Advanced AI is transforming the cyberthreat and cyberdefense sides of enterprise security. On the threat side, AI dramatically increases attack sophistication. On the defense side, AI technologies, like machine learning (ML) and neural networks, allow enterprises to detect, analyze, and respond to threats with unprecedented speed and accuracy. This side-by-side buildup has led to hackers and enterprises competing for the most effective AI technologies.
In this environment, it is vital for enterprises aiming to boost security and safeguard their data to adopt AI-driven solutions that can process and analyze vast amounts of data in real time, while autonomously identifying and mitigating threats. This level of automation speeds up response time and frees security personnel to focus on more strategic tasks, thereby improving the organization’s overall security posture.
The Great Cybersecurity Race
In September 2023, hacker groups ALPHV and Scattered Spider launched a cyberattack against MGM Resorts that disabled the company’s “online reservation systems, digital room keys, slot machines, and websites.” The ransomware attack extended over 10 days and cost MGM millions of dollars. The attack was just another in a long line of incidents that have led to companies of all types and sizes shoring up their defenses to stay ahead of ever-evolving hackers.
CrowdStrike’s 2024 Global Threat Report found that attackers are now launching social engineering attacks (the type of attack used in the MGM incident) that use generative AI (GenAI) to craft highly effective phishing and smishing (SMS phishing) messages. That threat report also discovered that attackers frequently target organizations’ vulnerabilities as they expand technologically and shift more operations to the cloud. Many companies decide to fight fire with fire by turning to AI-driven defense initiatives to combat the greater sophistication of these AI-based cyberattacks.
Using AI to Beat AI
Companies leverage AI to thwart attackers by creating AI-based smart detection systems that continually analyze user behavior and system activity to identify deviations from normal activities. AI has proven highly effective at analyzing vast amounts of data and detecting anomalies that could indicate threats. Companies can respond faster to attacks and even automate responses with AI tools, ensuring a swifter and more effective defense.
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Another significant benefit of AI-driven security is that AI is continually learning and getting stronger, enabling the use of predictive analytics to forecast potential threats based on current trends and historical data. This allows organizations to adopt a proactive defensive posture instead of a reactive one since AI can help them anticipate and prepare for attacks before they occur. A recent report on cybersecurity trends warned companies to be on the lookout for nation-state-backed attacks that utilize GenAI to create more realistic scam emails. One way to defend against this type of attack is to use AI in threat intelligence and data analysis to quickly digest large amounts of data and react faster and in greater scale when possible threats are identified.
Additional defense strategies that utilize AI include intelligent encryption and neural networks. Intelligent encryption systems adjust encryption policies based on the context in which data is accessed or used. For instance, when data is accessed remotely, it is encrypted at a higher level than when accessed in a more secure corporate environment. Neural networks, on the other hand, are a class of machine learning (ML) models inspired by the structure and function of the brain. They include interconnected nodes (or neurons) that work together to process data and generate output. These neural networks can be trained to discover patterns and relationships in data, which then fosters better prediction and decision-making by the AI system.
The Future of AI in Enterprise Security
With the increased sophistication of cyberattacks and data breaches, traditional data loss prevention (DLP) methods are no longer enough. AI enhances an organization’s ability to detect and prevent data loss in real time, offering more robust protection against modern threats. In addition, AI-driven security measures can be trained to comply with stringent data protection regulations, work seamlessly with complex, distributed networks, and reduce human error that frequently comes with manual processes and analysis of large amounts of data. The continuous evolution of AI technology also helps ensure that security measures can adapt to new and emerging threats, keeping enterprise defenses more robust and relevant. That’s why the key for organizations to stay safe today and in the future is to evolve technologically, which means embracing AI.
The Evolution of Enterprise Security
AI can help organizations strengthen their security without overburdening existing personnel or hiring many new workers. AI also fills the gaps so enterprises can build more robust security strategies. But not all AI technologies are simple, set-them-and-forget-them solutions. AI use and access to sensitive data have inherent ethical and privacy concerns. AI technologies may also exhibit false bias and issue false positives that can lead to unfair or discriminatory outcomes.
To maximize the benefits of AI and minimize the risks, it’s imperative for enterprises to practice accountability and transparency in their security initiatives. It’s also important for leaders to remember what Stephane Nappo, the Global Head of Information Security for Société Générale International Banking pole since 2011, recently said, “It takes 20 years to build a reputation and a few minutes of cyber-incident to ruin it.” Enterprises that embrace this, conduct extensive AI research before implementation, and follow best-use practices can create an enterprise security landscape that comprehensively protects them against today’s ever-growing array of complex cyberthreats.
About the Author
Animesh Kumar is an enterprise IT, senior solution architect with over 22 years of industry expertise, including more than 10 years of experience designing highly scalable, fault-tolerant applications and cloud-based solutions. Animesh is also actively researching emerging technologies such as AI, machine learning, and cloud computing. He has authored peer-reviewed articles and participates in judging and reviewing scholarly works. Animesh has a master’s degree in computer science from the Illinois Institute of Technology and a bachelor’s degree in computer technologies from Nagpur University in India. Connect with Animesh on LinkedIn.
