Multi-cloud strategies are being adopted by businesses in order to leverage the best aspects of each cloud solution, whether it’s to reduce storage costs or to take advantage of Machine Learning (ML) and automation capabilities. Security teams face several challenges as their organizations move and expand their services and applications to multi-cloud environments, ranging from business policies and budgetary limitations to compliance fines and new attack risks.
Internal and external threats to cloud data security might range from legitimate users misusing data to threat actors seeking to leverage stolen credentials. This strategy necessitates a unified security approach that ensures compliance standards are met along with automation of security controls.
Here are the top three cloud native security challenges and how businesses can mitigate risk.
Also Read: Organizations are Struggling to Manage Their Cyber Assets
Not Enough Visibility
When compared to on-premise environments, shifting to the cloud results in a significant loss of security and compliance insight. The ability to observe and control assets in another’s physical space is required in public cloud environments, and in the shared security responsibility paradigm, it is the responsibility of the public cloud customer for safeguarding data and traffic flows.
The ever-changing nature of cloud resources, as well as trying to keep track of these assets, adds to the complexity. As cloud native technologies like serverless gain traction, they introduce new challenges. Serverless apps, for instance, might have hundreds of functions, and when the application matures, managing all of this data and the services that access it becomes cumbersome.
This is why assets must be detected automatically as soon as they are built, and all changes must be tracked until the resource is no longer available.
To detect malicious use more effectively, cloud native security must be able to understand both normal use and user intent. Security solutions should also employ Machine Learning to create a thorough profile of what constitutes routine use in order to fully understand it. Such profiles enable a solution to detect and alert on questionable behavior automatically.
Also Read: How Businesses Can Improve Their Fraud Program
Diverse Threat Landscape
As cybersecurity professionals innovate, so do cybercriminals. Different attack types, like account takeover, can be carried out using a variety of methods, such as brute force botnet attacks, phishing, buying user credentials on the dark web, and even sifting through trash for personal information.
This type of attack necessitates some ingenuity on the side of security professionals. A diverse threat landscape necessitates a diverse defence strategy.
To protect sensitive data while averting threats, organizations shifting to the cloud must grasp the value of intrusion detection, data analysis, and threat intelligence. Through Machine Learning and threat research, cloud intelligence technologies can evaluate events in the cloud environment and deliver insights into account activity. Businesses must look for solutions that allow them to filter results, troubleshoot with queries, dive in for more details, and tailor alert notifications.
Unable to Implement Consistent Policies
Today’s cloud-native settings are made up of a diverse set of tools from a variety of vendors, making it challenging to centralize and enforce security policies consistently.
In a multi-cloud/hybrid environment, combining diverse solutions to achieve the actionable end-to-end view required for comprehensive cloud security posture monitoring is extremely tough. Businesses must seek solutions that can streamline their entire cloud architecture, including bringing in all Content Security Policies (CSPs) and automating rulesets, alerts, policies, and remedies.
For more such updates follow us on Google News ITsecuritywire News. Please subscribe to our Newsletter for more updates.
