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Staying Ahead of Adversarial AI with Incident Response Automation
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A Security Engineering Commentary from industry insider Rohan Bafna , SecOps Engineer.
The security operations (SecOps) community constantly seeks advancements in incident response. Consolidating security telemetry data, upgrading your organization’s cybersecurity posture, and integrating with various artificial intelligence (AI) and machine learning (ML) engines are essential to combatting adversarial AI and ML models.
Hackers will continue to have the upper hand without innovation, including adopting proactive measures and natural language processing (NLP) in the SecOps space. Powered by WormGPT and FraudGPT, hackers and scammers will continue to drive the cost of business higher as organizations pay more for cyber insurance.
This blog discusses the need to phase out traditional security measures and the need to adopt various automation, adaptation control, and processes SecOps teams can use to combat adversarial AI.
Overview of the Rise of Adversarial AI
Hackers, like organizations, invest in artificial intelligence (AI) and machine learning algorithms (ML) and capabilities. Organizations invest in AI to help transform their business operations and product development, optimize their financial systems, and provide greater automation within their customer success and marketing operations.
Hackers also invest in AI and ML with predictive analytics to help develop their cyberattack tools, increase their attack automation capabilities globally, and optimize their ability to collect ransom in Bitcoin and other cryptocurrencies faster and more securely.
As both entities continue to invest their capital, which one continues to show a return on their investment (ROI)? Surprisingly, the hackers see increased revenue from their illicit activities. Legitimate organizations continue to invest large amounts of capital with little hope of a return, even after adopting new robust security measures.
Types of Adversa
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