Aug 19, 22 10:00 am

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The State of AI in Cloud Security

Aug 19, 2022
| by:
Shimon Brathwaite

The Role of AI in Cloud Computing

The role of AI in cloud computing has been a hot topic of discussion recently. While some believe that AI will take over many of the tasks currently performed by humans, others believe that AI will augment human abilities and help humans to become more efficient. In cybersecurity AI is heavily implemented in a new field called User and Entity Based Analytics (UEBA), which is focused on identifying threats based not only on the software’s signature but also using deviations from normal behaviour in the target environment. In this blog post, we'll explore the evidence for and against the idea that AI will play a significant role in cloud computing.

Challenges of AI in Cloud Security

As cloud services become more commonplace, so too does the need to secure those services from malicious actors. And while there are many tools available to help secure cloud environments, one of the most promising is artificial intelligence (AI).

AI can help to identify and block threats that would otherwise go undetected. However, some challenges come with using AI for security purposes.

Security and Privacy Issues

As artificial intelligence (AI) continues to become more advanced, there are concerns about how these technologies will impact our privacy and security. For example, if AI is used to monitor our online activity, it could be used to collect sensitive data about our personal lives. And if AI is used to create self-driving cars, there could be potential risks if these systems are hacked.

Also, AI systems can be fooled by sophisticated attackers. Some people are under the illusion that AI solutions are foolproof and that is simply not true. AI can be tricked and bypassed by good attackers but this assumption of perfect security can cause companies to become complacent and leave themselves vulnerable.

And finally, AI can introduce new risks and vulnerabilities if not used properly. Just like any improperly configured system, an AI that is mismanaged can introduce potential security vulnerabilities to a company.

Data is Very Unstructured, Siloed, or Incomplete

Artificial intelligence is seeing rapid growth in both adaptation and deployment. However, unstructured data still presents a challenge for AI. Unstructured data is data that does not have a predefined data model or structure, making it difficult to organize and interpret. This can include things like text, images, and videos.

While unstructured data can be harder to work with, it can also contain a lot of valuable information. In many cases, unstructured data can be more important than structured data when it comes to making decisions. This is because unstructured data can often provide more context and detail than structured data.

Despite the challenges, unstructured data is becoming increasingly important in the world of AI. As we continue to develop new ways to work with this type of data, we will be able to find even more insights using AI and machine learning.

Regardless, AI is still a valuable tool for security and should be used alongside other security measures to help protect cloud environments.

Benefits of AI in Cloud Security

Artificial intelligence (AI) is playing an increasingly important role in cloud security. As more and more businesses move their data and applications to the cloud, AI can help them to better protect this sensitive information.

Less Human Intervention Required

There are many benefits of using AI in cloud security. AI can help to identify threats that humans might miss, and it can also provide insights into the behaviour of attackers. AI can also be used to automatically block attacks and respond to incidents.

One example of this is Azure Sentinel in the Microsoft Azure cloud suite. This tool allows for incident responses to trigger security remediation automatically based on the type of alert that sentinel generates. This means that the system is capable of detecting and remediating issues without human intervention.

Big Data Processing

Big data is a term that refers to the large volume of data that organizations generate daily. This data can come from a variety of sources, including social media, transactions, and clickstream data.

Processing big data presents several challenges for organizations, as it can be difficult to store and process such large volumes of data. However, big data also presents several opportunities for organizations that can effectively manage and utilize it. Big data can be used to improve decision-making, identify new business opportunities, and optimize business processes.

With the right tools and approach, big data can be a valuable asset for any organization. In a cybersecurity context, big data processing means that security tools will be able to ingest a larger amount of data and use that data to assist security professionals in making decisions. For example, one area that uses this heavily is UEBA (user and entity behaviour analytics). This field looks at data collected across an organization to identify that is considered a normal behaviour and can identify any behaviour that is suspicious as being a potentially malicious activity.

Delegating to Automated Technologies

As artificial intelligence (AI) continues to evolve, it is increasingly delegating tasks to automated technologies. This is done in order to free up the AI's time to focus on more complex tasks, or tasks that require human judgment.

There are many benefits to this trend, including increased efficiency and accuracy. However, there are also some risks, such as the potential for job loss as more and more tasks are delegated to automated technologies.

Overall, the trend of AI delegating to automated technologies is likely to continue, with both benefits and risks. As we learn more about the potential of AI, we will be better able to manage these risks and maximize the potential of this technology.

Within cybersecurity, being able to automate processes will save time and allow the security team to respond to issues much more efficiently. Also, when you automate a process with AI, it's less likely that there will be issues compared to having someone do it manually. This means that not only are things done much faster but they are done with fewer errors.

Event Detection and Blocking

As artificial intelligence becomes more sophisticated, so too does its ability to detect and block potential events. AI event detection and blocking is a technology that is used to monitor for potential events and to prevent them from happening.

Event detection and blocking is not new, but the use of AI to do it is. Using AI we can feed the algorithm data on what “normal” network activity looks like and based on that the AI model will be able to detect any activity that falls outside of the norm. Once the AI system has been trained, it can then monitor for those patterns and take action to prevent the event from happening.

AI event detection and blocking has a wide range of potential applications. It can be used to prevent crime, stop terrorist attacks, and keep people safe in general. It can also be used to prevent accidents, protect sensitive information, and guard against natural disasters. A common example of this is hurricane forecasting. In regions like the Carribean, weather companies have used AI and machine learning models to accurately predict when hurricanes will emerge and the most likely path that they will take (they can see this days and sometimes even weeks in advance). This gives people extra time to prepare and saves thousands of lives a year.

Cybersecurity companies that Leverage AI

In recent years, there has been a growing trend of cybersecurity companies that are turning to artificial intelligence (AI) to help them in their work. Here are some of the ways that AI can be used in cybersecurity:

  • Prediction of future attacks: AI can be used to identify patterns in data. By analyzing patterns of behaviour AI can be used to detect attacks that would go undetected by more traditional anti-malware solutions.
  • Automation: You can use AI driven solutions to automate the remediation of security issues. Allowing for faster response times and less overhead among administrators.
  • Cheaper Solutions: Using AI based solutions can be far more cost effective than manual testing, which is a big positive for the clients that don’t have big security budgets.

The Future of AI Cloud Computing

The future of AI in cloud computing is very exciting. With the rapid advancement of machine learning algorithms and the increasing availability of computing power, we are on the verge of seeing some amazing breakthroughs in this field.

AI in cloud computing will allow us to process data faster and more efficiently than ever before. It will also help us to automatically identify patterns and relationships that would otherwise be undetectable. This will have a huge impact on fields like medicine, finance, and logistics. For example, in the field of medicine, AI is often used in drug discovery and development to help find new products faster.

AI will also help us to better understand and predict human behaviour. This will be valuable for improving the usability of websites and apps, as well as for marketing and advertising.

Overall, the future of AI in cloud computing is very bright. We are just beginning to scratch the surface of what is possible, and the possibilities are truly limitless.

Conclusion

In conclusion, AI plays a very important role in cloud computing. By automating tasks and managing data, AI can help improve the efficiency of cloud computing systems. However, AI is still in its early stages of development and there is much that still needs to be done to fully realize its potential. 

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About the Author

Shimon Brathwaite
Shimon Brathwaite is a cybersecurity professional, Consultant, and Author at securitymadesimple. He is a graduate of Ryerson University in Toronto, Canada. He has worked in several financial institutions in security-related roles, as a consultant in incident response and is a published author with a book on cybersecurity law. My professional certifications include Security+, CEH and AWS Security Specialist.
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