What Is Artificial Intelligence in Cybersecurity?
Artificial intelligence has already made an indelible impact on cybersecurity — on both sides of the equation. Modern security tools commonly incorporate AI and machine learning features to automate threat detection and response. These tools now play an essential role in protecting the enterprise against the onslaught of security threats.
At the same time, cybercriminals are using AI to improve their password-cracking techniques, write malware and conduct social engineering attacks. They also use AI to find vulnerabilities and even manipulate machine learning algorithms. Organizations must “fight fire with fire” by using AI tools to counter these attacks.
Table of Contents
- Why Use Artificial Intelligence in Cybersecurity
- How Is Artificial Intelligence Used in Cybersecurity
- Data Analytics Is Not Artificial Intelligence
- Drawbacks and Risks of AI in Cybersecurity
- How Cybercriminals Use AI
- Conclusion
Why Use AI in Cybersecurity?
The primary benefit of AI in cybersecurity is speed. The attack surface has become too large and complex for human security analysts to defend. In a large enterprise, hundreds of billions of variables must be analyzed in real time. The scope of the problem exceeds human capabilities.
Machine learning and other AI technologies can respond rapidly to thousands or even millions of daily alerts, and correlate them to threat intelligence. Many alerts are duplicative or false positives, creating “alert fatigue” that causes human analysts to overlook or ignore potential threats. AI can cut through this “noise” and automatically detect and block most threats, drastically reducing response time. Combining AI with automation can cut the time to detect incidents by one-third or more.
If a threat requires human analysis, the AI-enabled system will present that information to the security team along with any relevant context. AI-enabled systems draw on huge volumes of information, virtually eliminating the risk of error. They can also learn from the threats they encounter, enabling them to draw on past experience to identify new types of attacks. Additionally, systems with natural language processing capabilities can glean information from news items, threat alerts, research papers and other sources to gain new insights into the security climate.
How Is AI Used in Cybersecurity?
AI technologies have several applications in cybersecurity, and use cases continue to grow. More vendors are adding AI capabilities to their security products, including technologies developed by some of the leaders in AI development. Top cloud providers also use machine learning to secure their platforms and services.
Verified Market Research expects the global market for AI in cybersecurity to see a compound annual growth rate of 30.1 percent through 2030. According to Kroll’s 2023 Fraud and Financial Crime Report, 56 percent of business leaders say they use AI as part of their cybersecurity strategy.
Here are six of the top applications of AI in cybersecurity.
Malware detection.
Traditional signature-based antivirus tools detect just 30 percent to 60 percent of threats. AI-enabled tools can identify up to 92 percent, including malware that continually transforms itself to evade detection.
Phishing attack detection.
Malware and other threats are often delivered through phishing attacks, which are increasingly difficult to detect. Using natural language processing, AI-enabled tools can analyze the text and structure of emails to determine if they are legitimate. They can also detect and block malicious attachments and trackers.
Behavioral analysis.
Malicious attacks behave differently than legitimate system and network activity. Machine learning can analyze and learn normal activity to identify attacks. AI-enabled tools can also distinguish malicious bots (such as those that steal credentials and data) from benign ones (search engine crawlers)
Threat prioritization
In addition to identifying threats, AI-enabled tools can prioritize them. This enables IT teams to focus resources on the threats that have the greatest potential to cause damage to the organization.
New threat detection.
The threat landscape is constantly changing — faster than traditional security tools can be updated. Machine learning tools collect global and industry-specific threat intelligence data. They can use this data to detect and defend against emerging threats.
Risk prediction.
Every asset attached to the network is a potential attack vector. AI tools can assess the entire IT environment, assemble a complete asset inventory, and identify weaknesses in defenses. These tools can even predict the most likely type of attack. This advanced warning enables the organization to take steps to prevent a breach.
Data Analytics Is Not Artificial Intelligence
Artificial intelligence is a hot topic right now. Some companies claim their products are AI-enabled when they don’t meet the criteria for AI. An AI system analyzes data and automates processes in a way that simulates human cognitive abilities. Machine learning systems go further, becoming smarter the more data they analyze. They are able to learn from past experience and predict future events.
Data analytics systems are programmed to look for patterns in large datasets. They draw conclusions about the data based on the specific steps of the algorithm. They cannot learn anything beyond what they are programmed to do.
While AI is more advanced than data analytics, it will never replace it. The two are complementary. Data analytics tools make AI more efficient and can help coordinate the efforts of multiple AI systems.
What Is Artificial Intelligence in Cybersecurity?
Drawbacks and Risks of AI in Cybersecurity
Despite its benefits, AI-enabled cybersecurity tools have drawbacks. One of the primary disadvantages is the amount of data required for training. Organizations that train machine learning systems must collect large volumes of malicious and non-malicious code and behaviors — a time-consuming and intensive process. But without large volumes of data, the AI system will not provide accurate results. The model may have to be modified if the system provides an incorrect answer.
The biggest risk for organizations that deploy AI-enabled security tools stems from a lack of familiarity with AI technologies. This is manifested in a couple of ways. In some organizations, security teams may distrust the AI tools and continue to perform many tasks manually. In others, security personnel may put too much trust in AI and fail to question results. Both problems can be resolved by monitoring, benchmarking and assessing AI systems. It’s important that humans not become complacent.
How Cybercriminals Use AI
The same capabilities that make AI effective for securing the IT environment enable cybercriminals to defeat those defenses. It’s important to understand these threats in order to develop an effective security strategy.
- ·Machine learning tools can crack passwords faster than traditional techniques. A tool called PassGAN can crack 51 percent of common passwords in less than a minute.
- Chatbots such as ChatGPT can be used to generate phishing emails that are virtually impossible for humans to detect. Additionally, chatbots can be used to generate an immediate response should the victim reply, avoiding one of the telltale signs of phishing.
- With AI tools, cybercriminals can develop malware that can analyze machine code to identify vulnerabilities in the IT environment. Chatbots can also be used to locate valuable data such as user credentials and personally identifiable information.
- Cybercriminals are using AI to develop polymorphic malware that can continually change its structure to evade detection.
- By manipulating data used to train machine learning tools, hackers can cause the tools to deliver inaccurate results. Hackers may also seek to influence these tools to ignore certain types of attacks
Conclusion
Humans are unable to defend a sprawling attack surface against constant security threats. Organizations need AI-enabled tools to detect and block attacks and provide insight into vulnerabilities and risks. With AI technologies becoming more accessible, they are being incorporated into a wide range of security tools to perform key tasks. AI has its drawbacks. In addition, cybercriminals can “poison” AI datasets to skew results in their favor. But despite these tradeoffs, AI has a key role in modern cybersecurity strategies.