AI and Machine Learning: Boosting Compliance and Preventing Spam

Some of the most advanced strategies in the current technology and analytics spaces include artificial intelligence and machine learning. These innovative approaches can hold nearly endless possibilities for technological applications: from the ability to eliminate manual work and enable software to make accurate predictions based on specific performance indicators.

In this way, it’s no surprise that AI and machine learning – utilized both individually and in conjunction with one another – are popping up in technologies that span industry sectors. As capabilities like these continue to bloom, it’s important for stakeholders and decision-makers to understand the ways in which these strategies can be leveraged within their business and the advantages they can provide.

To that end, let’s take a closer look at AI and machine learning and, specifically, the ways these approaches can support compliance with industry requirements and prevent spam messages.

AI and ML: Overlapping, but not interchangeable  

Before we delve into compliance and alleviating the problem of spam, it’s vital that technology and C-suite executives have a foundational understanding of AI and machine learning concepts. This is especially essential now that AI and machine learning overlap in more than a few areas and blur into the realm of interchangeable.

As TechRadar contributor Mike Moore noted, AI includes the use of robust algorithms to enable computers to complete tasks more accurately and efficiently than humans can, opening the door for automation and other key processes. AI allows hardware to “think for itself,” in a way, Moore explained.

Machine learning, on the other hand, takes this a step further and allows computers to not only complete tasks that used to require human intervention, but to also learn and advance based on the experiences of these tasks and the data used to complete them.

Technology expert Patrick Nguyen summed it up nicely for Adweek.

“AI is any technology that enables a system to demonstrate human-like intelligence,” Nguyen said. “Machine learning is one type of AI that uses mathematical models trained on data to make decisions. As more data becomes available, ML models can make better decisions.”

In this way, while AI and machine learning are often discussed in connection with one another, they are not identical concepts.

Overall, 15 percent of businesses currently use AI, while another 31 percent plan to use it within the next year, CME reported. Additionally, 47 percent of digitally mature companies already have an AI strategy in place.

Rising usage is also being seen with machine learning – The Enterprisers Project noted that 90 percent of business leaders agree that automation supported by machine learning will boost accuracy and decision-making. What’s more, 27 percent of executives have hired individuals with intelligent machine expertise to support their machine learning initiatives.

As AI and machine learning continue to emerge across enterprise software and important business strategies, it’s imperative to understand the difference between them and the use cases for these capabilities. Let’s examine a few examples, including AI’s use in compliance and how machine learning can help identify and eliminate spam.

Automation continues to provide companies of all kinds a means to modernize their businesses.

AI for GDPR compliance

The EU’s new data privacy measure, the General Data Protection Regulation (GDPR), went into effect in spring 2018 and left many businesses scrambling to shore up security according to its requirements. This fact was recently demonstrated through the use of an AI tool created by the European University Institute, which analyzed the privacy policies of 14 top technology companies. The tool, dubbed Claudette, showed that despite updates aligned with GDPR, organizations are still having trouble achieving compliance.

“One month after the GDPR was enforced, these were the results: from the total privacy policy sentences they evaluated, 11 percent were marked as unclear while 33.9 percent were identified as potentially problematic or provided insufficient information,” Trend Micro explained. “According to their report, none of the analyzed privacy policies meet the requirements of the GDPR.”

While the purpose of the report was to get a glimpse into how businesses are progressing with their compliance, the use of the AI Claudette tools also provides hope for the ways in which advanced capabilities like this can be used in the future. Currently, Claudette is only used in experimental capacities, but this could blaze a path for the use of AI to analyze compliance with GDPR and other industry regulations.

Machine learning to fight spam

Industry compliance is a continual challenge and important initiative for businesses. Another pressing problem is the persistence of spam messages, which, according to Trend Micro researcher Jon Oliver, can be fought with the use of advanced machine learning.

As Oliver explained, the processes to prevent spam messages require a considerable amount of data, which supports the actual learning necessary to enable a machine to identify and defend against spam. This presents quite the challenge, particularly as spam creators become savvier and evolve from the use of plain text messages to attachments and other approaches.

Thankfully, Trend Micro has been leveraging machine learning capabilities within the Trend Micro Anti-Spam Engine (TMASE) and Hosted Email Security (HES) for more than a decade now, building up a massive repository of quality datasets to support machine learning.

“Employed alongside other antispam protection layers (for example, Email Reputation Services, IP Profiler, antispam composite engine), machine learning algorithms were used to correlate threat information and perform in-depth file analysis to catch and keep spam off enterprise networks,” Oliver explained. “The strategy for using machine learning in antispam engines involved the use of state-of-the-art models, trust on an iterative method to improve the model’s accuracy, and the collection of accurately labeled data, which is a crucial part of the process.”

In this way, with each new spam message, machine learning-enabled prevention measures were able to learn a little more about current spam processes and approaches. And with spam representing a key threat to network and overall enterprise security, supporting an advanced way to identify and block spam before it reaches recipients is a considerable boon for data security.

Through Trend Micro’s antispam approach, which incorporates machine learning in conjunction with other protection technologies, researchers found that 95 percent of spam messages were effectively identified and blocked.

AI and ML for security

Artificial intelligence and machine learning will continue to emerge in an array of different settings, but they can be particularly beneficial for industry compliance and information security. As expert researchers like Oliver pointed out, however, it’s important to leverage these advanced processes as part of a layered security approach that incorporates other, established safeguarding measures.

To find out more about how AI and machine learning can benefit your network and infrastructure security, as well as how Trend Micro leverages machine learning in our TMASE and HES solutions, connect with our protection experts today.

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