The coronavirus pandemic has dealt a blow to many businesses around the world. While the pandemic has negatively impacted different sectors, it has also taken business owners to the drawing board when it comes to business operations.
For instance, IT businesses are now prioritizing a 'cloud-first' approach more than ever before. Most of them have realized the importance of investing in the cloud, something that was not a concern for many businesses before the pandemic.
Although the cloud provides the much-needed scalability and agility to deal with crises, compliance and security remain the biggest challenge towards cloud adoption.
According to a survey conducted in 2019 concerning cloud security, 93 percent of organizations were concerned about cloud security vulnerabilities.
So, how do AIOPs boost cloud security?
AIOP is an artificial intelligence software used in IT operations to automatically fix IT operational challenges within cloud environments.
Below are six ways AIOP optimizes cloud security:
- Threat intelligence
Threat intelligence analysis helps in providing crucial information with regard to the sources of attackers. It also gives indicators of compromised areas within a network system. The application works by ingesting data from different parts of the cloud environment. It then analyzes the data and later gives insights into any security incidents.
By correlating various relevant and real-time insights, the cloud security experts can identify the source of the attack and take appropriate action.
2. Incident response and management
AIOP makes it easy for security teams to quickly respond to incidents by offering relevant information regarding different security issues in a cloud system. Besides, the AI and ML capabilities of the app can be used by the security team to create and deploy vigorous intelligence detection and other alerting strategies.
3. Behavior analysis
This is also another major way through which AIOP optimizes cloud security. The analysis of endpoints and network behavior gives security teams an opportunity to swiftly detect subtle indicators of compromise. This allows them to identify attacks or threats and take action to avert breaches from happening.
4. Fraud detection
Generally, fraud detection needs deeper diving into a lot of raw data and this process can be very complicated and time-consuming. This often intensifies the need for text mining, database searches, anomaly detection, and social network analysis to detect fraud incidences. But the AI application can do away with all these complexities and make it easy for security teams to detect and avert fraud much faster.
5. Malware detection
Embedding AI capabilities and machine learning in cloud security can easily assist security teams to identify unusual activities or threats. The teams can take advantage of AIOP to track performance activities in relation to external threat intelligence feeds.
6. Data classification and monitoring
Last but not least, the application analyzes both structured and unstructured data within cloud environments including private, public, or hybrid and classifies them into different categories, depending on the type of content and metadata.
There are various ways in which AIOPs optimizes cloud security. The most important thing is to ensure the proper implementation of each strategy.