Approaching Security Operations With Machine Learning And AI

Approaching Security Operations With Machine Learning And AI


The world has entered a new era of cyberthreats, including actual cyber warfare against strategic digital assets. The Cybersecurity and Infrastructure Security Agency (CISA) is warning organizations to put their “shields up” to protect against cyberattacks. Key industries such as banking, manufacturing, and critical infrastructure are especially vulnerable.

Machine learning (ML) is the ability of a computer to learn and evolve. And artificial intelligence (AI) is used when the decision-making involved in performing a task is delegated to a machine through algorithms. Organizations need real-time threat detection and analysis – because it’s nearly impossible for humans to manually monitor all activity across all of our systems, both within the network perimeters as well as outside on the Internet. This computer system should be trained to detect threats to automatically respond with various processes that address each situation through monitoring of logs, identity and access management systems, threat intelligence feeds, and numerous other sources relevant to overall organization security.

Machine Learning (ML) can analyze millions of files in short order. Once data patterns are analyzed and understood, security incidents can be correlated with each other into a single alert to prompt a response. For example, ML uses many sources of data to discern that a threat is present and can take action to respond to that threat without the need for human intervention. When events are similar, they can ideally be dealt with using the same response mechanism. This type of automation is a force multiplier for a SOC team’s ability to monitor, detect and respond to malicious activities in the infrastructure.

Getting Started with AI and ML

In the cybersecurity world, there are typically two opposite ways to approach security: automation or code. But as Shannon Ward correctly explained, building your ML/AI can be unnecessary and a waste of time. So if you’re looking into security measures, you must select a platform that provides user-friendly tools for easy access, the option of deploying models hosted on a cloud environment (GRID), and scalable feature sets depending on how many users your business has! It’s important to have the data secured in real-time. Otherwise, you’re beginning with historical events, and that doesn’t help your ability to stop an attack early on. Make sure you’re looking at specific use cases of the data – especially things that might break or be unexpected. Seek out an unusual activity or user behavior by asking someone who can be closely connected with users, such as a product manager or a digital designer.

Response for mitigation

With SOAR, you can integrate systems and efficiently pull data from various systems like a Threat Detection system. SOAR is a platform that facilitates detecting security risks based on real-time information obtained in threat intelligence feeds and endpoint detection systems (EDS). Security automation platforms automate tasks for event management, incident response and for security operations teams to handle incidents associated with the evaluation of responses to unauthorized network intrusions. It also automates responses to detect potentially malicious software, attacks, or rule violations. Social Engineering: Confidence is an exploit’s best friend! In today’s digital world, we are constantly being probed by potential hackers wanting us to provide them with access codes while they change something. Do not underestimate the power of social engineering techniques that can gain access to your passwords, phone number, or online accounts. Of course, there are also playbooks for security incident response. Our Integrated Adaptive Cyber Defense (IACD). Cybersecurity Framework’s Five Functions: Identity, Protect, Detect, Respond and Recover. 


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