Ultimate Guide to AI Cybersecurity: Benefits, Risks, and Rewards

AI, defined as a form of machine learning based on neural network architecture, is more than a trending topic—it’s growing so fast that even Elon Musk can’t pause AI’s rapid development. And the use of AI is showing no signs of slowing down any time soon. A recent report by Bloomberg Intelligence projected the generative AI market will experience a compound annual growth rate of 42% from $40 billion in 2022 to $1.3 trillion over the next 10 years.

With this projected increase in AI adoption and expansion, many organizations are evaluating whether artificial intelligence can help improve business operations that normally require human intelligence, such as analyzing vast amounts of data, managing the increasing complexity of environments, and as a powerful tool for implementing cybersecurity strategies to protect business-critical elements like customer data and other sensitive information.

However, to paint the full picture of the pivotal role AI has in today’s digital world, let’s take a closer look at why the benefits of AI in cybersecurity are only one critical piece of what you need to know to make more informed decisions around implementing AI solutions in your security operations.

• Benefits of using AI in cybersecurity
• Security risks of artificial intelligence
• U.S. Government warns about dangers of AI-based cyberattacks
• The transformative power of AI cybersecurity
• The future of Converged Endpoint Management is autonomous
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Benefits of using AI in cybersecurity

While the full extent and implications of AI capabilities within the cybersecurity industry are not yet understood, here is a simplified overview of common problem areas in which AI-powered systems could show promising results:

Improve threat detection

AI can analyze massive amounts of data from various sources, such as network traffic, system logs, user behavior, and external intelligence to identify anomalies and suspicious activity that may indicate known or unknown adversarial attacks, such as malware, security breaches, ransomware, phishing, denial-of-service, or advanced persistent threats.

Automate responses to security threats

AI can help minimize the effects of cyberattacks with the deployment of automated responses to attacks and can prioritize incident response based on actual risk to quickly and efficiently isolate infected systems, endpoint devices, or networks. AI can also provide real-time mitigation and highly tailored alerts, recommendations, and guidance to security teams on how to recover and restore normal system functions.

Accelerate incident investigation

When an incident occurs, there is a lot of data an analyst must acquire, review, and analyze to learn the breadth and depth of an attack. This can be a time-consuming and tedious process, especially when dealing with large and complex incidents. AI can help shorten that process by automating the collection, correlation, and analysis of data from various sources, such as logs, network traffic, endpoints, and threat intelligence. AI can help investigators gain a comprehensive understanding of malicious activities and their impact in a fraction of the time.

Provide predictive threat prevention

AI models like deep learning can help prevent cyberattacks by proactively identifying and automatically blocking potential threats before they can compromise systems. AI can also use different algorithms, such as supervised, unsupervised, or semi-supervised learning methods, to learn from historical data and perform predictive analytics to better anticipate future incidents.

[Read also: Are cybersecurity analytics missing from your security strategy?]

Determine root cause

AI can help eliminate human error and false positives sometimes found in traditional data science efforts like root cause analysis that rely on manual analysis, collection, and extraction of insights from large and complex data sets, so teams can gain a more accurate sense of any potential vulnerabilities or weaknesses that enabled the attack, improve their security posture, shrink the potential attack surface, and free up human analysts for more important and creative tasks.

While you can see how AI is a powerful tool that can enhance cybersecurity by providing new solutions and opportunities for remediating and defending against cyber threats, improving cyber situational awareness and increasing cyber resilience, it is equally as crucial to recognize that these same benefits of AI in cybersecurity can also create potential risks when AI falls into the hands of malicious hackers.

Let’s explore how cybercriminals and other bad actors are leveraging AI technologies to launch more sophisticated attacks, and what this means for AI and cybersecurity.

Security risks of artificial intelligence

While organizations and users are incorporating AI and related technology into many different business processes, threat actors are also using similar AI tools to cause damage more quickly and easily.

Unfortunately, many are unprepared for this emerging threat from AI. A 2023 report on the state of AI by consulting firm McKinsey showed only 38% of respondents were actively mitigating the cybersecurity risks of generative AI, a statistic made even more shocking when you learn this percentage actually decreased by 13% when compared to the results of the same study conducted last year while security and privacy threats continue to grow.

Potential threats of AI use in cyberattacks

The increasing complexity and sophistication of these types of AI-based cybersecurity incidents can pose significant challenges for traditional security solutions, such as antivirus software, firewalls, and intrusion detection systems. Since these solutions often rely on more static and rule-based systems to identify and block known attacks, they may fail to detect new or unknown attacks that exploit zero-day vulnerabilities or use advanced techniques such as encryption, obfuscation, or polymorphism.

One way threat actors are leveraging AI is to perform adversarial attacks, which are designed to exploit vulnerabilities in machine learning models like neural networks by crafting inputs that look normal to humans but are built to fool machine learning models and manipulate their outputs. An example of an adversarial attack is using AI to slightly alter an email to bypass a corporate spam filter.

Cybercriminals are also using generative AI models, which encompass a wide subset of AI types such as large language models (LLMs) and natural language processing (NLP), to improve their social engineering techniques. For example, threat actors are leveraging AI algorithms to create more convincing phishing emails. And these types of incidents are already happening on an enormous scale.

According to a 2023 report by cybersecurity company SlashNext that surveyed more than 300 cybersecurity professionals, there’s been a 1,265% increase in malicious phishing attacks since the launch of ChatGPT at the end of 2022 driven by cybercriminals using generative AI tools to write business email compromise (BEC) and other phishing emails.

Some of the most common users of large language model chatbots are cybercriminals leveraging the tool to help write BEC attacks and systematically launch highly targeted phishing attacks.

On average, the report found some 31,000 phishing attacks were sent daily. About half of the cybersecurity professionals surveyed said they had received a BEC attack, and three-quarters reported being targets of phishing attacks.

Phishing emails have historically been easy to detect because they include misspelled words, poor grammar, or other deficiencies. However, phishing emails created by AI systems typically have higher rates of being opened by users than manually created phishing emails. Using AI tools, phishers can now create very personalized and targeted phishing emails by analyzing past content to make the emails highly convincing and more easily trick users into clicking on links within the emails to inadvertently launch attacks.

Attackers are also leveraging generative AI to develop unique content able to evade controls for other types of AI-based attacks, including synthetic identities and deepfakes. For example, threat actors can use AI to deepfake peoples’ voices to launch spear phishing or other attacks.

[Read also: What is phishing? How phishing works and what to look for]

U.S. Government warns about dangers of AI-based cyberattacks

The growing concerns around the use of AI technologies and social engineering attacks have quickly escalated from being an issue just within private sectors to becoming a national and global discussion, with several federal agencies spearheading efforts to protect people and assets from cyber and physical threats.

For example, the U.S. Federal Trade Commission (FTC) issued this warning about deep fakes in 2023 年 3 月:

You get a call. There’s a panicked voice on the line. It’s your grandson. He says he’s in deep trouble—he wrecked the car and landed in jail. But you can help by sending money. You take a deep breath and think. You’ve heard about grandparent scams. But darn, it sounds just like him. How could it be a scam? Voice cloning, that’s how.

Government agencies continue to play a crucial role in recognizing the importance and complexity of AI in cybersecurity and infrastructure security. Some of the notable federal legislation around cybersecurity includes Executive Order 14028, “Improving the Nation’s Cybersecurity,” issued in 2021 年 5 月, which requires federal agencies to enhance cybersecurity and software supply chain integrity.

Additionally, the Cybersecurity and Infrastructure Security Agency Act of 2018 established the Cybersecurity and Infrastructure Security Agency (CISA), whose mission is to lead and coordinate efforts to enhance the security and resilience of the nation’s critical infrastructure. CISA also offers a variety of services and resources that can assist organizations in assessing their cybersecurity risks, implementing best practices, and responding to security incidents.

The transformative power of AI cybersecurity

The use of AI and automation in cybersecurity presents both challenges and opportunities. While cybercriminals are leveraging these technologies to launch more sophisticated attacks, cybersecurity vendors are also using AI and automation to develop novel solutions to counter such attacks.

You should expect to see more AI-focused tools becoming available in the coming months, as vendors race to integrate generative AI capabilities, automation, and related technologies to create novel solutions to help organizations better counter cybersecurity threats. Soon, these AI-powered solutions could help improve a wide range of cybersecurity use cases, such as data loss prevention, antivirus/antimalware, fraud detection, identity and access management, intrusion detection/prevention systems, risk and compliance management, IT asset inventory management, and security and vulnerability management to name a few.

As the use of AI and automation continues to grow, security experts, cybersecurity teams, and organizations need to stay on top of the latest AI-generated threats by also leveraging AI-based cybersecurity systems to enhance their security capabilities and resilience to build even stronger cyber defense systems.

[Read also: Why the Chief AI Officer is here to stay]

The future of Converged Endpoint Management is autonomous

Tanium is leading the way toward an autonomous future for IT, information security, operations, and risk and compliance teams. We are closely monitoring AI and automation trends and building our product roadmap around these breakthrough technologies that are creating a vast array of exciting new opportunities.

At the center of Tanium’s go forward strategy is Autonomous Endpoint Management (AEM), which represents the most ambitious step in the evolution of our Converged Endpoint Management (XEM) platform to date. The initiative is a natural progression of Tanium’s XEM platform and will leverage our unique real-time endpoint data and Tanium AI to make highly tailored recommendations and automate actions. Tanium AI will be informed by many sources, including machine learning algorithms, peer success rates, and risk thresholds, to empower organizations to optimize and better secure their environments in ways previously not possible with conventional endpoint management, risk and compliance, digital employee experience, and incident response solutions.

We are busy at work integrating AI into our XEM platform to deliver AEM capabilities, which will enable organizations to address previously unsolvable challenges in managing and securing their growing and complex IT estates. Powered by Tanium AI, our platform will process millions of actions, billions of real-time data points, and a trillion signals across 33+ million endpoints and will learn from the worldwide experiences of the Tanium community to help organizations improve decision-making abilities to more easily combat the ever-increasing reality that is global cybercrime by continuing to provide The Power of Certainty™.

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As AI cybersecurity evolves, this guide will act as a living resource to provide updates as new data emerges to continue to help companies make more informed decisions around best practices and use cases.

Interested in learning more about active incidents and recent attacks from professional cyber analysts? Read our blog series by the Tanium Cyber Threat Intelligence (CTI) team as they review what’s in the news to deliver what you need to know about changes in the threat landscape that could potentially impact businesses and cybersecurity.