The conventional wisdom held that every business should undergo digital transformations in order to improve internal operations and client relationships. They were subsequently convinced that cloud workloads were the future and that elastic computer systems made businesses more nimble and cost-effective, scaling up and down as needed. While digital transformations and cloud migrations are unquestionably wise decisions that all organizations should make (and those that haven’t, what are you doing! ), IT Security systems designed to protect such IT infrastructures have fallen behind threats capable of undermining them.

There Is An Abundance Of Data And Tools, But Not Enough Resources

As internal business operations become more digitized, massive amounts of data are generated. As data accumulates, IT and cloud security systems face increased strain because more data means more IT Security breaches.

In early 2022, the Lapsus$ cyber extortion gang went on a hacking spree, stealing source code and other valuable data from well-known companies such as Nvidia, Samsung, Microsoft, and Ubisoft. The attackers had initially exploited the companies’ networks through phishing attacks, which resulted in a contractor being compromised, granting the hackers full access to the contractor’s Okta account (an ID and authentication service). The source code and other files were then made public.

This and other data breaches target organizations of all sizes, from large multinational corporations to small startups and growing businesses. Sadly, most firms have too many data points for security engineers to find, meaning that present network security systems and approaches are faulty. Organizational security tools can be overwhelming.

Furthermore, organizations are frequently overwhelmed by the various tools available to address these security challenges. Too many tools necessitates an exorbitant investment of time and energy, not to mention resources, in researching, purchasing, integrating, and running these tools. This places additional strain on executives and IT teams.

With so many moving parts, even the best security engineers are rendered helpless in attempting to mitigate potential network vulnerabilities. Most organizations simply do not have the resources to invest in cybersecurity.

As a result, they face a double-edged sword: Their operations rely on the highest levels of security, but achieving that comes at a cost that most businesses simply cannot afford.

To protect sensitive data in businesses and organizations, a new approach to computer security is desperately needed. The current method that is considered conventional is one that combines rule-based systems with a variety of tools in order to cover all of the bases. This practice wastes security analysts’ time by enabling and disabling rules and logging in and out of various systems in an attempt to determine what is and isn’t a threat.

Organizations can use machine learning to overcome security challenges

Machine learning (ML) algorithms are the best option for organizations dealing with these ever-present pain points. In this way, algorithms can develop models based on behavior, providing a secure IT infrastructure for any business or organization. Any organization or business looking to revamp its security infrastructure should prioritize a tailored ML-based SaaS platform that operates efficiently and in a timely manner.

A security and compliance solution, cloud-native application protection platforms (CNAPP), can enable IT security teams to deploy and run secure cloud native applications in automated public cloud environments. CNAPPs can utilize machine learning techniques on cloud data to discover accounts with unusual permissions (one of the most popular and unreported attack pathways) and host and open source vulnerabilities.

ML can also weave together a large number of anomalous data points to create rich stories about what’s going on in a given network, which would take a human analyst days or weeks to uncover.

CSPM and CIEM software

These platforms make use of machine learning in two ways. Platform security is handled by cloud security posture management (CSPM), which monitors and delivers a full inventory to identify any deviations from customized security objectives and standard frameworks.

Cloud infrastructure entitlements management (CIEM) focuses on identity security by identifying all possible access to sensitive data through the permissions of each identity. Furthermore, host and container vulnerabilities are considered, implying that ongoing attacks can be treated with the appropriate urgency. Anomaly observed on a host with known vulnerabilities, for example, is far more serious than on a host without known vulnerabilities.

ML-based SaaS can also outsource SOC and SIEM to a 3rd person and use their ML algorithm. SaaS can use ML to handle critical security functions such as network monitoring, log management, single-sign on (SSO), and endpoint alerts, as well as access gateways, with dedicated security analysts investigating any and all threats.

SaaS ML platforms provide the most efficient means of covering all security bases. By applying ML to all actions, organizations can focus on their business goals while algorithms bring all the context and insights into a security platform.

Using third-party experts

Even if an organization has the personnel to make it a reality, running complex ML algorithms to learn a baseline of what is normal in a given network and assessing risk is difficult. For the vast majority of organizations, using third-party platforms that have already built and trained algorithms on data results in a more scalable and secure network infrastructure that is far more convenient and effective than in-house options.

Using a trusted third party to host a SaaS ML platform frees up time for organizations to focus on internal needs, while the algorithms analyze network behavior to provide the highest levels of security.

When it comes to network security, relying on a trusted third party is analogous to hiring a locksmith to repair your home’s locks. Most of us have no idea how our home’s locks work, but we rely on a professional to do the job. Using third-party experts to run ML-algorithms gives businesses and organizations the flexibility and agility they need to thrive in today’s digital world.

By maximizing this new approach to security, all types of organizations can overcome complex data problems without worrying about the resources and tools required to protect their network, providing unparalleled peace of mind.

Read More: What Impact Will AI Have in the Future on Our Lives?

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