As cyberattacks become commonplace, businesses are turning to AI to help with cloud security — but there are many misconceptions about what AI actually does. We explore areas such as data management, automated threat responses and more, covering what AI can and cannot do in each situation.
SwissCognitive Guest Blogger: Zachary Amos – “How to Improve Customer Perception of AI Chatbots”
For many businesses, getting ahead of the competition requires innovation and digital transformation. Yet many have shifted their focus towards security and user experience these days.
This movement is happening because enterprises have realized maintaining optimal security and quality user experience is paramount.
In tech innovations, data security has played a major role in business. And with the emergence of AI (artificial intelligence) and ML (machine learning), these two technologies have significantly impacted security measures and practices.
With the help of the two, organizations can help minimize human errors, prevent data breaches and more.
AI in Cloud Security
Cyberattacks are becoming more advanced in sophistication and severity each day. The IT industry is working nonstop to improve its security systems and protect its organizations.
One of the routes experts are exploring is AI and ML in cloud security.
AI can improve security in various ways, including monitoring vulnerabilities and violations — many of these issues stem from user errors.
Numerous people have misguided perceptions of what these tools can provide. Here are some ways AI enhances cloud security and the truths of what it precisely offers.
1. Predictive Analytics
With the proper data, ML solutions can predict future events accurately. The way this works is experts pile together loads of information or content and use that to build a predictive model. In turn, this data shows how various events could play out.
For instance, the retail market is constantly evolving. Using this could help businesses prepare for upcoming changes, such as increasing or decreasing demand and supply.
In cybersecurity, businesses can understand potential threats and predict what could happen.
Currently, the systems are slightly underdeveloped. Nevertheless, they break down into two types of predictive analysis.
The first type is less complex and more likely to arrive sooner than the second. This AI system must have information about different attacks on different organizations. Then, AI can make accurate risk assessments for a particular company.
The second type is far more complex, where organizations spend much of their resources on white-hat analysis. This is where “friendly” hackers attempt to bypass the system using any means.
Companies can automate this analysis and make more quick and efficient solutions. However, creating white-hat-type AI hackers can form a robust set of hacking tools. This is a problem with no easy solution and is why governments worldwide monitor these tools closely.
2. Data Management
Data leaks happen more often than most think and often occur because of unauthorized user access.
Users should not have access to a set of data that hackers can breach. Otherwise, it can result in a severe attack.
However, AI with network security and gateway protection can lock systems into place and prevent potential threats. Additionally, AI can monitor users to detect suspicious activity before attacks occur.
This type of functionality is widespread across various industries. Some cloud providers may even ship it with a basic cloud storage system.
Yet, many organizations aren’t aware that AI is collecting data on how they use their cloud service to detect unusual activity. This tool also represents the limitations of what AI does regarding cloud security because many organizations lack the components to use AI in a complex way.
Organizations often rely on the cloud provider to set up an AI-driven system without much knowledge of how it works.
3. Automated Responses
AI models can continuously counteract threats by responding to hackers. Many organizations are dependent on AI-driven cloud security systems to deploy constant measures.
One of the many claims AI systems have is they can intelligently respond to attacks. However, this is a common misconception for many reasons.
First, these systems make automated suggestions to the administrators — only they have the final say in what to do during an attack. Essentially, AI or ML technologies only follow simple and explicit coded rules.
Secondly, even where cloud computing systems can directly deploy these capabilities, they are still constrained. While they might be able to block a specific user who tries to access a restricted area, it remains generally limited in power.
There is a good reason for this. Deploying automated systems that directly affect computer networks is a considerable risk.
If an AI system goes rogue, it could easily block users and systems, costing an organization millions of dollars due to downtime and recovery.
The Future of AI in Cloud Security
While people are often misguided about what AI can genuinely do to enhance security systems, this tool still plays a prominent role in improving cloud security. It also has a high potential for advancements.
For example, AI can potentially aid the cybersecurity skills shortage. Between this deficit and more employees working from home, these factors have opened more opportunities for hackers to carry out suspicious activities.
While this technology has already fueled threat innovation and protection, AI will increasingly alleviate the bandwidth of security professionals’ efforts and automate protection processes.
As hackers continuously enhance their tools, implementing AI will be most effective in cloud computing security. It could completely automate all cyber operations with minimal human interventions.
Keep Focusing on Enhancing Cloud Security with AI
The only issue with ML-enhanced security is that people rely too much on it, believing it can detect and stop all threats. As of now, AI cloud security software is limited.
Humans need to focus on improving cloud security platforms to truly make intelligent responses rather than relying on machines to do it for them.
Organizations need to keep investing in AI and ML to ensure optimum security. Once they accomplish this, they can accelerate their business development and flourish.
About the Author:
Zachary Amos is the Features Editor at ReHack where he writes about artificial intelligence, cybersecurity and other tech topics.