Drinking from the Fire Hose: How CyberSaint is Using NLP to Enable Continuous Control Automation

The quote “safety is about protecting humans from machines, while cybersecurity is about protecting machines from humans” has never been more accurate. As bad actors rise with the expansion of digital transformation across every industry, data and sensitive information are at peak vulnerability. In this day and age, there need to be more GRC automation tools to take some of the burden off of security teams that go through controls manually. 

Ray Kurzweil, one of the modern thought leaders for artificial intelligence (AI) and machine learning (ML), has expanded on what embracing AI means. He says, “We are entering a new era. It’s a merger between human intelligence and machine intelligence that will create something bigger than itself. It’s the cutting edge of evolution on our planet. Human beings are a species that have undergone cultural and technological evolution, and it’s the nature of evolution to accelerate. Its powers grow exponentially, and that’s what we’re talking about.”

Human intelligence and machine intelligence are becoming increasingly intertwined, but there’s still a lot of room for evolution in the space of cybersecurity, specifically in how AI can influence governance risk management and compliance. We’ve delved into the flaws of continuous control monitoring before, but when we’re talking about continuous control monitoring or continuous control automation, what does that mean? What if, with natural language processing (NLP), we could automate a majority of the process, freeing up time, money, and a way to pave the way for company-wide innovation? 

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A new way to address threats 

There are a number AI-fueled cybersecurity solutions out there, and it has definitely become a buzzword in cyber. These GRC automation tools still tend to require human oversight and intervention. They do not achieve pure automation. In this industry, automation tends to encompass employees and security leaders getting texts or emails when controls need to be addressed or updated, but what if there were more powerful options that allowed for an automated system that could identify how threats endanger your current tech stack?

NLP’s ultimate objective is to “read,” decipher, and understand language that’s valuable to the end-user. Currently, there are several ways NLP is used in day-to-day life. Many are familiar with chatbots, or auto-complete in emails or texts. But there’s a gap in cybersecurity and integrated risk management where NLP could be used to inform risk and regulatory compliance. Since interactions between humans and machines are based on language processing, NLP allows organizations to process increasingly large amounts of data, granting them the ability to be more efficient, more risk cognizant, and more secure.

Incident detection and prediction is one area where humans can take advantage of AI. NLP used for risk and compliance requirements can identify overlaps in frameworks and data from an enterprise’s tech stack and use it to identify vulnerabilities in security infrastructure. 

Cybersecurity as an industry is shifting its stance when it comes to addressing threats. Reacting to breaches after they occur is no longer enough. When companies get “bombed” in these incidences, they lose revenue and trust. 

Threat intelligence with NLP-fueled automation can take vast amounts of data and understand not just the meaning of the words but can use millions of data points to identify a pattern that will aid in detecting threats. And it only continues to learn. It’s not a product that is always in danger of becoming obsolete, like legacy IT GRC solutions. It will evolve with us.

How to use NLP to bring Cybersecurity into the Future

Using NLP in a cyber risk strategy can increase cyber resilience. Since most risk assessments operate on textual information, NLP can connect the dots between different frameworks and standards against a risk register. It can inform business processes and mitigate risk through content analysis that efficiently tracks changes to regulatory requirements.

Many modern integrated risk management solutions require the use of multiple, segmented products, resulting in siloed information that can be difficult to explain, much less navigate. This is even more critical when a breach happens because it doesn’t allow CISO’s or higher-level executives to make decisions based on aggregated, real-time data and insights. When data breaches can happen in seconds, this kind of manual monitoring can make or break a situation when every decision takes hours due to manual tracking.

CyberStrong uses NLP technology to make sense of data coming out of a security tech stack, showing where and how various tools and solutions manage compliance programs across standards. NLP allows for improvements over time by learning from itself and becoming more efficient to enhance cybersecurity processes. The automation of assessments gives business leaders insight into real-time risk monitoring.

“Crosswalking” is a process where the NLP engine identifies keywords that map to specific controls and control actions. Currently, the process of crosswalking in many cybersecurity solutions is manual and inexact. NLP gives organizations the ability to leverage nascent data that’s coming out of a platform. When other cybersecurity companies discuss crosswalking it’s typically behind a closed door and no one knows how it happens, or what it does. Mapping different frameworks doesn’t always provide a direct 1:1 solution. So having an option for automation that is transparent, thorough, and learns, is critical in increasing maturity and understanding.

CybserSaint takes a deep learning approach that allows crosswalking between frameworks without a significant amount of human intervention. This increases security maturity and makes the organization more risk cognizant. This becomes even more key when discussing cloud-based shifts post COVID-19.

Teams that monitor risk must become aware of the changes that can happen minute to minute in an agile environment. Every environment needs to be supervised and evaluated. This kind of constant, manual assessment isn’t practical for companies to manage with employees alone. Humans are fallible, and it’s harder to discover gaps in security without a continuous auditing process that leverages automation to achieve its goals.

Conclusion

A reactive approach in the ever-changing digital landscape of cybersecurity is no longer enough. Manually sifting through spreadsheets to determine compliance processes when the result may no longer be relevant when the assessment is through wastes thousands of hours.

There is a simple solution: automation. When looking to the future, we can consider Ray Kurzweil’s words once more, “One thing that observers don’t fully recognize is the fact that the pace of change itself has accelerated. Centuries ago, people didn’t think that the world was changing at all. Their grandparents had the same lives that they did, and they expected their grandchildren would do the same, and that expectation was largely fulfilled. The 20th century was like 25 years of change at today’s rate of change. In the next 25 years, we’ll make four times the progress you saw in the 20th century. And we’ll make 20,000 years of progress in the 21st century, which is almost a thousand times more technical change than we saw in the 20th century.”

To bring AI-assisted automation into your organization, contact us. To learn more about how NLP is changing the landscape of cybersecurity, check out our webinar.