The role of generative AI in improving security

We know that the increasing number of cyber attacks are the result of a complex digital environment. Defenders must always be right, while threat actors must only be right once. The threat landscape is constantly evolving and organizations are struggling to keep up with limited budgets and resources.

This leads to unfocused decision making and inadequate strategic planning at all levels of the organization. What’s the solution? We don’t have all the answers yet, but we know generative AI can help.

What is the role of Generative AI in cybersecurity?

For decades, the industry has adopted an ineffective and expensive point solution approach, where threats are met with technologies that prioritize incident detection and response. Companies use hundreds of different cybersecurity solutions, each with their own analytics and no consistent reporting.

All of these tools can lead to duplicative and ineffective alerts without a clear way to tangibly reduce risk or concisely communicate an organization’s security status.

Preventing cyber attacks therefore requires full visibility of all assets and risks, broad context of potential security threats, and clear metrics to objectively measure cyber risk. Organizations that can Predict cyber attacks and communicate these risks for decision support They will be in the best position to defend themselves against emerging threats.

In this context, artificial intelligence (AI) plays a fundamental role in the evolution of security today. Gartner defines AI as the application of advanced analytics and logic-based techniques, including machine learning (ML), to interpret events, support and automate decisions, and take action.

This definition is consistent with the current and evolving state of AI technologies and capabilities. It is well known that AI now involves probabilistic analysis, which combines probability and logic to assign a value to uncertainty.

Even with broad visibility into the attack surface, it is difficult for security teams to analyze, interpret findings, and determine what steps need to be taken to mitigate risk as quickly as possible. As a result, they are in constant reaction mode, giving maximum effort but always being one step behind their attackers.

In this sense, I point out that generative AI is an important support as it can contribute to:

• log analysis;
• anomaly detection;
• scanning capabilities;
• promoting data anonymization;
• creating secure passwords;
• Phishing detection;
• monitoring suspicious network activity;
• Analyzing cyber attack data to prevent threats and take preventive measures.

It is true that artificial intelligence is still at an early stage and will be necessary for this. expert professionals truly verify their benefits and understand their risks to data security. But what’s intriguing is that the ability to learn beyond machine learning (whether by identifying patterns or groupings within a data set) is scary in both its potential benefits and risks.