Home Artificial Intelligence Artificial Intelligence News Transforming Cyber Risk and Compliance with NLP

Transforming Cyber Risk and Compliance with NLP

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Cybersecurity as a business function can be a very complicated process for any organization. Legacy GRC and even some IRM products require manual configuration, often across multiple applications and clients, to record compliance initiatives. This configuration fatigue often leads to headaches and costly errors that can hurt your business and stunt your path to becoming compliant. This issue becomes amplified when considering many compliance and regulatory requirements, one may need to satisfy to maintain a healthy operation standard. With the ever-growing rise of cyber threats and bad actors, it’s more important than ever for companies to stay compliant. Artificial intelligence (AI) can address this problem, yet even this approach also has its caveats.

Even with many cyber security solutions that are AI-powered, they require human intelligence; and are not automated at their core. Typically, in cybersecurity, AI technology is used for IT asset inventory, intrusion detection/IoC detection, control effectiveness, breach risk prediction, and incident response. One thing that differentiates CyberStrong as a great example of an Integrated Risk Management solution is that it utilizes Natural Language Processing (NLP). NLP is categorized as a subset of Machine Learning (ML) and has excellent applications for cyber security professionals seeking to improve their compliance processes continuously.

As the branch of AI-based deep learning that deals with the interaction between humans and computers using natural everyday language, NLP offers a wealth of capabilities to augment human ability. NLP in risk and compliance can identify overlaps in standards and frameworks and data from an organization’s tech stack, and threat feeds to identify vulnerabilities in your security infrastructure. NLP’s ultimate objective is to “read,” decipher, and understand language that’s valuable to the end-user. In CyberStrong, NLP supports the need for automation across two of the most menial processes in risk and compliance: framework crosswalking and making security telemetry actionable from a risk and compliance perspective.

CyberStrong’s patented NLP technology makes sense of all the data coming out of a security tech stack, showing where and how various tools and solutions achieve compliance across standards. As a mode of AI, NLP also improves over time by learning from itself to become more efficient and enhance its cybersecurity processes. The automation of assessments is achieved by mapping telemetry to controls to operationalize threat and vulnerability information in real-time.

In automating the crosswalking process before unseen in the industry, the NLP engine identifies keywords in telemetry that map to specific controls and control actions. Currently, the process of crosswalking in many cybersecurity solutions is manual and inexact.

Organizations can make some use of their vulnerability information in many other integrated risk management solutions. Still, it typically requires the use of multiple, segmented products, resulting in siloed information that can be difficult to explain, much less navigate and maintain accuracy. CyberStrong’s AI solves this issue and is capable of harmonizing across all frameworks and standards. In addition to this, CyberStrong will soon be able to map multiple control actions to describe a specific control and automatically investigate if compliance requirements are met across other controls or frameworks. The continuous training of the NLP enables true harmonization across frameworks at the assessment level.

If you have any questions about AI in cybersecurity, NLP, or how integrated risk management may benefit your organization’s security teams, give us a call at 1 800 NIST CSF or click here to schedule a conversation.

October is National Cyber Security Awareness Month (NCSAM). Now in its 17th year, National Cybersecurity Awareness Month is a month-long campaign by the Cybersecurity and Infrastructure Security Agency (apart of the Department of Homeland Security) to raise awareness of the importance of cybersecurity for both individuals and organizations. This year’s theme “Do Your Part, #BeCyberSmart” seeks to acknowledge that especially in the rise of remote work, there is a shared responsibility to ensure that Americans stay safe and more secure online.

 

During this year’s NCSAM, CyberSaint is promoting the importance of cybersecurity in the face of digital transformation efforts. With more organizations than ever seeking to digitize their businesses, organizations must ensure that they are protecting their part of cyberspace as well as their employees and customer to be safer and more secure online.

Cybersecurity as a business function can be a very complicated process for any organization. Legacy GRC and even some IRM products require manual configuration, often across multiple applications and clients, to record compliance initiatives. This configuration fatigue often leads to headaches and costly errors that can hurt your business and stunt your path to becoming compliant. This issue becomes amplified when considering many compliance and regulatory requirements, one may need to satisfy to maintain a healthy operation standard. With the ever-growing rise of cyber threats and bad actors, it’s more important than ever for companies to stay compliant. Artificial intelligence (AI) can address this problem, yet even this approach also has its caveats.

 

Even with many cyber security solutions that are AI-powered, they require human intelligence; and are not automated at their core. Typically, in cybersecurity, AI technology is used for IT asset inventory, intrusion detection/IoC detection, control effectiveness, breach risk prediction, and incident response. One thing that differentiates CyberStrong as a great example of an Integrated Risk Management solution is that it utilizes Natural Language Processing (NLP). NLP is categorized as a subset of Machine Learning (ML) and has excellent applications for cyber security professionals seeking to improve their compliance processes continuously.

As the branch of AI-based deep learning that deals with the interaction between humans and computers using natural everyday language, NLP offers a wealth of capabilities to augment human ability. NLP in risk and compliance can identify overlaps in standards and frameworks and data from an organization’s tech stack, and threat feeds to identify vulnerabilities in your security infrastructure. NLP’s ultimate objective is to “read,” decipher, and understand language that’s valuable to the end-user. In CyberStrong, NLP supports the need for automation across two of the most menial processes in risk and compliance: framework crosswalking and making security telemetry actionable from a risk and compliance perspective.

CyberStrong’s patented NLP technology makes sense of all the data coming out of a security tech stack, showing where and how various tools and solutions achieve compliance across standards. As a mode of AI, NLP also improves over time by learning from itself to become more efficient and enhance its cybersecurity processes. The automation of assessments is achieved by mapping telemetry to controls to operationalize threat and vulnerability information in real-time.

In automating the crosswalking process before unseen in the industry, the NLP engine identifies keywords in telemetry that map to specific controls and control actions. Currently, the process of crosswalking in many cybersecurity solutions is manual and inexact.

Organizations can make some use of their vulnerability information in many other integrated risk management solutions. Still, it typically requires the use of multiple, segmented products, resulting in siloed information that can be difficult to explain, much less navigate and maintain accuracy. CyberStrong’s AI solves this issue and is capable of harmonizing across all frameworks and standards. In addition to this, CyberStrong will soon be able to map multiple control actions to describe a specific control and automatically investigate if compliance requirements are met across other controls or frameworks. The continuous training of the NLP enables true harmonization across frameworks at the assessment level.

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