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 ten 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 advancements 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
• Can AI really predict future cyberattacks?
• Security risks and limitations of artificial intelligence in cybersecurity
• U.S. Government warns about dangers of AI-based cyberattacks
• Is AI going to replace cybersecurity?
• The transformative power of AI cybersecurity
• The future of Converged Endpoint Management is autonomous
• Additional resources

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 and applications of AI-powered systems 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.

Support proactive threat hunting

AI can make threat hunting in cybersecurity more efficient and effective. Since traditional methods often rely on manual analysis and fixed rules, they can be slow and miss sophisticated threats. However, AI can quickly analyze large amounts of data from sources like network traffic and user behavior to spot anomalies and potential threats and detect known and unknown threats by learning from historical data and identifying patterns that indicate malicious activities.

Speed incident responses to security threats

AI can help minimize the effects of cyberattacks by deploying automated responses to attacks and prioritizing 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 recovering and restoring normal system functions.

Accelerate incident investigation

When an incident occurs, an analyst must acquire, review, and analyze a lot of data to learn the breadth and depth of an attack. This can be time-consuming and tedious, especially when dealing with large and complex incidents. AI can help shorten that process by automating data collection, correlation, and analysis from various sources, such as logs, network traffic, endpoints, and threat intelligence. AI can help investigators comprehensively understand 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 anticipate future incidents better .

[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.

Can AI really predict future cyberattacks?

Yes, AI can help predict future cyberattacks, but probably not in the way you think. AI cybersecurity tools are not fortune tellers who provide grave premonitions of future attacks.

The “prediction” in AI cybersecurity involves all the work that goes into detecting potential threats before an attack can occur. It’s helpful to think of these predictions more like a severe weather watch vs. warning: AI security solutions can “watch” to see if all the components that could lead to a future attack are present through continuous monitoring and analysis, allowing teams to act proactively to identify, contain, and remedy vulnerabilities that could be exploited, before a “warning,” or active attack, ever has the chance to happen.

Through predictive modeling, which is the process of using machine learning algorithms and statistical techniques to learn from historical data and improve its ability to detect threats, AI-powered solutions can analyze vast amounts of data from various sources, such as network traffic, system logs, threat intelligence feeds, and user behavior, to identify patterns and anomalies that may indicate potential threats, even if they’ve never been seen before.

How? Since AI cybersecurity tools are designed to look for malicious behavior, they excel at identifying zero-day threats compared to traditional security tools built to identify only known threats.

Traditional methods of defense, which include antivirus software, patching, firewalls, and other cybersecurity controls are less effective against zero-days, which are unknown to vendors and organizations. Zero-days bypass the traditional signature and anomaly-based detections and antivirus software, which contain signatures information for known attacks.

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 leverage AI technologies to launch more sophisticated attacks and what this means for AI and cybersecurity.

Security risks and limitations of artificial intelligence in cybersecurity

While organizations and users are incorporating AI and related technology into many different business processes, threat actors also use 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.

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 leverage AI is to perform adversarial attacks 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, encompassing 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 use 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, 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 also leverage generative AI to develop unique content 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 compromise other attack vectors.

[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 March 2023:

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 May 2021, 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.

[Read also: Can we really ‘ignore’ GenAI’s vulnerable code? This security professional says yes (sort of)]

Is AI going to replace cybersecurity?

While AI is a powerful tool transforming the field significantly, it will not replace all cybersecurity efforts or the need for skilled cybersecurity professionals.

Even with a number of promising results in its application to enhance cybersecurity efforts, such as automating responses to threats, accelerating incident investigations, and improving threat detection, AI is not yet considered a standalone solution.

Cybersecurity professionals are still needed to interpret AI findings, make strategic decisions, and handle complex situations that AI might not fully understand. Additionally, as cybercriminals continue leveraging AI to launch more sophisticated attacks, human oversight becomes even more critical.

AI works best when combined with human expertise that works alongside them to create more robust and effective security measures.

The transformative power of AI cybersecurity

The use of AI and automation in cybersecurity presents both challenges and opportunities. While cybercriminals continue to leverage 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 adaptive security 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 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 to make highly tailored recommendations and automate actions.

See how Tanium’s real-time data powers Microsoft Copilot for Security

Tanium AI features 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 more easily manage and secure their growing and complex IT estates. 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™.

¹https://digitalcommons.odu.edu/cgi/viewcontent.cgi?article=1064&context=covacci-undergraduateresearch

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Additional resources

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.

Check out these helpful AI resources also from Tanium:

• AI vs. Humans: Why SecOps May (Not) Be the Next Battleground
• 12 AI Terms You (and Your Flirty Chatbot) Should Know by Now
• Top 10 risks & remedies for adopting generative AI
• Building Diversity in AI: 3 New Hires Discuss Supply and Demand in the Tech Revolution
• Ep. 17: Copilot AI and the Steps to Transform Your Enterprise
• 3 Considerations to Make Sure Your AI is Actually Intelligent
• 4 Critical Leadership Priorities for CISOs in the AI Era

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.