy of individual users.
However, it’s important to understand that the choice of epsilon and the
noise distribution, as well as how they are implemented, can greatly a몭ect
the privacy guarantees of the system. Balancing privacy protection with utility
(accuracy of the data) is one of the key challenges in implementing
di몭erential privacy.
Homomorphic encryption

Homomorphic encryption is a cryptographic method that allows
computations to be performed on encrypted data without decrypting it 몭rst.
The result of this computation, when decrypted, matches the result of the
same operation performed on the original, unencrypted data.
This o몭ers a powerful tool for data security and privacy because it means you
can perform operations on sensitive data while it remains encrypted, thereby
limiting the risk of exposure.
Unmatched data safety with LeewayHertz’s AI
development services
LeewayHertz follows stringent data security
measures at every step of the AI development
process, delivering robust and reliable solutions.
Learn More
Here’s a simple explanation of how it works:
Encryption: The data owner encrypts their data with a speci몭c key. This
encrypted data (ciphertext) can then be safely sent over unsecured
networks or stored in an untrusted environment, because it’s meaningless
without the decryption key.

without the decryption key.
Computation: An algorithm (which could be controlled by a third-party, like
a cloud server) performs computations directly on this ciphertext. The
homomorphic property ensures that operations on the ciphertext
correspond to the same operations on the plaintext.
Decryption: The results of these computations, still in encrypted form, are
sent back to the data owner. The owner uses their private decryption key
to decrypt the result. The decrypted result is the same as if the
computation had been done on the original, unencrypted data.
It’s important to note that there are di몭erent types of homomorphic
encryption depending on the complexity of operations allowed on the
ciphertext:
Partially Homomorphic Encryption (PHE): This supports unlimited
operations of a single type, either addition or multiplication, not both.
Somewhat Homomorphic Encryption (SHE): This allows limited operations
of both types, addition and multiplication, but only to a certain degree.
Fully Homomorphic Encryption (FHE): This supports unlimited operations
of both types on ciphertexts. It was a theoretical concept for many years
until the 몭rst practical FHE scheme was introduced by Craig Gentry in 2009.
Homomorphic encryption is a promising technique for ensuring data privacy
in many applications, especially cloud computing and machine learning on
encrypted data. However, the computational overhead for fully
homomorphic encryption is currently high, which limits its practical usage. As
research continues in this 몭eld, more e몭cient implementations may be
discovered, enabling broader adoption of this powerful cryptographic tool.
Federated learning

Federated learning is a machine learning approach that allows a model to be
trained across multiple decentralized devices or servers holding local data
samples, without exchanging the data itself. This method is used to ensure
data privacy and reduce communication costs in scenarios where data can’t
or shouldn’t be shared due to privacy concerns, regulatory constraints, or
simply the amount of bandwidth required to send the data.
Here is how federated learning works:
Local training: Each participant (which could be a server or a device like a
smartphone) trains a model on its local data. This means the raw data
never leaves the device, which preserves privacy.
Model sharing: After training on local data, each participant sends a
summary of their locally updated model (not the data) to a central server.
This summary often takes the form of model weights or gradients.
Aggregation: The central server collects the updates from all participants
and aggregates them to form a global model. The aggregation process
typically involves computing an average, though other methods can be
used.
Global model distribution: The updated global model is then sent back to
all participants. The participants replace their local models with the
updated global model.
Repeat: Steps 1-4 are repeated several times until the model performance
reaches a satisfactory level.

The main bene몭t of federated learning is privacy preservation since raw data
doesn’t need to be shared among participants or with the central server. It’s
especially useful when data is sensitive, like in healthcare settings, or when
data is large and di몭cult to centrally collect, like in IoT networks.
However, federated learning also presents challenges. There can be
signi몭cant variability in the number of data samples, data distribution across
devices, and the computational capabilities of each device. Coordinating
learning across numerous devices can also be complex.
For data security, federated learning alone isn’t enough. Additional security
measures, such as secure multi-party computation or di몭erential privacy,
may be used to further protect individual model updates during transmission
and prevent the central server from inferring sensitive information from
these updates.
Best practices for AI in data security
Be speci몭c to your need
To ensure data security in AI, it is crucial that only what is necessary is
collected. Adhering to the principle of “need to know” limits the potential
risks associated with sensitive data. By refraining from collecting
unnecessary data, businesses can minimize the chances of data loss or
breaches. Even when there is a legitimate need for data collection, it is
essential to gather the absolute minimum required to accomplish the task at
hand. Stockpiling excess data may seem tempting, but it signi몭cantly
increases the vulnerability to cybersecurity incidents. By strictly adhering to
the “take only what you need” approach, organizations can avoid major
disasters and prioritize data security in AI operations.
Know your data and eliminate redundant records
Begin by conducting a thorough assessment of your current data, examining
the sensitivity of each dataset. Dispose of any unnecessary data to minimize
risks. Additionally, take proactive measures to mitigate potential
vulnerabilities in retained data. For instance, consider removing or redacting

vulnerabilities in retained data. For instance, consider removing or redacting
unstructured text 몭elds that may contain sensitive information like names
and phone numbers. It is crucial to not only consider your own interests but
also empathize with individuals whose data you possess. By adopting this
perspective, you can make informed decisions regarding data sensitivity and
prioritize data security in AI operations.
Encrypt data
Applying encryption to your data, whether it’s static or in transit, may not
provide a foolproof safety net, but it usually presents a cost-e몭ective strategy
to boost the security of your network or hard disk should they become
compromised. Assuming that your work doesn’t necessitate exceptionally
high-speed applications, the negative e몭ects of encryption on performance
are no longer a signi몭cant concern. Thus, if you are handling con몭dential
data, enabling encryption should be your default approach.
The argument that encryption negatively impacts performance is losing its
relevance, as many modern, high-speed applications and services are
incorporating encryption as a built-in feature. For instance, Microsoft’s Azure
SQL Database readily provides this option. As such, the excuse of
performance slowdown due to encryption is increasingly being disregarded.
Opt for secure 몭le sharing services
While quick and simple methods of 몭le sharing might su몭ce for submitting
academic papers or sharing adorable pet photos, they pose risks when it
comes to distributing sensitive data. Therefore, it’s advisable to employ a
service that’s speci몭cally engineered for the secure transfer of 몭les. Some
individuals might prefer using a permission-regulated S3 bucket on AWS,
where encrypted 몭les can be securely shared with other AWS users, or an
SFTP server, which enables safe 몭le transfers over an encrypted connection.
However, even a simple switch to platforms such as Dropbox or Google Drive
can enhance security. Although these services are not primarily designed

with security as their key focus, they still o몭er superior fundamental security,
such as encrypting 몭les at rest, and more re몭ned access control compared to
transmitting 몭les via email or storing them on a poorly secured server.
For those seeking a higher level of security than Dropbox or Google can
provide, SpiderOak One is a worthy alternative. It o몭ers end-to-end
encryption for both 몭le storage and sharing, coupled with a user-friendly
interface and an a몭ordable pricing structure, making it accessible for nearly
everyone.
Ensure security for cloud services
Avoid falling into the trap of thinking that if the servers are managed by
someone else, there is no need for you to be concerned about security. In
fact, the reality is quite contrary – you need to be cognizant of numerous
best practices to safeguard these systems. You would bene몭t from going
through the recommendations provided by users of such services.
These precautions involve steps like enabling authentication for S3 buckets
and other 몭le storage systems, fortifying server ports so that only the
necessary ones are open, and restricting access to your services solely to
authorized IP addresses or via a VPN tunnel.
Practice thoughtful sharing
When dealing with sensitive information, it is recommended to assign access
rights to individual users (be they internal or external) and speci몭c datasets,
rather than mass authorization. Further, access should only be granted when
absolutely necessary. Similarly, access should be provided only for speci몭c
purposes and durations.
It is also bene몭cial to have your collaborators sign nondisclosure and data
usage agreements. While these may not always be rigorously enforced, they
help set clear guidelines for how others should handle the data to which you
have granted them access. Regular log checks are also crucial to ensure that
the data is being used as intended.

Ensure holistic security: Data, applications, backups,
and analytics
Essentially, every component interacting with your data needs to be
safeguarded. Failure to do so may result in futile security e몭orts, for
instance, creating an impeccably secure database that is compromised due
to an unprotected dashboard server caching that data. Similarly, it’s crucial to
remember that system backups often duplicate your data 몭les, meaning
these backups persist even after the original 몭les are removed – the very
essence of a backup.
Therefore, these backups need to be not only defended, but also discarded
when they have served their purpose. If neglected, these backups may turn
into a hidden cache for hackers – why would they struggle with your
diligently maintained operational database when all the data they need
exists on an unprotected backup drive?
Ensure no raw data leakage in shared outputs
Certain machine learning models encapsulate data, such as terminologies
and expressions from source documents, within a trained model structure.
Therefore, inadvertently, sharing the output of such a model might risk
disclosing training data. Similarly, raw data could be embedded within the
몭nal product of dashboards, graphs, or maps, despite only aggregate results
being visible at the surface level.
Even if you’re only distributing a static chart image, bear in mind that there
exist tools capable of reconstituting original datasets, so never assume that
you’re concealing raw data merely because you’re not sharing tables. It’s vital
to comprehend what precisely you’re sharing and to anticipate potential
misuse by ill-intentioned individuals.
Understanding privacy impacts of correctly ‘de-
identifying’ data
Eliminating personally identi몭able information (PII) from a dataset, especially

when you don’t need it, is an e몭ective way to mitigate the potential fallout of
a data breach. Moreover, it’s a crucial step to take prior to making data
public. However, erasing PII doesn’t necessarily shield the identities in your
dataset. Could your data be re-associated with identities if matched with
other data? Are the non-PII attributes distinct enough to pinpoint speci몭c
individuals?
A simple hashing method might not su몭ce. For instance, one might receive a
supposedly “anonymized” consumer data 몭le only to identify oneself swiftly
based on a unique blend of age, race, gender, and residential duration in a
Census block. With minimal e몭ort, one could potentially discover records of
many others. Pairing this with a publicly available voter registration 몭le could
enable you to match most records to individuals’ names, addresses, and
birth dates.
While there isn’t a 몭awless standard for de-identi몭cation, if privacy protection
is a concern and you’re relying on de-identi몭cation, it’s strongly
recommended to adhere to the standards laid out by the Department of
Health and Human Services for de-identifying protected health information.
Although this doesn’t guarantee absolute privacy protection, it’s your best
bet for maintaining useful data while striving for maximum privacy.
Understand your potential worst-case outcomes
Despite all the preventative measures, complete risk eradication is
impossible. Thus, it’s essential to contemplate the gravest possible
consequences if your data were to be breached. Having done that, revisit the
몭rst and second points. Despite all e몭orts to prevent breaches, no system is
impervious to threats. Therefore, if the potential risks are unacceptable, it’s
best not to retain sensitive data to begin with.
Future trends in AI data security
Technological advancements for enhanced data security
As data security and privacy have taken center stage in today’s digital

landscape, emergence of several transformative technological advancements
is leading the trend. Based on Forrester’s analysis, key innovations include
Cloud Data Protection (CDP) and Tokenization, which protect sensitive data
by encrypting it before transit to the cloud and replacing it with randomly
generated tokens, respectively. Big Data Encryption further forti몭es
databases against cyberattacks and data leaks, while Data Access
Governance o몭ers much-needed visibility into data locations and access
activities.
Simultaneously, Consent/Data Subject Rights Management and Data Privacy
Management Solutions address personal privacy concerns, ensuring
organizations manage consent and enforce individuals’ rights over shared
data while adhering to privacy processes and compliance requirements.
Advanced techniques such as Data Discovery and Flow Mapping, Data
Classi몭cation, and Enterprise Key Management (EKM) play pivotal roles in
identifying, classifying, and prioritizing sensitive data, and managing diverse
encryption key life-cycles. Lastly, Application-level Encryption provides
robust, 몭ne-grained encryption policies, securing data within applications
before database storage. Each of these innovations serves as a crucial tool in
enhancing an organization’s data security framework, ensuring privacy,
compliance, and protection against potential cyber threats.
The role of blockchain technology in AI data security
At present, blockchain is recognized as one of the most robust technologies
for data protection. With the digital landscape rapidly evolving, new data
security challenges have emerged, demanding stronger authentication and
cryptography mechanisms. Blockchain is e몭ciently tackling these challenges
by providing secure data storage and deterring malicious cyber-attacks. The
global blockchain market is projected to reach approximately $20 billion by
2024, with applications spanning multiple sectors, including healthcare,
몭nance, and sports.
Distinct from traditional methods, blockchain technology has motivated

companies to reevaluate and redesign their security measures, instilling a
sense of trust in data management. Blockchain’s distributed ledger system
provides a high level of security, advantageous for establishing secure data
networks. Businesses in the consumer products and services industry are
adopting blockchain to securely record consumer data.
As one of this century’s signi몭cant technological breakthroughs, Blockchain
enables competitiveness without reliance on any third party, introducing new
opportunities to disrupt business services and solutions for consumers. In
the future, this technology is expected to lead global services across various
sectors.
Blockchain’s inherent encryption o몭ers robust data management, ensuring
data hasn’t been tampered with. With the use of smart contracts in
conjunction with blockchain, speci몭c validations occur when certain
conditions are met. Any data alterations are veri몭ed across all ledgers on all
nodes in the network.
For secure data storage, blockchain’s capabilities are unparalleled,
particularly for shared community data. Its capabilities ensure that no entity
can read or interfere with the stored data. This technology is also bene몭cial
for public services in maintaining decentralized and safe public records.
Moreover, businesses can save a cryptographic signature of data on a
Blockchain, a몭rming data safety. In distributed storage software, Blockchain
breaks down large amounts of data into encrypted chunks across a network,
securing all data.
Lastly, due to its decentralized, encrypted, and cross-veri몭ed nature,
blockchain is safe from hacking or attacks. Blockchain’s distributed ledger
technology o몭ers a crucial feature known as data immutability, which
signi몭cantly enhances security by ensuring that actions or transactions
recorded on the blockchain cannot be tampered with or falsi몭ed. Every
transaction is validated by multiple nodes on the network, bolstering the
overall security.

Endnote
In today’s interconnected world, trust is rapidly becoming an elusive asset.
With growing complexity of interactions within organizations, where human
and machine entities, including Arti몭cial Intelligence (AI) and Machine
Learning (ML) systems, are closely integrated, establishing trust presents a
considerable challenge. This necessitates an urgent and thorough
reformation of our trust systems, acclimatizing them to this dynamic
landscape.
In the forthcoming years, data will surge in importance and value. This rise
will inevitably draw the attention of hackers, intent on exploiting our data,
services, and servers. Furthermore, the very nature of cyber threats is
undergoing a transformation, with AI and ML enabled machines superseding
humans in orchestrating sophisticated attacks, making their prevention,
detection, and response considerably more complex.
In light of these evolving trends, data security’s importance cannot be
overstated. AI systems, owing to the fact that they deal in vast amounts of
data, are attractive targets for cyber threats. Therefore, integrating robust
security measures, such as advanced encryption techniques, secure data
storage, and stringent authentication protocols, into AI and ML systems
should be central to any data management strategy.
The future success of organizations will pivot on their commitment to data
security. Investments in AI and ML systems should coincide with substantial
investments in data security, creating a secure infrastructure for these
advanced technologies to operate. An organization’s dedication to data
security not only safeguards sensitive information but also reinforces its
reputation and trustworthiness. Only by prioritizing data security can we fully
unleash the transformative potential of AI and ML, guiding our organizations
towards a secure and prosperous future. It’s time to fortify our defenses and
ensure the safety and security of our data in the dynamic landscape of
arti몭cial intelligence.

arti몭cial intelligence.
Secure your AI systems with our advanced data security solutions or bene몭t from
expert consultations for data security in AI systems tailored to your needs.
Contact LeewayHertz today!
Author’s Bio
Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of
building over 100+ platforms for startups and enterprises allows Akash to
rapidly architect and design solutions that are scalable and beautiful.
Akash's ability to build enterprise-grade technology solutions has attracted
over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology
enthusiast, and an investor in AI and IoT startups.
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