How Machine Learning Can Help Solve IT Security Problems

Machine Learning

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Less than a decade ago, the prevailing view was that all companies should undergo a digital transformation to strengthen internal operations and improve customer relationships. Next, we were told that cloud workloads are the future and that elastic compute solutions will allow us to operate in a more agile and cost-effective manner, scaling up and down as needed.

Digital transformation and moving to the cloud is arguably a smart decision for all organizations to make (what are those that haven’t done yet!), but how can such an IT infrastructure Security systems designed to protect are failing to keep up with potential threats. weaken them.

As internal operations become increasingly digital, a large amount of data is generated. As data piles up, it puts more pressure on IT and cloud security systems, as more data means more threats of security breaches.

In early 2022, a cyber extortion group known as Lapsus$ hacked to steal source code and other valuable data from prominent companies such as Nvidia, Samsung, Microsoft, and Ubisoft. Attackers initially exploited corporate networks using phishing attacks. This compromised the contractor and gave the hacker access to everything the contractor had through his Okta (identity and authentication service). The source code and other files were then leaked online.


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This attack and numerous other data breaches have targeted all kinds of organizations, from large multinationals to small start-ups and growing companies. Unfortunately, most organizations have too many data points that security engineers cannot identify. In short, current systems and methods for securing networks are fundamentally flawed.

Additionally, organizations are often overwhelmed with the variety of tools available to tackle these security challenges. Too many tools mean that organizations invest a great deal of time and energy, not to mention resources, in researching, purchasing, integrating, and running these tools. This puts additional stress on executives and IT teams.

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

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

A new approach to computer security is desperately needed to protect the sensitive data of businesses and organizations. The current standard approach usually consists of a rule-based system with multiple tools covering all bases. This practice leads security analysts to waste time enabling and disabling rules and logging in and out of various systems to establish what is considered a threat and what is not. will be

ML Solutions to Overcome Your Organization’s Security Challenges

The best option for organizations to address these ever-present pain points is to leverage machine learning (ML) algorithms. In this way, algorithms can train models based on behavior and provide a secure IT infrastructure for any company or organization. A ML-based SaaS platform tuned to operate efficiently and in a timely manner should be a priority for organizations and businesses looking to modernize their security infrastructure.

Cloud Native Application Protection Platform (CNAPP), a security and compliance solution, enables IT security teams to deploy and run secure cloud native applications in automated public cloud environments. CNAPP applies ML algorithms to cloud-based data to detect accounts with unusual permissions (one of the most common and undetected attack paths) and detect vulnerabilities such as host and open source vulnerabilities. Potential threats can be exposed.

ML can also combine many anomalous data points to create a rich story about what is happening in a particular network. This is something that would take a human analyst days or weeks to discover.

These platforms leverage ML through two main practices. Cloud Security Posture Management (CSPM) handles platform security by monitoring and providing a complete inventory to identify customized security objectives and deviations from standard frameworks.

Cloud Infrastructure Entitlement Management (CIEM) focuses on identity security by understanding all possible accesses to sensitive data through authorization of all identities. In addition to this, host and container vulnerabilities are also taken into account so that appropriate urgency can be applied to ongoing attacks. For example, anomalous behavior seen on hosts with known vulnerabilities is much more immediate than on hosts without known vulnerabilities.

Another ML-based SaaS option is to outsource your security operations center (SOC) and security incident and event management (SIEM) functions to a third party and leverage third-party ML algorithms. With dedicated security analysts investigating every threat, SaaS can use ML to handle critical security functions such as network monitoring, log management, single sign-on (SSO), endpoint alerts, and access gateways. increase.

A SaaS ML platform provides the most effective way to cover all security bases. Applying ML to everything you do allows organizations to focus on their business goals, and algorithms bring all the context and insight they need into a single security platform.

Rely on Third Party Experts

Running complex ML algorithms to learn a baseline of what is normal for a given network and assessing risk is difficult even if your organization has the resources to make it happen. For the vast majority of organizations, using third-party platforms that have already built algorithms to train on data produces a more scalable and secure network infrastructure, making it far more convenient and effective than homegrown options. can be executed effectively.

Relying on a trusted third party to host their SaaS ML platform allows organizations to spend more time on their internal needs, while algorithms study network behavior to provide the highest level of security.

When it comes to network security, relying on a trusted third party is like hiring a locksmith to fix your house locks. Most of us don’t know how our home locks work, but we trust outside experts to do the job. It gives businesses and organizations the flexibility and agility they need to operate in today’s digital world.

By taking full advantage of this new approach to security, organizations of all types will be able to overcome complex data challenges without worrying about the resources and tools they need to secure their networks. It gives you peace of mind.

The Terrifying Ganesha (Steven Padefat)) Technical Sales Architect at . global dots.

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