Top Ways Machine Learning Can Help Crack the IT Security Problem
A decade ago, it was common wisdom that all businesses should undergo digital transformations in order to improve their internal operations and client relationships. They were then told that cloud workloads were the future and that elastic computing solutions allowed them to operate in a more agile and cost-effective way, scaling up or down as required.
While digital transformations and cloud migrations are smart decisions that every organization should make, and those that haven’t already, what are they doing? Security systems designed to protect these IT infrastructures aren’t able to keep up with the increasing threat of compromising them.
Plenty of data and tools, but not enough resources
Boatloads of data are being generated as internal business operations become more digitalized. Data is becoming more and more complex, which puts IT and cloud security systems under greater pressure. More data means that there are greater security threats.
A cyber-extortion gang known as Lapsus$ went on a hacking spree in early 2022. They stole source code and other valuable information from top companies like Nvidia and Samsung, Microsoft, and Ubisoft. The hackers had initially exploited the networks of the companies using phishing attacks. This led to a contractor being compromised and the hackers gaining all access via Okta (an identification and authentication service). The hackers then released the source code and other files online.
This attack, along with many others, targets organizations of all sizes, from large multinational corporations to small startups and growing businesses. Security engineers are often overwhelmed by the sheer volume of data in organizations. This means that current methods and systems to protect a network are flawed.
Overwhelming security tools for organizations
Organizations are often overwhelmed with the many tools available to address these security problems. Organizations can spend a lot of energy and time researching, buying, integrating, and managing too many tools. This adds stress to IT departments and executives.
There are so many moving parts that even the most skilled security engineers can’t help but be overwhelmed by trying to minimize potential vulnerabilities within a network. Many organizations don’t have the funds to invest in cybersecurity.
They are therefore subject to a double-edged sword. Their business operations depend on high levels of security but that comes at a price that most companies simply cannot afford.
A new approach to computer security must be developed to protect the sensitive data of businesses and organizations. Current security systems are rules-based and often include multiple tools to cover all bases. Security analysts are left spending their time disabling and enabling rules and logging in and out of various systems to determine what the threat is.
Organizational security problems can be overcome with ML solutions
Machine learning (ML) algorithms are the best solution for companies dealing with these ever-present problems. Algorithms can be trained to create a model based on behaviors. This will give any organization a secure IT environment. Any business or organization looking to improve its security infrastructure must prioritize an ML-based SaaS platform. It should be efficient and timely.
Cloud-native app security platforms (CNAPP), are security and compliance tool that empowers the IT security team to deploy cloud native apps in public clouds. CNAPPs can use ML algorithms to cloud-based data to detect accounts with unusual permissions (an undetected attack path) and expose potential threats, including open source and host vulnerabilities.
ML can also combine many different data points to tell rich stories about what’s going on in a network. This is something that would take a human analyst weeks or days to discover.
CSPM and CIEM tools
Two primary methods are used to leverage ML in these platforms. Cloud security posture management, (CSPM), manages platform security by monitoring and providing a complete inventory to identify deviations from standard security frameworks and custom security objectives.
Cloud infrastructure entitlements Management (CIEM) is focused on identity security. It allows for all access possible to sensitive data with every identity’s permission. Host and container vulnerabilities are also considered so that ongoing attacks can be dealt with with the right urgency. A host with known vulnerabilities may exhibit anomalous behavior that is more urgent than one without.
Another ML-based SaaS option is to outsource the security operations center (SOC) Security incident and event management (SIEM), and other functions to third parties and the benefit of their ML algorithm. Security analysts dedicated to investigating all threats, SaaS can use machine learning (ML) to manage critical security functions like network monitoring, log management, Single-sign on (SSO), endpoint alerts, and access gateways.
SaaS ML platforms are the best way to ensure security. Organizations can use ML to apply all behaviors. Algorithms pull all context and insights into one security platform, allowing them to focus on their business goals.
Third-party experts are a good option
It is difficult to run complex ML algorithms in order to establish a baseline for normal network behavior and assess risk. This is even if the organization has the resources to do it. The majority of organizations use third-party platforms to train algorithms on data. This creates a more secure and scalable network infrastructure achieving this much more efficiently and effectively than any home-grown options.
Organizations can spend more time on their internal business needs by relying on trusted third parties to host a SaaSML platform. The algorithms analyze the network behavior to ensure the highest level of security.
Relying on a trusted third party for network security is the same as hiring a locksmith to fix your locks. While most of us aren’t familiar with the mechanism behind our locks, we can trust an expert to help us. Third-party experts can run ML algorithms, giving businesses and organizations the agility and flexibility they need in today’s digital world.
This new security approach allows organizations of all sizes to solve complex data problems without worrying about their network protection resources. It gives them unparalleled peace of mind.