How AI and Machine Learning Is Securing Personal Information

By Kayla Matthews
Published 03/17/2020
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The security of personal information is a constant concern in today’s society. People give their details freely and willingly in the digital realm, whether they order products online or sign up to receive daily updates from news sites.

However, many are increasingly wary about their precious information falling into the wrong hands, mainly due to frequent and wide-scale data breaches.

Artificial intelligence (AI) and machine learning tools can make security lapses less likely to happen. Here are six compelling examples of what these technologies can do.

1. They Can Keep Email Platforms More Secure

Many people become victims of cybersecurity scams when they believe the content of misleading phishing emails. These messages cause individuals to give personal information to criminals. Several email services use machine learning and AI to detect phishing attempts and remove them from inboxes before people see them. Google, for instance, applies AI security to Gmail to screen for spam.

If a person never sees dangerous messages that request their details for dishonest reasons, they won’t provide the information to unauthorized parties.

2. They Bring More Security to Banking Transactions

Banks depend on AI and machine learning for a variety of reasons, including to enhance security. For example, a cybersecurity machine learning tool can understand what constitutes normal behavior for a customer and immediately lock down their account when detecting suspicious activity.

Moreover, some machine learning tools work in ways that customers never experience. Solutions exist to aid banking brands in staying compliant and adhering to all the stipulations those entities must follow when working with or storing customer information.

3. They Give Apps Built-In Threat Protection

Many apps have AI and machine learning running in the background to protect sensitive data on any device. These tools offer users peace of mind, especially since cybersecurity machine learning solutions are powerful but don’t interfere with the experience of using an app.

AI security also appears in tools that scan apps for malware that leaks user data. Unfortunately, such apps are more common than many people realize. Plus, nothing about the software from the outside tips people off to the potential dangers. The smart tools that look for malware-riddled programs generally trigger the removal of such downloadable content from online marketplaces. They aren’t perfect, but engineers make continual improvements as new or likely threats arise.

4. They Let People Know How Sites Handle Data

The data-related policies published by most websites are not exactly light reading. Many people who sign up for things skim them or don’t read the documents at all before registering. That approach could mean that users give up more private details than they realize. However, it’s now easier for anyone to get familiar with privacy policies in an efficient yet thorough way.

Researchers developed an AI-powered tool that can tell users which sites they visit may sell their data. The team named it Polisis and made the resource available via an official website and a browser extension. Once a person searches for a company or website, they get a breakdown of the positive and negative aspects of their privacy policy. They can also see how a site stacks up regarding something specific, such as data retention or the treatment of information related to children.

Guard is another option. It’s built with AI security enhancements and provides users with letter grades indicating how well sites do at keeping data safe and avoiding breaches. A is excellent, and F indicates failure.

5. They Improve Identity and Access Management

Identity and access management ensures people can get the information they need without seeing personal details or sensitive materials that do not relate to their jobs. It can be instrumental in stopping the intentional or accidental misuse of personal data.

Applying AI to assist with the task is becoming a popular choice in many sectors, such as health care. Humans still set access permissions, but a machine learning security platform can verify that the people responsible for doing it did not overlook anything.

AI could also be advantageous when a person leaves an organization to prevent any former access points from remaining open. Alternatively, if a person receives a promotion to a job that offers more permissions, an AI-powered tool could make those tweaks quickly and without errors.

6. They Aid Companies in Finding Sensitive Information 

One of the facets of the General Data Protection Regulation (GDPR) is the right to be forgotten. People can contact businesses and ask them to delete the data they hold about them. Meeting those requests isn’t always easy, but companies must do it within a short timeframe. AI and machine learning options are available to meet that need.

Text IQ is a machine learning-driven tool that helps companies find sensitive details about customers quickly and accurately. It started as a technique for legal professionals to find evidence or other specific information. However, it’s easy to see why the technology can meet a broader need.

MinerEye also caters to data privacy needs in the age of GDPR. It assists businesses in classifying data correctly according to what the regulations require. The tool’s interface also highlights compliance incidents. Then, company representatives can take quick action to remedy those problems and avoid fines or related issues.

Exciting Possibilities to Improve Cyber Security With Machine Learning and AI

These information security applications are not the only ways to use AI and machine learning. However, they’re worthwhile options in a world where data breaches and other types of misuse are increasingly damaging to everyone involved.

Kayla Matthews writes about technology, the IoT, FutureTech and big data. Previously, her work has been featured on IoT Times, InformationWeek, The Daily Dot and IBM’s Big Data Hub blog. To read more of Kayla’s work, please follow her personal tech blog at