As employees increasingly use unsanctioned generative AI applications to boost their efficiency and productivity, many organizations have invested in eLearning modules and one-off training sessions that help their people understand what data they can and can’t share with the likes of Bard and ChatGPT.
However, at the same time, the recent Verizon DBIR shows that human error is still the number one cause of data breaches in 2024.
In other words, despite training efforts, employees aren’t learning.
For security teams everywhere, that means a one-off training session on generative AI security won’t be enough to prevent a catastrophic data breach.
Here, we’ll look at why security training programs often fail, offering tailored advice to help you boost generative AI data protection and learning outcomes.
Where security training goes wrong
Before you slash your cybersecurity training budget altogether, it’s crucial to note that effective cybersecurity training does have an important role to play in overall cybersecurity resilience. In fact, research shows that effective training reduces security-related risks by 70%.
Things go awry, however, when organizations treat security training like a tick-box exercise. Here’s why:
- Lack of repetition: Learning anything new requires repetition and time. Irregular training fails in this regard. As Harvard Business Review research shows, employees will only remember 10% of what you taught them in a single training session.
- Disengaging: Security training often misses the mark, dragging on with long sessions, outdated tech, and impersonal messages. It’s no wonder that one in five employees opt out of these sessions!
- Impersonal: Security training often takes a blanket approach, providing the same content to every employee with little personalization. However, each employee is different, with unique responsibilities and access privileges. Ignoring this fact can lead to confusion and information gaps.
Why you need active learning for generative AI
To effectively combat the risks of generative AI data leakage and create a culture of security, organizations must reimagine how they approach cybersecurity training by embracing active learning.
Unlike traditional training sessions, active learning integrates security education and awareness seamlessly into employees’ daily workflow, enabling them to learn quickly and in real-time.
A prime example of active learning in action is the password strength meter, which dynamically shifts from red to green based on password complexity. This simple nudge incentivizes users to achieve the green indicator by crafting robust passwords.
But active learning isn’t just for bolstering credentials. You can also use it to help your users make security conscious decisions whilst working in apps like ChatGPT and Bard.
Here’s a closer look at how active learning works step-by-step:
- Data classification and monitoring: Active learning solutions monitor sensitive data activity in real-time, looking for risky user behavior like unauthorized data sharing with ChatGPT.
- Prompt delivery: When a user attempts to share sensitive data with a generative AI application, the automated active learning solution will appear with a training prompt, advising the user on why their action is risky and encouraging them to correct their course.
- Remediation: If the user corrects their action, the training prompt will disappear. Otherwise, depending on the severity of the incident, it may automatically remediate the action to prevent a data leak.
- Repetition and gamification: The active learning solution continues to work autonomously and interact with users, gamifying the process of making security-conscious decisions while reinforcing a security-first mindset.
How to choose an active learning solution for generative AI
Active learning is, undoubtedly, the best way to enhance generative AI data security and reduce the risk of human error in tandem. However, not all active learning solutions are created equal.
Here’s what to look for:
ROI Metrics
For security teams, accurately quantifying investments has long been a proverbial thorn in the side. However, active learning changes the game. Best-in-class offerings like Polymer data loss prevention (DLP), for example, come equipped with robust data analytics capabilities that help to quantify ROI.
By capturing and tracking employee risk scores over time, organizations unlock invaluable metrics to measure the effectiveness of their awareness programs. As these scores steadily decline, stakeholders will see tangible evidence of the program’s impact, fostering greater buy-in and support.
Automation
In the fast-paced world of cybersecurity, time is of the essence. That’s why it’s crucial to seek out active learning solutions that minimize manual intervention. By automating security training, best-in-class active solutions free up valuable time for you and your team to focus on high-impact, strategic initiatives.
Polymer DLP, for example, works autonomously in the background of your generative AI applications, seamlessly integrating into your employee workflows and handling the heavy lifting on your behalf. With these automated features at your disposal, you can redirect your energy towards tackling complex challenges and driving innovation within your organization.
Data Loss Prevention (DLP)
While training can help to reduce human error, you still want to be protected against the worst-case scenario. That’s why you should look for an active learning solution that also features DLP embedded with natural language processing.
These solutions offer a sophisticated approach to safeguarding your organization’s sensitive data. By educating employees on security best practices and employing highly accurate, precise threat detection mechanisms, they instill a culture of security awareness and mitigate data leakage risks in real-time.
Ready to stop generative AI data leaks once and for all? Find out more about Polymer DLP’s active learning solution now.
