GREENTECH

How machine learning can help crack the IT security problem

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Less than a decade ago, the prevailing wisdom was that every business should undergo digital transformations to boost internal operations and improve client relationships. Next, they were being told that cloud workloads are the future and that elastic computer solutions enabled them to operate in an agile and more cost-effective manner, scaling up and down as needed. 

While digital transformations and cloud migrations are undoubtedly smart decisions that all organizations should make (and those that haven’t yet, what are you doing!), security systems meant to protect such IT infrastructures haven’t been able to keep pace with threats capable of undermining them.  

Plenty of data and tools, not enough resources

As internal business operations become increasingly digitized, boatloads more data are being produced. With data piling up, IT and cloud security systems come under increased pressure because more data leads to greater threats of security breaches. 

In early 2022, a cyber extortion gang known as Lapsus$ went on a hacking spree, stealing source code and other valuable data from prominent companies, including Nvidia, Samsung, Microsoft and Ubisoft. The attackers had originally exploited the companies’ networks using phishing attacks, which led to a contractor being compromised, giving the hackers all the access the contractor had via Okta (an ID and authentication service). Source code and other files were then leaked online.

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This attack and numerous other data breaches target organizations of all types, ranging from large multinational corporations to small startups and growing firms. Unfortunately, in most organizations, there are simply too many data points for security engineers to locate, meaning current systems and methods to safeguard a network are fundamentally flawed. 

Overwhelming security tools for organizations

Additionally, organizations are often overwhelmed by the various available tools to tackle these security challenges. Too many tools means organizations invest an exorbitant amount of time and energy — not to mention resources — in researching, purchasing and then integrating and running these tools. This puts added stress on executives and IT teams. 

With so many moving parts, even the best security engineers are left helpless in trying to mitigate potential vulnerabilities in a network. Most organizations simply don’t have the resources to make cybersecurity investments. 

As a result, they are subject to a double-edged sword: Their business operations rely on the highest levels of security, but achieving that comes at a cost that most organizations simply can’t afford. 

A new approach to computer security is desperately needed to safeguard businesses’ and organizations’ sensitive data. The current standard approach comprises rules-based systems, usually with multiple tools to cover all bases. This practice leaves security analysts wasting time enabling and disabling rules and logging in and out of different systems in an attempt to establish what is and what isn’t considered a threat. 

ML solutions to overcome security challenges for organizations

The best option for organizations dealing with these ever-present pain points is to leverage machine learning (ML) algorithms. This way, algorithms can train a model based on behaviors, providing any business or organization a secure IT infrastructure. A tailored ML-based SaaS platform that operates efficiently and in a timely manner must be the priority of any organization or business seeking to revamp its security infrastructure.

Cloud-native application protection platforms (CNAPP), a security and compliance solution, can empower IT security teams to deploy and run secure cloud native applications in automated public cloud environments. CNAPPs can apply ML algorithms on cloud-based data to discover accounts with unusual permissions (one of the most common and undetected attack paths) and uncover potential threats including host and open source vulnerabilities.

ML can also knit together many anomalous data points to create rich stories of what’s happening in a given network — something that would take a human analyst days or weeks to uncover.

CSPM and CIEM tools

These platforms leverage ML through two primary practices. Cloud security posture management (CSPM) handles platform security by monitoring and delivering a full inventory to identify any deviations from customized security objectives and standard frameworks.

Cloud infrastructure entitlements management (CIEM) focuses on identity security by understanding all possible access to sensitive data through every identity’s permission. On top of this, host and container vulnerabilities are also taken into account, meaning correct urgency can be applied to ongoing attacks. For example, anomalous behavior seen on a host with known vulnerabilities is far more pressing than on a host without known vulnerabilities.

Another ML-based SaaS option is to outsource the security operations center (SOC) and security incident and event management (SIEM) function to a third party and benefit from their ML algorithm. With dedicated security analysts investigating any and all threats, 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. 

SaaS ML platforms offer the most effective way to cover all the security bases. By applying ML to all behaviors, organizations can focus on their business objectives while algorithms pull all the necessary context and insights into a single security platform. 

Relying on third-party experts

Running the complex ML algorithms to learn a baseline of what is normal in a given network and assessing risk is challenging — even if an organization has the personnel to make it a reality. For the majority of organizations, using third-party platforms that have already built algorithms to be trained on data produces a more scalable and secure network infrastructure, doing so far more conveniently and effectively than home grown options.

Relying on a trusted third party to host a SaaS ML platform enables organizations to dedicate more time to internal needs, while the algorithms study the networks’ behavior to provide the highest levels of security.

When it comes to network security, relying on a trusted third party is no different than hiring a locksmith to repair the locks on your home. Most of us don’t know how the locks on our homes work but we trust an outside expert to get the job done. Turning to third-party experts to run ML-algorithms enables businesses and organizations the flexibility and agility they need to operate in today’s digital environment. 

Maximizing this new approach to security allows all types of organizations to overcome their complex data problems without having to worry about the resources and tools needed to protect their network, providing unparalleled peace of mind. 

 Ganesh the Awesome (Steven Puddephatt) is a technical sales architect at GlobalDots.

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