Are you looking to add a third-party security system to secure your cloud data? There are a lot of factors to consider when implementing a cloud security platform in your organization.
What type of machine learning is the solution leveraging? How well will it scale to meet demand over time? Specifics about data, such as how it’s sourced and where it’s stored, are also essential considerations.
The following are ten essentials to check when evaluating a security platform:
Machine Learning
One of the essential features of a security platform is its ability to automate processes and manage information effectively. For this reason, many organizations have started experimenting with artificial intelligence (AI) and machine learning.
In short, AI is the study of computer systems that perform tasks that require human intelligence. Simply put, machines are taught to learn from experience rather than be explicitly programmed.
Since these security platforms will need to deal with high levels of data in a short period, they need to have the capability to process and learn about this information rapidly. If they don’t have built-in machine learning capabilities, you’ll need a team to review data and make decisions manually.
Data Collection
Data is the lifeblood of any platform. The more data you can provide, the better the machine learning algorithms will accurately identify patterns and threats.
Ideally, you’ll want to collect data from a variety of sources. It could include machine data, network flow data, user activity, and more.
It could be an issue with compliance or privacy regulations in some cases. You must know that your organization is taking steps to protect personally identifiable information (PII), for instance.
You don’t want machine learning platforms to automatically send out notifications as they detect a threat in the data.
Ease of Use
If you have a security platform that’s difficult to use, people will find workarounds or stop using it altogether. This defeats the purpose of implementing a security platform and leaves your organization vulnerable.
If the platform is simple to use, your team will be more likely to engage with it and leverage its capabilities to the fullest extent.
Scalability
As you implement a new machine learning-powered security platform, monitor how effectively it scales as time goes on.
It’s also important to consider how well the platform will perform under heavy load. Some platforms may not handle the influx of data or the increased number of requests. It can lead to degraded performance and, ultimately, a failure to protect your organization from threats.
Threat Detection
How well does the security platform detect threats? What are its false favorable rates? These are the first questions you should ask.
You should ask for information about how the machine learning algorithms work to separate false positives from real threats.
You should look for detailed explanations of how it will process data and make decisions.
Access Controls
If you implement a new security platform, ensure that it has adequate access controls. It is valid for both the administrators and the users of the platform.
Administrators need to manage the platform effectively, while users need to be able to access only the data they’re authorized to see.
Access controls should also include restrictions on what actions users can take. For instance, certain users may only need access to view the information, while others may be allowed to delete or modify it.
Reporting
A security platform should generate detailed reports, both for administrators and users. Reports should include information on threats and data on how the platform is performing.
This information can help administrators troubleshoot issues and optimize the platform’s performance. Users can use reports to understand better the types of threats their organization is up against.
Updates
How frequently is the security platform updated? As your organization’s risk profile changes, does the platform change accordingly? If it isn’t changed accordingly, you may miss out on new types of threats targeting your organization specifically.
That said, machine learning platforms need to be trained on data to learn effectively. So, when you implement updates, make sure that their frequency doesn’t interfere with the platform’s ability to learn and improve its detection capabilities over time.
Security Measures
According to research, around 49 percent of businesses do not have their cloud databases encrypted.
Therefore, security measures are another essential part of any secure platform. If the data is compromised somehow, you’ll want to know immediately so you can take action.
Is the machine learning platform operational on a virtual private network (VPN), so it’s only accessible to authorized users? Are logs kept in case there are any questions about who accessed the data or why?
Support
When you implement a new security platform, you’ll want to make sure that there’s someone available to help you if you have any questions or problems.
Good support includes not only technical support but also training and documentation.
You must make sure that the platform provider has a comprehensive support system in place so you can get the most out of your investment.
If you’re looking to implement a security platform, make sure to check for these ten essentials. By doing so, you’ll ensure that your organization is well protected against the latest threats.