How to Use AI in Cybersecurity for Business

With rapid advancements in technology, security leaders are actively exploring how to use artificial intelligence (AI) in cybersecurity as traditional measures alone may no longer be sufficient in defending against sophisticated threats. AI has emerged as a potentially powerful tool in bolstering cybersecurity efforts, offering enhanced threat detection, prediction, and response capabilities among other uses.

A survey by The Economist Intelligence Unit revealed that 48.9% of global executives and leading security experts believe that AI and machine learning (ML) are best equipped for countering modern cyberthreats. Additionally, IBM found that AI and automation in security practices can significantly reduce threat detection and response times by up to 14 weeks of labor and reduce costs associated with data breaches. In fact, global interest in AI’s potential in countering cyberthreats is evident by the growing investments in it. The global AI in cybersecurity market is projected to reach USD 96.81 billion by 2032.

Despite the promise of AI, Baker McKenzie found in a survey that C-level leaders tend to overestimate their organization’s preparedness in relation to AI in cybersecurity. This serves to underscore the importance of realistic assessments on AI-related cybersecurity strategies.

Security Applications of AI

Many tools in the market leverage subsets of AI such as machine learning, deep learning, and natural language processing (NLP) enhance the security ecosystem. CISOs are challenged with finding the best ways to incorporate cybersecurity and artificial intelligence into their strategies.

1. Enhanced Threat Detection and Response

One of the main examples of AI in cybersecurity is its use for malware detection and preventing phishing, AI-powered tools are shown to be significantly more efficient compared to traditional signature-based systems.

Where traditional systems can prevent about 30% to 60% of malware, AI-assisted systems have an efficiency rate of 80% to 92%.

Researchers at Plymouth University detected malware with an accuracy of 74% on all file formats using neural networks. The accuracy was between 91% to 94% for .doc and .pdf files specifically. As for phishing, researchers at the University of North Dakota proposed a detection technique utilizing machine learning, which achieved an accuracy of 94%.

Given that phishing and malware remain the biggest cybersecurity threats for organizations, this is good news. These advancements enable organizations to identify potential threats more accurately and respond proactively to mitigate risks that could cause massive financial and reputational damage.

2. Knowledge Consolidation

A pressing issue for CISOs is the sheer volume of security protocols and software vulnerabilities poses a challenge for their security teams. An advantage of AI in cybersecurity is that ML-enabled security systems can consolidate vast amounts of historical data and knowledge to detect and respond to security breaches. Platforms like IBM Watson leverage ML models trained on millions of data points to enhance threat detection and minimize the risk of human error.

AI’s ability to improve its knowledge of cybersecurity threats and risks by consuming billions of data points and recognize patterns and anomalies faster than humans enables it to learn from past experiences and come up with increasingly efficient ways to deal with combat cyberattacks. This allows AI-powered security systems to keep pace with the evolving threat landscape more efficiently.

IBM notes that AI is also able to analyze relationships between threats in mere seconds or minutes, thus reducing the amount of time it takes to find threats. This is essential to reducing the detection and response times of cybersecurity breaches, which can significantly reduce costs to organizations as well.

The global average total cost of data breach according to IBM is $4.35 million USD in 2022. Organizations also took an average of 277 days to identify and contain a breach. However, if that number is brought down to 200 days or less with the help of AI, organizations can save an average of $1.12 million USD.

3. Enhanced Threat Analysis and Prioritization

Tech giants like Google, IBM, and Microsoft are investing heavily in AI systems to identify and analyze and prioritize threats. In fact, Microsoft’s Cyber Signal’s program leverages AI to analyze 24 trillion security signals, 40 nation-state groups, and 140 hacker groups to detect software vulnerabilities and malicious activities.

Given the vast amounts of data that must be analyzed, it’s not surprising that 51% of IT security and SOC decision-makers said they were overwhelmed by the volume of alerts (Trend Micro) while 55% cited their lack of confidence in prioritizing and responding to them. Moreover, 27% of surveyed respondents spent up to 27% of their time managing false positives.

Worryingly, Critical Start found that nearly half of SOC professionals turn off high-volume alerts when there are too many to process.

One answer to the question of how to use AI in cybersecurity is by applying it to analyze vast amounts of security signals and data points to detect and prioritize threats quickly and effectively. With the assistance of AI, security teams are better able to promptly respond to threats under the increasing frequency of cyberattacks.

4. Threat Mitigation

The complexity of analyzing every component of an organization’s IT inventory is well-understood. With the help of AI tools, the complexity can be managed. AI can identify points within a network that may be more susceptible to breaches and even predict the type of attacks that may occur.

In fact, some researchers have proposed cognitive learning-based AI models that can monitor security access points for authorized logins. This model can detect remote hacks early, alert the relevant users, and create additional security layers to prevent a breach.

Of course, this would also require training AI/ML algorithms to recognize attacks carried out by other such algorithms as cybersecurity and risks evolve in lockstep. For example, hackers have been found to use ML to analyze enterprise networks for weak points. This information is used to target possible entry points for phishing, spyware, and DDoS attacks.

5. Task Automation

When talking of AI applications in cybersecurity, task automation is one of the most widely adopted. Especially for repetitive tasks, such as analyzing a high-volume of low-risk alerts and taking immediate measures, AI tools can come in handy to free up human analysts for higher-value tasks. This is especially valuable to companies that are still short on qualified cybersecurity talent.

Beyond that, intelligent automation is also useful for gathering research on security incidents, assessing data from multiple systems, and consolidating it into a report for analysts. Shifting this routine task to an AI helper will save plenty of time.

How Threat Actors Are Using AI

While AI is proving to be a valuable tool in the cybersecurity arsenal, it is also becoming a mainstay for threat actors who are leveraging it for their malicious activities. AI’s high processing capabilities enable them to hack systems faster and more effectively than humans.

In fact, generative AI models such as ChatGPT and Dall-E have made it easier for cybercriminals to develop malicious exploits and launch sophisticated cyberattacks at scale. Threat actors can use NLP AI models to generate human-like text and speech for social engineering attacks such as phishing. The use of NLP and ML enhances the effectiveness of these phishing attempts, creating more convincing emails and messages that trick people into revealing sensitive information.

AI enables cybercriminals to automate attacks, target a broader range of victims, and create more convincing and sophisticated threats. For now, there is no efficient way to distinguish between AI- or human-generated social engineering attacks.

Apart from social engineering attacked, AI-powered cyberthreats come in various forms including:

• Advanced persistent threats (APT)s that use AI to evade detection and target specific organizations;
• Deepfake attacks which leverage AI-generated synthetic media to impersonate real people and carry out fraud; and
• AI-powered malware which adapts its behavior to avoid detection and adjust to changing environments.

The rapid development of AI technology allows hackers to launch sophisticated and targeted attacks that exploit vulnerabilities in systems and networks. Defending against AI-powered threats requires a comprehensive and proactive approach that combines AI-based defense mechanisms with human expertise and control.

AI and Cybersecurity: The Way Forward

The integration of AI into cybersecurity is transforming the way organizations detect, prevent, and respond to cyber threats. By harnessing the power of AI, organizations can bolster their cybersecurity defenses, reduce human error, and mitigate risks.

Read more: ChatGPT & GPT-4: How to Implement Generative AI in Your Organization.

Having said that, the immense potential of AI also increases the risk of cyber threats which demand vigilant defense mechanisms. After all, humans remain a significant contributing factor to cybersecurity breaches, accounting for over 80% of incidents. This emphasizes the need to also address the human element through effective training and awareness programs.

Ultimately, a holistic approach that combines human expertise with AI technologies is vital in building a resilient defense against the ever-evolving landscape of cyber threats.

FAQ: AI in Cybersecurity

How is AI used in cybersecurity?

In cybersecurity, AI removes the need for human experts to do tedious, time-consuming tasks. AI can read an immense amount of data and identify potential threats while reducing false positives by filtering non-threatening activities. This helps human security experts to focus on vital tasks instead.

How will AI improve cybersecurity?

AI technologies can spot potential weak spots in a network, flag breach risks before they occur, and even automatically trigger measures to prevent and mitigate cyberattacks from ransomware to phishing and malware.

What are the risks of AI in cybersecurity?

AI-enabled cybersecurity tools are reliant on the data sets they are trained on. This means bias may unintentionally skew the model, resulting in mistaken analysis and inefficient decisions that could lead to terrible consequences.

What are pros and cons of AI in cybersecurity?

Some benefits of AI-based security tools include quicker response times, better threat detection, and increased efficiency. On the other hand, there are ethical concerns to AI such as privacy, algorithmic bias, and talent displacement.

Read more: Interview with Dr Rebecca Wynn: “We Didn’t Think of AI Privacy by Design”.