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Equity and Responsibility


UNDERSTANDING THE ROLE OF PRIVACY AND SECURITY IN RESPONSIBLE INNOVATION

By Kristi Boyd on May 31, 2023 2 Comments


AS ORGANIZATIONS EMBRACE AI, THEY OFTEN HANDLE LARGE VOLUMES OF DATA THAT POWER
AI SYSTEMS. HANDLING DATA APPROPRIATELY INCLUDES IMPLEMENTING ADEQUATE PRIVACY
POLICIES AND SECURITY MEASURES TO PROTECT IT. DOING SO PREVENTS ACCIDENTAL
EXPOSURE AND ENSURES ETHICAL DATA USE.

AI technology often uses sensitive data for creating, training and utilizing
models. This data may contain private information about people, sensitive
business use cases, or health care data. Unauthorized access to or inappropriate
disclosure of these types of data can cause harm to individuals or
organizations. In response, governments have expanded privacy and security laws.
These laws protect individual data subjects' privacy and set up guardrails to
minimize sensitive data exposures and data breaches.

Read more stories in this series about data ethics principles

While privacy and security were previously associated with intellectual property
(IP) and cybersecurity, the definition and scope have expanded in recent
years.to encompass data access management, data localization and the rights of
data subjects. It’s important that organizations, especially those using or
building AI solutions, be aware of privacy and security best practices and
regulations.

Interestingly, existing privacy regulations overlap with upcoming AI
regulations, as both emphasize principles such as explainability, fairness and
security. In fact, a recent study found that more than 50% of organizations
develop their AI governance on top of existing privacy frameworks. These
organizations see parallels between keeping data secure and keeping AI models
well-governed. Responsible innovators understand the need to meet regulatory
requirements and respect the privacy and security of training data subjects.

Let‘s discuss three things organizations can do to make sure their data is more
private and secure.


1. DATA MASKING: RESPECTING THE PRIVACY OF SUBJECTS

A global study recently found that 68% of consumers are concerned with their
online privacy. While the study focused on data collection while browsing
online, the message is clear – consumers care about their data and how it is
collected, processed and used. For organizations processing data containing
personal or sensitive information, it is imperative to effectively protect data
subjects’ privacy and data masking can help.

Data masking is a technique used to protect sensitive information by
randomizing, substituting or reshuffling original data with similar values. In
other words, you’re “hiding” the data's values. This method ensures that the
data remains usable for testing, development or training purposes while
minimizing the risk of exposing sensitive information. Data masking can protect
addresses, names, social security numbers, intellectual property and financial
information, among other sensitive information.

Fig 1: SAS® Information Catalog Overview tab displaying Information Privacy Fig
2: Data masking using SAS® Studio custom tasks


2. AUTHORIZATION MANAGEMENT: WHO NEEDS TO KNOW?

Authorization management is integral to data hygiene and data management. In a
physical office, an organization may have spaces open to guests and visitors;
employees only; or special access required. If you have a business visitor, you
will likely hand out day visitor passes and invite them to the conference rooms
but not give them the building security codes. Authorization management is a
similar concept but in the digital domain.

Not everyone needs the same level of access when engaging with data and seeking
data-driven insights. In many cases, only select data scientists in your
organization need access to the full data set to find the most relevant and
enlightening insights. These insights can then be shared with the appropriate
employees without disclosing the underlying sensitive data. Authorization
management asks, “Does this person need to see the data, or do they just need
the insights?”

A good authorization management practice is to apply a least-privileges model
that grants each role the minimum access required to complete the tasks they
have on hand successfully. Similarly, setting up user groups to manage
appropriate rights can help restrict data usage on a need-to-know basis. This
approach ensures that those with a legitimate need only access sensitive
information. By limiting access in this way, organizations can improve their
data hygiene and protect sensitive information from those with unauthorized
access or disclosure.

Fig 3: SAS® Environment Manager CAS library authorization


3. SAFEGUARDING AGAINST EXTERNAL ATTACKS

Data masking and authorization management provide privacy to the data subjects
when their data is used within the organization. However, it is important not to
overlook the possibility of adverse external influences. Organizations are
exposed to increasing malicious attacks, which increases the importance of
having a private and secure environment.

One type of attack that organizations should be particularly wary of is
adversarial attacks. These attacks can muddy the data and introduce incorrect
variables in the machine learning models by fooling the models with deceptive
data and labeling. This can lead to incomplete or inaccurate data points, which
may harm not only the business but also the individual employees and users of
the system.

For these reasons and more, organizations should take active steps to mitigate
these external attacks by using adversarial training, detecting and cleaning
adversarial inputs, and implementing differential privacy in training data.
Additionally, monitoring input variables that exhibit substantial distribution
shifts could help defend against data poisoning attacks.


UPHOLDING ETHICAL STANDARDS

At SAS, we consider privacy and security principles essential to responsible
innovation. As a global company with operations in over 50 countries, we collect
personal data from our customers, prospects, partners, suppliers, applicants and
employees. To protect the data as a data processor and controller, we work with
multiple third-party and regulatory frameworks to stay on top of industry
standards and regulations.

With data masking, authorization management and proactive defense against
external attacks, organizations can create a private and secure environment to
protect data. These measures protect individuals and organizations and uphold
ethical standards and regulatory compliance in the AI landscape.


STAY INFORMED ABOUT WHERE YOUR DATA IS LOCATED, HOW IT IS USED AND HOW WE
PROTECT AND DEFEND YOUR DATA.

Kristi Boyd and Vrushali Sawant made contributions to this article

Tags data ethics stories innovation resiliency resiliency rules responsible
innovation Trustworthy AI
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ABOUT AUTHOR

Kristi Boyd
Trustworthy AI Specialist

Kristi Boyd is the Trustworthy AI Specialist with SAS' Data Ethics Practice
(DEP) and supports the Trustworthy AI strategy with a focus on the pre-sales,
sales & consulting teams. She is passionate about responsible innovation and has
an R&D background as a QA engineer and product manager. She is also a proud Duke
alumna (go Blue Devils!).


2 COMMENTS

 1. Deb Roughton on May 31, 2023 2:09 pm
    
    Thanks for the article Kristi! Great info
    
    Reply
 2. Franklin J Manchester on June 2, 2023 12:32 pm
    
    It's such a simple question to ask, "where's your data?" I wonder how many
    organizations cannot, fully answer that question.
    
    Reply


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