Companies are in need of better cloud security strategies as IT teams are always flooded with threat alerts. Experts bet on deeper data science
The evolution of traditional cloud security is crucial to maintain the infrastructure and challenges that come with cloud environments. IDG’s Cloud Computing Study states that 92 percent of companies have established their cloud footprint and the most common challenge faced by them is the security of the flooding data insights. Experts reckon that Machine Learning (ML) cybersecurity is the way to go for a secure future.
Most security analysts and IT security employees neglect threat alerts after a point of time as they are already flooded. This challenge coaxes companies to turn themselves to ML-based analytics that can filter the ubiquitous data based on the level of vulnerability. This will bring about careful, well-informed decisions.
Nevertheless, industry leaders cannot fully depend on these analytics. When bad data is infiltrated into the system, the ML tools will be inadequate. It can put the company infrastructure at risk with a high possibility of security breaches and cyber-attacks.
Experts recommend a solution that will take enterprise security strategy deeper into data science. It is crucial that ML security extends beyond good data and integrates the massive industry experience and rules sets to understand insights better. Companies can get their ML-based security system to leverage good quality telemetry for better visibility in their cloud environment. This includes containers-as-a-service (CaaS), container runtimes, application layers, and host machines.
Experts claim that the collection of telemetry will be the foundation of advanced analytics and security insights with two major benefits. One, the data collection will drive meaningful insights and increase security checks. Two, it can minimize the management of large data sets and in turn, reduce the time and cost of engineering.
Streamlining Detection Strategies
Industry leaders need to make informed decisions about the detection methods that they would use, since massive amounts of data come with a lot of security and compliance insights. From IP reputation scoring to behavior-based alerting rules, there are many solutions for organizations to choose from. Experts recommend a combination of them all to detect known and unknown threats or anomalies.
While alerting rules can detect threats from existing patterns in the cloud environment, ML techniques can inform on unknown risks. Watching out for insider threats and gathering historical access data for a compliance audit is handled by alerting rules, and ML tools catch threats that are impossible to predict with alerting rules. Yet, they complement each other and can allow companies to detect known and unknown threats, suspicious trends, anywhere in their cloud environment.
Meanwhile, our technology is not so advanced to eliminate the need for human intelligence and expertise. The security strategy decisions about alert dismissals, rule modifications, and escalations play the most crucial role. Such decisions that use inputs from data science technology call for security professionals who must determine further risk remediation strategies.
With a rooted data science cybersecurity system merged with human expertise, companies can hope for full visibility of the security risks in their cloud infrastructure.
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