How AI Is Helping to Better Protect Customer Data and Privacy




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Annie Spratt/Unsplash

Artificial Intelligence is being used for a myriad of tasks today, including customer self-service bots, employee HR portals, virtual assistants, companion chatbots and robots, predictive analytics, and many other applications. This article will look at the ways that AI is being used to help protect customer privacy.

Consumers Are Concerned About Data Privacy

Many countries and states are passing, or have already passed, consumer data privacy legislation such as the GDPR, the CCPA, and the ePD. With vast amounts of personal data being accumulated across multiple devices on a daily basis, consumers have a right to know how their personal data is being collected, how it is being used, and how long it will be kept. 

A report by TechRepublic from August of 2021 revealed that 70% of businesses in the study actually increased the amount of personal data they collected, but 62% of business leaders that were polled felt that their brands should be doing more to protect customer data. On the consumer side of things, 86% indicated that they feel a growing concern about data privacy, 78% are fearful about the amount of data being collected, and 40% said that they don’t trust brands to use their data ethically. Clearly, concerns about data privacy are justified.

It’s not just the misuse of personal data by brands. Data breaches have also exposed millions of customers’ data to nefarious parties. A 2021 report from IBM and the Ponemon Institute revealed that customer personal data (name, email, password) was the most common type of data (44%) that was exposed in data breaches. Fortunately, the use of AI, security analytics and encryption were shown to reduce the cost of a breach, saving brands between $1.25 million and $1.49 million compared to those who were not using such technology. 

According to Eliano Marques, EVP of Data and AI at Protegrity, a data security company, AI is being used to accelerate the process of data identification to improve customer data privacy. “When companies have hundreds of databases in the cloud and on premises, identifying what needs to be protected is not a simple task. Due to the complexity of data ecosystems, it’s almost impossible to imagine the process of data identification without leveraging automation and AI. This is especially true for semi-structured data, documents, and images,” said Marques. 

Related Article: 4 Reasons Why Explainable AI Is the Future of AI

How AI Can Improve Compliance

Compliance with privacy legislation such as the GDPR is another area where AI is playing a huge role. “Enterprises can reap the benefits of AI by accelerating data privacy initiatives with AI-driven techniques to classify sensitive data across a data ecosystem in both ‘batch’ and/or real-time. The real-time use case is interesting especially if enterprises are looking for a solution that classifies sensitive data and immediately applies a type of protection such as tokenization,” Marques explained. 

A customer’s right to be forgotten offers an example of how AI could be leveraged to ensure compliance. “AI could also be used to search across an enterprise data ecosystem to identify specific individuals that had previously asked to be forgotten, a requirement specified under privacy regulations such as GDPR. In this scenario, a company can guarantee that a certain set of individuals are truly ‘forgotten’ across their data ecosystem — even down to the level of a PDF contract document, for example.”

Related Article: What Is Explainable AI (XAI)?

AI, Data and Privacy

Because AI is able to utilize large datasets for analysis, and can process big data in a reasonably short amount of time, it has the potential to be abused when it comes to privacy. AI can be used to identify, track and monitor people through multiple devices, including when they are at work, at home, or out in public. Personal data that has been anonymized can be de-anonymized based on inferences from other devices. 

AI is also able to use machine learning algorithms to predict or infer sensitive personal information from non-sensitive data. Keyboard typing patterns are able to be used to determine a person’s emotional states, and a person’s political views, sexual orientation, etc., can be determined. It is also able to use data as input to be sorted, scored, classified, evaluated, or ranked, which then facilitates discrimination against people for things such as credit, housing, employment, and more. Thankfully, AI also has the potential to be used to enhance privacy protection. 

Jared Stern, CEO of Uplift Legal Funding, told CMSWire that AI is being used to manage sensitive data, which ensures the highest levels of customer privacy as automation becomes more commonplace. “The possibility of human error is negated as human intervention is out of the question. Many companies are resorting to using AI as the system can handle sheer amounts of massive data. AI plays a significant role in streamlining interactions,” said Stern. “Privacy requests can be handled discretely by assessing the information available. Action can be taken based on the choice made by a user to delete such information from the system.”

AI as the Protector of Privacy

Many consumers are under the mistaken assumption that the more complex technology becomes, the greater the chances of the loss of their personal data. Although as we have mentioned, that is always one possibility, there are many opportunities to use AI and machine learning to decrease the chances of the misuse of data or data breaches.

“A leak of information in the wrong hands is avoided as AI serves as the one-stop for managing data. It can even help standardize privacy practices,” said Ruben Gamez, CEO and founder of SignWell, a B2B SaaS provider,. “AI is adept at making decisions based on trends and patterns that are found in extensive data. User access to sensitive data can be restricted if patterns display anomalies.”

“Data classification and federated learning are other areas where AI helps enhance customer privacy,” said Gamez. “Federated learning helps build new models on data from different sources. This can happen without sharing the original information.” As AI technology progresses, through the use of federated learning, customer data privacy does not have to be compromised. “There can be two banks that want to develop an algorithm for early detection of bad debts without compromising on the personal details of clients. Federated learning helps securely achieve the same. Data classification using AI helps reduce organizational silos and makes compliance easy.”

Brogan Renshaw shared with CMSWire that through the use of AI, customer data can be protected, as AI identifies and responds to data breaches more efficiently than traditional technologies. “AI technology utilizes a more efficient system as opposed to traditional security measures because of its automated features. AI protects data using behavior modelling in identifying malware and has automated measures to counter these attacks.” 

Rather than the limited amount of time humans are able to monitor and flag any issues they detect, AI has no such limitations. “Artificial intelligence has proven to be a big asset in protecting customer data because of its ability to constantly monitor network behavior and flag any anomalies it detects on a 24/7 basis,” said Renshaw.

Final Thoughts

Consumers today are aware of and concerned about the loss or misuse of their personal and private data, either intentionally by brands or through data breaches that expose their data. Although AI has the capability of being used nefariously or erroneously to expose consumer data, there are many opportunities for brands to use AI to reduce malware and data breaches, standardize privacy practices, and become more compliant with privacy regulations such as the GDPR.



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