Floating on Cloud 9...
Dr. Dustin Sachs, DCS, CISSP, CCISO
??Chief Cybersecurity Technologist | ??Researcher in Cyber Risk Behavioral Psychology | ??? Building a Network of Security Leaders
Cloud security and data management worries are becoming a crucial issue as businesses adopt cloud computing more and more (Nassif et al., 2021). Organizations must have proactive security measures that can identify and thwart possible threats in order to secure sensitive data and assets. The needs for data management and cybersecurity in the cloud may be met by artificial intelligence (AI).
Suitability of AI for Cybersecurity and Data Management
AI has the potential to be an effective tool for identifying and preventing cloud-based cyberthreats (Lee et al. 2019). It can be used to instantly evaluate massive amounts of data and spot trends that could point to security breaches. AI can be used to spot anomalies and suspect activity that conventional security technologies might miss. AI can continuously learn from new threats, adapt to them, and provide proactive threat detection and response capabilities by utilizing machine learning algorithms (Lee et al. 2019).
AI can be utilized for cloud data management in addition to cybersecurity. It can assist businesses in the analysis of huge datasets and the discovery of operational insights. In order to assist enterprises enhance data quality and make better decisions, AI can be used to automate data management processes including data cleaning, data integration, and data modeling (Nassif et al., 2021).
Designing an AI System for Cybersecurity
AI can be utilized for cloud data management in addition to cybersecurity. It can assist businesses in the analysis of huge datasets and the discovery of operational insights. In order to assist enterprises enhance data quality and make better decisions, AI can be used to automate data management processes including data cleaning, data integration, and data modeling (Nassif et al., 2021).
Combining supervised and unsupervised learning algorithms is one method for creating an AI system for cybersecurity (Lee et al. 2019). The system can be trained using supervised learning on labeled data, like known malware and attack patterns. To find patterns and anomalies in the data that may point to possible security breaches, unsupervised learning can be applied.
Another method is to analyze network traffic and look for potential security breaches using deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). RNNs may be used to study network traffic over time and spot anomalies, whereas CNNs can be used to examine network packets and spot harmful payloads (Lee et al. 2019).
Feasibility of AI for Cybersecurity
The quality, quantity, and complexity of the data, as well as the system's complexity, all affect how feasible an AI system for cybersecurity is. Access to big datasets of labeled and unlabeled data is required by businesses in order to train AI systems for cybersecurity (Lee et al. 2019). Since the system will only be as good as the data it is trained on, the quality of the data is equally crucial. Also, a high level of technical know-how and resources are needed to create and implement an AI system for cybersecurity.
Conclusion
In conclusion, AI can be a suitable solution for meeting the cybersecurity and data management requirements in the cloud. It can provide proactive threat detection and response capabilities, as well as automate data management tasks. Designing an AI system for cybersecurity is a complex task, but with the right data and technical expertise, it is feasible. AI can be a powerful tool for protecting sensitive data and assets in the cloud, and organizations should consider incorporating AI into their cybersecurity and data management strategies.
References
Lee, J., Kim, J., Kim, I., & Han, K. (2019). Cyber threat detection based on artificial neural networks using event profiles. Ieee Access, 7, 165607-165626.
Nassif, Ali & Abu Talib, Manar & Nasir, Qassim & Albadani, Halah & Albab, Fatima. (2021). Machine Learning for Cloud Security: A Systematic Review. IEEE Access. PP. 1-1. 10.1109/ACCESS.2021.3054129.
Former NASA Executive | Former CIA CI Officer| DHS CISA Advisory Board Member | F500 CISO | CSO | CIO | Expert Witness Attestation Provider
1 年I would suggest that there are an unlimited number of use cases leveraging AI in the cloud and data management. On the space side of the house, AI is leverage as as a tool within the cloud to perform management (parse and search) of tremendously large scientific data sets derived from deep space, Cis Lunar, or Earth Observing satellites. These data sets get into the petabyte range and using cloud services to store these data so that scientist can quickly and efficiently access it is an option that is being used in an increasingly pervasive fashion today. But imagine trying to parse through a particular data set that is many petabytes in size for just a sliver of data for a specific task. This is where AI is extremely useful! With the correct prompt, one could get to that slice of data in a matter of seconds, instead of days or weeks. Even better, how about leveraging AI to automagically parse data into user define data set "buckets" and draw out conclusions (made into information) on those data sets as the data is making it's way from the spacecraft to the cloud's data archive environ! This is going to shorten the raw data to information cycle time by an inordinate amount of time.
Security Architecture Engineering Enablement
1 年IMO AI is a solid capability no different than when electricity was invented or we learned flight. I am more curious on how the human involvement and aligned evolution will coexist especially that the data that AI is incorporating is what it will be leveraging. #thehumancondition
Research Technical Director @ Cloud Security Alliance
1 年I think the biggest capability right now for uses such as ChatGPT are the ability to put multiple prompts together to formulate a larger picture or strategy for business. I don't think it resolves items such as hardening in a physical manner, but it truly can help simulate a garnered approach and compile multiple areas to be inclusive of one another. We still need to be the drivers of the information, and use it to help make sense of struggling areas.