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9 Considerations for Building an AI-based Model for Cybersecurity In April 2019, Consumer Technology Association carried out a survey which revealed that the major utility of AI model in 2018 was for cybersecurity purposes. Specifically, an estimate of 44% of the total AI applications were employed in detecting and deterring security intrusions. Furthermore, another survey carried out by Capgemini in July 2019, reported that over ⅔ of respondents claimed that the present threat environment indicates that AI will be indispensable in tackling future cyberattacks. It is therefore indisputable that the role of AI in cybersecurity is crucial. If you're starting to ponder: "how does AI work in cybersecurity and how can I implement this in my business?" The answer lies in the rest of this article, as we will be discussing 9 standard considerations for establishing an AI-based model for your cybersecurity needs. Enjoy! How Does AI Work in Cybersecurity 1. Evaluate Objectives and Possible Threats For as many companies that are willing to embrace the challenges - both seen and unseen, that are associated with AI, you should know that it offers immense potential for proactive approaches, accurately targeted resource distribution, and predictive insight that will enable you to perfect your security strategies. AI also offers complex detection functionalities, decreases professional "alert tiredness", and assists in the augmentation of security departments. However, before the implementation of AI in cybersecurity begins, it is necessary for businesses to identify factors necessary for its success, risks associated with the process, and expectations after the project. 2. Create a Strong Foundation In order to establish a remarkable foundation, it is pertinent to understand the basics of the system. They include: visibility, administration, storage and analysis, and workflows. • Visibility To begin with, an established IT Asset Management Program must be used to provide accountability for all assets on the system. By understanding the components of the system, you are able to recognize and respond to cybersecurity threats, as well as making sure your AI model is utilizing the appropriate data. • Administration Thereafter, multiple data feeds are needed for the most effective operationalized AI use cases. • Storage and Analysis After the data undergoes standardization, a data broker such as RabbitMQ or Kafka is used to help migrate data out of existing security networks to sites where complex evaluation takes place. • Workflows Finally, it is important for companies to develop well-defined and highly organized workflows and procedures that stretch beyond the security department. This will make it easier to launch the AI model across your company. 3. Pay Attention to The Human Factor AI augments human effort by assisting analysts in the automation of labor-demanding processes, fast tracking evaluation, and reduction of errors. To organize, manage and develop the human factor of AI in cybersecurity, the following questions should be considered: • What tasks would you automate and which would you retain in human hands? • How do you intend to introduce emerging roles into the business? These might comprise employees and cyber data analysts who supervise machine learning models or advocate ML integration into the system. • What preparations doses your company have to enlighten employees on the overall concept of AI? (e.g., hack-a-thons, podcasts, webinars, online tutorials etc.) 4. Concentrate on Use Cases To alleviate risk and maximize the success of an AI model launching, companies are advised to concentrate on the implementation of AI-based use cases. This should be carried out in the initial stage in any wider AI adoption initiative. 5. Automating and Arranging for Fast ROI After observing the first 4 steps above, the next thing is for organizations to automate their processes and permit analysts to change the focus of their efforts. According to the Security Magazine, about 75% of companies are aware of the advantages of automation, however nearly a third of them have been unable to successfully implement resource-saving automation programs. Certain areas like traffic patterns, phishing email header analysis, vulnerability scoring, and intelligence collection are key areas to start with for high automation and AI model use cases for a fast ROI. 6. Ensure Organizational Maturity Next, it is significant for you to make sure your organization has a well outlined security detailing. This should include considering the basics, and also the more sophisticated threats. It is necessary to have this in place before embarking on the launch of an AI model. To ensure organizational maturity, you need to start thinking of how you can augment the workflow of your cybersecurity team. This will help you to understand the functional capabilities that you need to employ. 7. The Importance of Data Integrity and Subject Matter Experience Data integrity is another associated prerequisite for AI model in cybersecurity. Instead of spending countless hours on algorithm optimization, businesses should devote more time for data optimization instead. Experience in subject matter is another element that is also necessary in ensuring data quality as well. For example, during the deployment of ML for cybersecurity, domain gurus are your best bet for priceless feedback. 8. Achieving more with little According to McKinsey, several cybersecurity managers have had to pause their new investments in 2020 in the Post-COVID-19 pandemic period. It is in fact very likely that crisis responses would continue to occupy top budget priorities for the rest of 2020. Considering the tough financial reality, it is the aim of some organizations to widen security automation, while finding ways to save at the same time. This is why some companies are leveraging on the existing machine learning models in public cloud platforms to try and initiate smarter data analytics on their cybersecurity data. 9. Be Willing to Embrace New Possibilities The AI landscape is moving so fast that it's difficult to comprehend. The issue is not with the changing rate of the technology, but that the technology is progressing so rapidly, it's almost impossible to believe. Hence, you need to be ready to embrace new innovation, otherwise you won't enjoy the benefits of this rapidly advancing landscape. Being open to new possibilities in AI model for cybersecurity would allow the executives to increase efficiencies, limit costs, and re-consider other options that would facilitate an up-to-date security network. Conclusion Overall, AI development in cybersecurity can help you initiate complex network threat identification, smart prediction, active supervision, behavior and streaming analytics, and workflow automation. With a successfully implemented AI model in cybersecurity, you stand the chance to fast track analysis, quickly process huge volumes of network data, and uncover previously hidden threats and opportunities. What are you waiting for? Tap into the limitless potentials of AI in cybersecurity today!

9 Considerations for Building an AI-based Model for Cybersecurity In April 2019, Consumer Technology Association carried out a survey which revealed that the major utility of AI model in 2018 was for cybersecurity purposes. Specifically, an estimate of 44% of the total AI applications were employed in detecting and deterring security intrusions. Furthermore, another survey carried out by Capgemini in July 2019, reported that over ⅔ of respondents claimed that the present threat environment indicates that AI will be indispensable in tackling future cyberattacks. It is therefore indisputable that the role of AI in cybersecurity is crucial. If you're starting to ponder: "how does AI work in cybersecurity and how can I implement this in my business?" The answer lies in the rest of this article, as we will be discussing 9 standard considerations for establishing an AI-based model for your cybersecurity needs. Enjoy! How Does AI Work in Cybersecurity 1. Evaluate Objectives and Possible Threats For as many companies that are willing to embrace the challenges - both seen and unseen, that are associated with AI, you should know that it offers immense potential for proactive approaches, accurately targeted resource distribution, and predictive insight that will enable you to perfect your security strategies. AI also offers complex detection functionalities, decreases professional "alert tiredness", and assists in the augmentation of security departments. However, before the implementation of AI in cybersecurity begins, it is necessary for businesses to identify factors necessary for its success, risks associated with the process, and expectations after the project. 2. Create a Strong Foundation In order to establish a remarkable foundation, it is pertinent to understand the basics of the system. They include: visibility, administration, storage and analysis, and workflows. • Visibility To begin with, an established IT Asset Management Program must be used to provide accountability for all assets on the system. By understanding the components of the system, you are able to recognize and respond to cybersecurity threats, as well as making sure your AI model is utilizing the appropriate data. • Administration Thereafter, multiple data feeds are needed for the most effective operationalized AI use cases. • Storage and Analysis After the data undergoes standardization, a data broker such as RabbitMQ or Kafka is used to help migrate data out of existing security networks to sites where complex evaluation takes place. • Workflows Finally, it is important for companies to develop well-defined and highly organized workflows and procedures that stretch beyond the security department. This will make it easier to launch the AI model across your company. 3. Pay Attention to The Human Factor AI augments human effort by assisting analysts in the automation of labor-demanding processes, fast tracking evaluation, and reduction of errors. To organize, manage and develop the human factor of AI in cybersecurity, the following questions should be considered: • What tasks would you automate and which would you retain in human hands? • How do you intend to introduce emerging roles into the business? These might comprise employees and cyber data analysts who supervise machine learning models or advocate ML integration into the system. • What preparations doses your company have to enlighten employees on the overall concept of AI? (e.g., hack-a-thons, podcasts, webinars, online tutorials etc.) 4. Concentrate on Use Cases To alleviate risk and maximize the success of an AI model launching, companies are advised to concentrate on the implementation of AI-based use cases. This should be carried out in the initial stage in any wider AI adoption initiative. 5. Automating and Arranging for Fast ROI After observing the first 4 steps above, the next thing is for organizations to automate their processes and permit analysts to change the focus of their efforts. According to the Security Magazine, about 75% of companies are aware of the advantages of automation, however nearly a third of them have been unable to successfully implement resource-saving automation programs. Certain areas like traffic patterns, phishing email header analysis, vulnerability scoring, and intelligence collection are key areas to start with for high automation and AI model use cases for a fast ROI. 6. Ensure Organizational Maturity Next, it is significant for you to make sure your organization has a well outlined security detailing. This should include considering the basics, and also the more sophisticated threats. It is necessary to have this in place before embarking on the launch of an AI model. To ensure organizational maturity, you need to start thinking of how you can augment the workflow of your cybersecurity team. This will help you to understand the functional capabilities that you need to employ. 7. The Importance of Data Integrity and Subject Matter Experience Data integrity is another associated prerequisite for AI model in cybersecurity. Instead of spending countless hours on algorithm optimization, businesses should devote more time for data optimization instead. Experience in subject matter is another element that is also necessary in ensuring data quality as well. For example, during the deployment of ML for cybersecurity, domain gurus are your best bet for priceless feedback. 8. Achieving more with little According to McKinsey, several cybersecurity managers have had to pause their new investments in 2020 in the Post-COVID-19 pandemic period. It is in fact very likely that crisis responses would continue to occupy top budget priorities for the rest of 2020. Considering the tough financial reality, it is the aim of some organizations to widen security automation, while finding ways to save at the same time. This is why some companies are leveraging on the existing machine learning models in public cloud platforms to try and initiate smarter data analytics on their cybersecurity data. 9. Be Willing to Embrace New Possibilities The AI landscape is moving so fast that it's difficult to comprehend. The issue is not with the changing rate of the technology, but that the technology is progressing so rapidly, it's almost impossible to believe. Hence, you need to be ready to embrace new innovation, otherwise you won't enjoy the benefits of this rapidly advancing landscape. Being open to new possibilities in AI model for cybersecurity would allow the executives to increase efficiencies, limit costs, and re-consider other options that would facilitate an up-to-date security network. Conclusion Overall, AI development in cybersecurity can help you initiate complex network threat identification, smart prediction, active supervision, behavior and streaming analytics, and workflow automation. With a successfully implemented AI model in cybersecurity, you stand the chance to fast track analysis, quickly process huge volumes of network data, and uncover previously hidden threats and opportunities. What are you waiting for? Tap into the limitless potentials of AI in cybersecurity today!

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