Artificial intelligence (AI) has made remarkable progress in recent years and has proved its value in various fields, including cybersecurity. With the rise of cyber threats and the increasing complexity of cyberattacks, AI has become a central tool for protecting against cybercrime.
Integrated artificial intelligence systems have the potential to be trained for the automatic identification of cyber threats, alerting users, and safeguarding sensitive information of businesses. Therefore, this article explores AI in cybersecurity, its benefits, use cases, and solutions, and addresses some frequently asked questions.
How is AI used in cybersecurity?
Artificial intelligence combines large data sets and uses them with intuitive processing algorithms. As the scope of networks and systems expands, AI in cybersecurity helps to automate operations by processing large amounts of data much faster than a human ever could. For this reason, most cybersecurity tools integrate deep learning and other capabilities intended to work with big data. Here are the main ways in which AI is used in cybersecurity:
Threat detection. AI can act as a filter for analyzing files and software code to identify potential malware threats while avoiding false positives. Machine learning algorithms can be trained for threat detection to recognize patterns and characteristics of known malware and flag any new code that matches these patterns.
Network security. AI algorithms can analyze network traffic data to detect patterns and anomalies indicating an attempted intrusion or attack. AI can flag any deviations from this baseline as potential threats by learning what normal network traffic patterns look like.
Behavioral analysis. AI can be used to analyze user behavior and detect anomalies that may indicate unauthorized access or malicious activity using machine learning. This allows for more effective user activity monitoring and detection of potential threats while limiting false positives.
Automated incident response. AI-based systems can be used to automatically respond to detected threats, like shutting down connections, quarantining infected machines, and disabling user accounts. Advanced machine learning models help to contain hacking attempts and minimize potential damage.
Vulnerability assessment. AI can identify potential vulnerabilities in systems and networks. This allows for proactive measures to be taken to mitigate potential threats before they can be exploited.
AI can be a powerful tool that can contribute in real-time, which can be essential in today's rapidly evolving cyber threat landscape and lowers the odds that an organization will be affected by a data breach.
Benefits of AI in cybersecurity
AI solutions are versatile and can be applied in various scenarios. However, it requires preparation and feeding the deep learning models with plenty of data that could be used as a reference when identifying patterns. AI for cybersecurity does bring benefits, creating a more secure environment. Here are some of them that are noteworthy in a business setting.
1. Better vulnerability management
Considering the scope of threats that organizations face daily, network administrators need all the help they can get for endpoint protection. AI can analyze existing security measures to identify potential gaps, enabling businesses to focus on the most critical areas. This makes troubleshooting more efficient and provides in-depth oversight of the security level faster than any human ever could.
2. Self-correcting models
AI models can use deep and machine learning techniques to analyze network behavior and identify deviations from the norm. This allows further adjustments, enabling them to trigger various response actions when something odd is detected. This system adjusts its model over time, making it more accurate.
3. Limits process duplication
Some cybersecurity tasks are repetitive and monotonous, adding to personnel frustration and increasing the chances that some threats will slip by. AI-driven tools can perform all those recurring tasks automatically and only require confirmation before making the final changes. This allows security against potential gaps by consistently implementing the best network security practices.
4. Secure authentication
The industry is moving away from passwords and looking for ways to make security smarter. AI can be a helpful addition to implementing multiple authentication layers to verify a user's identity. Using tools like fingerprint scanners, facial recognition, and other AI solutions helps identify fraudulent login attempts. This creates a much tighter security mechanism when allowing users in.
5. Helps to cover more ground
AI tools can perform multiple tasks simultaneously. At the same time, AI can scan and identify disguised threats while prioritizing prevention, even when dealing with multiple threats simultaneously. This versatility positively translates in terms of cybersecurity. Human attention can be limited to a single task at once, while AI can cover them in all other areas, which helps to expand network visibility and ensure appropriate security.
6. Helps to balance out workloads
Cybersecurity personnel isn't cheap to hire or maintain, so it's in a business's best interest to ensure their experience is spent on tasks with the highest complexity. While AI can take care of manual tasks, human personnel can think of other ways to improve the cybersecurity posture in the organization. In the long run, this creates a greater value.
The limitations of traditional methods
The main difference between traditional cybersecurity tools and AI is their flexibility. Conventional cybersecurity tools like antiviruses or firewalls function based on strictly predetermined rule sets. A tool comes equipped with a list of malware types or blacklisted websites, which must be manually updated over time — it's a very static system.
Meanwhile, AI can detect and respond to threats in real-time. Its ability to process large amounts of data when making decisions is unparalleled and extremely valuable. Cybersecurity threats are becoming more complex, so cybersecurity tools must react quickly if they want to stop them, which is why static models are too slow in today's cyber landscape.
Hackers are also following developments of AI, which puts a lot of pressure on traditional cybersecurity solutions, as well. That's another reason why AI in cybersecurity can level the playing field and provide a more well-rounded security solution.
AI cybersecurity solutions
The current cybersecurity market is saturated with solutions that integrate AI capabilities. Their advanced models allow them to process large amounts of data in real-time. Here's a broad overview of cybersecurity technologies that integrate AI for cybersecurity.
Endpoint security uses AI integrating network and device security to provide holistic protection against various threats. Tracking and analyzing processes on laptops, desktops, and mobile devices before the execution of malicious code allows the solution to shut down threats before they cause damage. Additionally, the models are expanded with additional input from past threats as they're actively updated as they're used.
Intrusion detection systems (IDS)
AI-powered IDS systems are capable of autonomously identifying threats using machine learning models. With enough data to work with and thorough training (and enough computational power), the model can be very accurate when discerning potential threats. This can help identify signs of intrusion moments from when it started. When combined with the remaining cybersecurity suite, the solutions can also help automate certain tasks, i.e., alert security teams or shut down network parts.
Data Loss Prevention (DLP)
DLP tools automatically encrypt data before it's transmitted or restrict unauthorized users from accessing sensitive information. It's no wonder that modern DLP tools are using AI and machine learning to improve their functionality and performance. AI can monitor and analyze organizational data flows to prevent unauthorized or accidental data leaks. Identifying sensitive information, enforcing data handling policies, and detecting potential data exfiltration attempts in a blink of an eye.
Security Information and Event Management (SIEM)
AI-powered SIEM tools use machine learning, user behavior analytics, and cybersecurity threat feeds to detect abnormal activities. This contribution to threat hunting can help automate many time-consuming manual tasks that network administrators must perform by using AI. This allows for balancing automation with cost-effectiveness and efficiency, improving the organization's overall security posture. Automatic events correlation, suspicious activity detection, and real-time insights into potential threats enable faster incident response and threat hunting.
What is the future of AI in cyber security?
Recent developments have shown that AI will continue to be closely integrated into cybersecurity solutions as attacks become more sophisticated. Many experts believe that using AI will be one of the main directions in which cybersecurity solutions will evolve. This will allow them to identify threats and potential vulnerabilities before they cause damage.
What are AI-enhanced cyber threats?
AI is used not only by cybersecurity specialists but by hackers, as well. This allows them to evade detection and cause more damage. The whole process can be automated — hackers are already writing convincing phishing attack emails using AI and natural language processing. Malware development can also be enhanced using AI, allowing hackers to write sophisticated malware that effectively bypass security measures. Various freely available chatbots are already contributing to the already saturated malware development.