Marketing is powered by technology. The vast amount of data at the marketer’s disposal provides unparalleled insight into what customers want, why they want it, and how they use their products and services . Behavioral analytics benefits businesses and consumers. This allows businesses to increase sales and increase conversion rates while providing services that are tailored to their customers’ wants and needs.
Behavioral analytics is also an invaluable cybersecurity resource. Artificial intelligence (AI) and machine learning (ML) tools analyze data and help security teams identify suspicious behavioral patterns that could indicate malicious activity.
However, the use of behavioral analytics comes with significant data privacy concerns. Excessive data collection is not only based on shaky ethical grounds, it can also bog the organization down in regulation. This article outlines how organizations can reap the benefits of behavioral analytics without compromising user privacy.
What is Behavioral Analysis?
Behavioral analytics involves collecting and analyzing data about how consumers use digital products such as apps and websites. Organizations can use this data to see exactly how users are interacting with digital services and make decisions about how to improve them.
Much of behavioral analytics is event-based, where organizations track behaviors and events to generate insights about user preferences, intentions, and habits. These events include, but are not limited to:
- abandon cart
- fill out the form
- Function usage
- Purchase a subscription
- Retention
Behavioral analytics relies on a combination of third-party and first-party data. Data brokers typically collect third-party data and sell it to other organizations. This information establishes who the prospect or customer is, not what they want. First-party data refers to the use of an individual’s unique digital product or service. By combining first-party and third-party data, organizations can establish who their customers are and what they want.
Behavioral analytics in the context of cybersecurity involves collecting data such as network traffic and access logs, database user activity records, and departmental usage habits, and transforming them into a format that AI or ML tools can understand. It involves establishing what normal behavior looks like. From there, AI or ML tools detect and flag behavior that deviates from the norm. For example, an employee tries to access or extract data without good reason. If so, AI and ML tools flag this as a potential insider threat and notify security teams. The best AI or ML tools go further and automatically prevent unauthorized data exfiltration.
Privacy implications of behavioral analytics
Personal data is subject to some of the most stringent regulatory standards on the planet. Just this year, the EU fined Facebook owner Meta €1.2 billion, the largest amount in GDPR history, for illegally transferring personal data from the EU to the US. Additionally, employees may view excessive internal behavioral monitoring as an invasion of privacy. Given that nearly half of Americans would prefer not to give their jobs to their worst enemy, organizations are advised to act cautiously to avoid going too far and prompting mass resignations. To do.
Organizations should be familiar with applicable regulatory standards to avoid misuse of consumer data. The General Data Protection Regulation (GDPR) is the broadest data privacy law and applies to all organizations that handle data of European Union (EU) citizens. There is currently no federal equivalent of GDPR in the United States, but lawmakers are increasingly introducing data privacy regulations at the state level. California, Colorado, Connecticut, and Utah have detailed and extensive data privacy laws. Organizations should also be aware of industry-specific regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the Gramm-Leach-Bliley Act (GLBA).
Self-control and respect are paramount for organizations seeking to conduct behavioral analysis for cybersecurity purposes. Naturally, the employee would be apprehensive about the prospect of her being monitored 24 hours a day. Organizations need to be clear that behavioral analysis is strictly for security purposes and not for evaluating productivity or efficiency. Additionally, organizations should ensure that behavioral data is inaccessible to anyone outside of the security team, especially outside of the human resources team.
The key here is that security solutions cannot rely solely on behavioral analysis. Data analytics should also be used. Behavioral analysis alone can generate a myriad of false positives. For example, if an employee brings up a picture of a colleague’s dog, behavioral analytics solutions will flag it as a potential insider threat because it cannot distinguish between non-critical corporate data and sensitive corporate data. Organizations should seek solutions that combine data and behavioral analytics to avoid false positives and potentially unnecessary and invasive investigations.
Walking the line between effective behavioral analysis and invasion of privacy requires an informed, restrained approach. Organizations need to ensure they have a good understanding of behavioral analytics, how it benefits them, and their privacy implications. A comprehensive understanding of relevant data privacy regulations is critical, and organizations must take a thoughtful approach to internal behavioral analysis to avoid compromising employee privacy.
Editor’s Note: The opinions expressed in this guest author article are those of the contributor only and do not necessarily reflect those of Tripwire.
