9 Ways AI is Enhancing Data Center Security
Data centers can overhaul their traditional technologies with artificial intelligence and drastically improve their cybersecurity and physical security. Here are the nine main methods they can use to improve data center security.
1. Administrative Automation
Many IT departments have massive workloads. Whether they have a high ticket volume, receive constant security alerts, or need to tend to administrative duties, something always claims their attention. Eventually, an overabundance of responsibilities leads to poorer data center security.
Even seasoned professionals are prone to the occasional slip-up. In fact, around 43% of remote workers make errors that lead to workplace cybersecurity incidents. Since automation is one of AI’s primary functions, they can transfer their more tedious tasks to it. They’ll tackle alerts more effectively, make fewer mistakes and respond with haste once they have more free time.
2. Predictive Analytics
Machine learning models can leverage predictive analytics, giving IT professionals insight into their data center. For example, they can predict when a server will need repairs, allowing technicians to act proactively instead of waiting for scheduled service or responding to an outage.
This AI subset can drastically improve cybersecurity by alerting IT professionals to likely failures and outages. Although it may not seem as important as other methods, it’s crucial. Preventative equipment maintenance processes like software updates, firewall tests or camera cleaning are essential to overall security and data center management. On average, a data breach can cost a company $4.24 million — preventative maintenance can help ensure IT professionals don’t overlook something simple.
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3. Autonomous Network Surveillance
Network surveillance is crucial for adequate cybersecurity. AI has unmatched speed and accuracy when it comes to data set monitoring. It outperforms traditional observation technology, dramatically enhancing data center security. It can rapidly detect, identify and report any suspicious activity it notices.
Where other technology operates within a rule-based structure to identify potential risks, machine learning models constantly adapt to emerging threats. Instead of remaining stagnant — and unprepared to address new cyber attack methods — IT professionals can outpace hackers. Consistently staying one step ahead grants them a tremendous advantage.
4. Automatic Alert Processing
AI’s ability to monitor hundreds of cybersecurity incidents simultaneously is one of its most impressive functions. It can individually process every alert to determine whether they are genuine or false positives — a crucial duty. After all, what happens when a network monitoring system flags dozens of things at once and the only defense is manual?
Even the most productive person on the planet can’t sustain such a massive workload. Frankly, it’s also unfair to expect any IT department to outpace botnets, dozens of malicious hackers and large-scale surprise attacks. Leveraging AI lets them address only the most pressing incidents, ensuring their response is fast and reliable.
5. Automatic Vulnerability Testing
Since vulnerability testing is a significant part of data center cybersecurity, automation is ideal. AI can scan code and detect configuration mistakes without human intervention, meaning an IT department can maintain cybersecurity in any case. Procedures will continue even if they experience a labor shortage or get caught up in an incident response.
6. Autonomous Firewalls
A next-generation firewall (NGFW) combines the traditional firewall with AI technology. It filters traffic with precision and can block complex attacks. It can even adapt to emerging threats, consistently outpacing the technological advancements of malicious cyber attackers.
While threat detection takes up to 200 days on average, an NGFW can recognize them within seconds. Further, it can identify successful breaches in only minutes — allowing IT professionals to vastly improve their incident response time and swiftly eliminate malicious activity.
7. Synthetic Training Data
If IT professionals want to get the most out of their AI, they must train it properly. However, finding an accurate, bias-free data set with the exact specifications they need can be challenging. Fortunately, a generative model can help them. While most of its use cases revolve around the arts, using it for cybersecurity purposes is possible.
Generative AI can produce realistic phishing messages, malicious code or social engineering emails. The IT department can train its original algorithm on this content to prepare it better for real-world scenarios, enhancing its cybersecurity.
8. Surveillance Recognition
Physical security is almost as important as cybersecurity. After all, network monitoring and firewalls are relatively useless against someone who walks in through the front door. Even though this scenario isn’t nearly as common as cyber attacks, it’s critical to overall data center security.
For example, employees fall victim to social engineering and phishing all of the time. Preventing them from physically accessing servers can protect against data breaches and unintentional tampering. Departments can integrate AI with the surveillance system to enhance data center security. It can detect intrusions and restrict entry using biometric recognition.
9. Automatic Encryption
A data center is an exceptional target for threat actors since it contains massive collections of valuable information. Securing the data within it can dissuade them from making hacking attempts. While a typical encryption process is time-consuming, AI can simplify it.
IT professionals can use two neural networks to encrypt anything on their servers. This is an especially useful tool when transferring data to other centers. Since decryption is only possible for people with access to the key or the AI model, their information stays secure.
How Data Centers Can Use AI
AI can improve data center security with automation, content generation, and rapid processing speeds. Data center professionals should identify their key pain points to determine where integration would be most beneficial.
Originally posted on OpenDataScience.com
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