AI (Artificial Intelligence) has revolutionized the way IT security professionals think about cybersecurity. Newer AI-powered cybersecurity tools and systems have the ability to support providing even better data protection against threats by quickly recognizing behavior patterns, automating processes, and detecting anomalies.
How Does AI Apply to Cybersecurity?
AI can monitor, analyze detect, and respond to cyber threats in real time. As AI algorithms analyze massive amounts of data to detect patterns that are indicative of a cyber threat, it can also scan the entire network for weaknesses to prevent common kinds of cyber attacks.
AI primarily monitors and analyzes behavior patterns. Using these patterns to create a baseline, AI can detect unusual behaviors and restrict unauthorized access to systems. AI can also help to prioritize risk, instantly detect the possibility of malware and intrusions before they begin.
When implemented properly, AI can serve as the engine for security automation, which frees up the time and resources of employees by automating repetitive tasks. AI can also reduce the occurrence of human error by removing humans from a task or process.
How Is AI Cybersecurity Different?
Cybersecurity protection with artificial intelligence will never fully replace security professionals, as there will always be a need for creative problem-solving and more complex challenges in the workplace. However, AI can and already does assist human security professionals by analyzing vast amounts of data, recognizing patterns, and creating insights based on large volumes of security data. This could take hours, sometimes weeks to complete with traditional security processes.
Before AI, security professionals used signature-based detection tools and systems to identify potential cyber threats. These security tools compare incoming network traffic to a database of known threats or malicious code signatures. Upon detection, the system triggers an alert and suggests to the security professional that they should take an action to block or quarantine the threat.
This signature-based security approach has been reasonably effective against known threats. However, the signature-based detection approach has proven to be inadequate against new (Zero-Day) or unknown threats. Too often, these tools also resulted in a higher frequency of false positives, which sent security professionals on a "wild goose chase."
Traditional cybersecurity also relies heavily on manual analysis. Security analysts must manually investigate security alerts and event logs in search of any identifiable patterns that serve as indicators of a potential security breach. Investigating logs and events can take extensive amounts of time and relying solely on a single security analyst is a mistake companies cannot afford to make.
AI has the power to address these shortcomings of traditional cybersecurity and much more. As this technology continues to mature, it will have a massive impact on cybersecurity processes and people.
Why Is AI in Cybersecurity Important?
Cyber criminal organizations have already invested in machine learning, automation, and AI to launch large-scale, targeted cyberattacks against organizations. The number of threats and potential for ransomware impacting networks continues to grow.
AI and machine learning is helping security analysts level the playing field by processing massive amounts of data, providing rapid insights based on analysis, and cutting through the noise of daily security alerts and false positives. This drastically improved your team's efficiency and productivity, giving them an advantage over potential cyber criminals.
With the rise of more sophisticated attack vectors such as polymorphic malware, scripting, and so called "living-off-the-land" attacks, it has become easier for cybercriminals to bypass traditional, file-scanning-based anti-virus defenses. To protect against this evolution of malware, more modern approaches such as behavior analysis are becoming more popular in cybersecurity. Behavior analysis and detection approaches are powerful, as all malware eventually needs to exhibit malicious behavior in order to succeed. AI, when properly trained, has the capability to monitor, detect, and respond to these malicious behaviors faster than humans alone.
What Are the Benefits of AI in Cybersecurity?
Today's AI systems are trained to detect potential cyber threats, identify new attack vectors, and safeguard your company's sensitive data. The three top benefits to using AI driven cybersecurity tools include:
- Quickly analyzing large amounts of data
- Detecting anomalies and vulnerabilities
- Automating repetitive processes
The potential of leveraging AI in cybersecurity is virtually endless. The speed and accuracy of threat detection and response is as close to real-time as possible. AI can help minimize the impact of a ransomware attack by flagging suspicious behavior to your security team as soon as possible. And finally, AI makes cybersecurity operations more efficient through automation, freeing up your security team's valuable time and resources to work on other, more important tasks.
What is Machine Learning (ML)?
Machine learning primarily focuses on the capability of a machine to imitate intelligent human behavior. The engine for machine learning is data. ML uses mathematical models of data to help a machine learn without direct instruction or programming by a human. This means that a machine learning-enabled system continues learning and improving its performance based on its experience, without human intervention.
Machine learning is a type of AI, but ML and AI are not interchangeable. ML is AI with the ability to learn and automatically adapt with minimal human intervention or programming.
What Are Deep Neural Networks?
Deep learning is a more sophisticated type of ML that uses neural networks to imitate the learning process of the human brain. A neural network leverages machine learning and AI to teach machines how to process data in a way that is inspired by the human brain. Like the human brain, a neural network consists of functional layers. Within these layers, certain behaviors, tasks, or processes trigger a specific response from the machine. The more layers within the neural network, the more expressive and sophisticated the response.
Neural networks with multiple hidden layers are known as deep neural networks. Neural network algorithms are designed to follow a preset list of rules by predicting solutions and drawing conclusions based on previous iterations and experiences. A deep neural network creates an adaptive system in which machines learn from their mistakes and improve continuously. Deep neural networks have the ability to solve more complex problems that traditional machine learning can't, such as summarizing documents or recognizing faces with greater accuracy.
What Are the Risks of AI in Cybersecurity?
It's important to remember that AI as a technology is still in its early days. AI still requires human intervention, not only to train AI engines but to step in if an engine makes a mistake. AI-powered security systems rely on machine learning algorithms that learn from historical data. This can lead to false positives when the system encounters new, unknown threats that do not fit into existing patterns. Another growing concern is how hackers can leverage AI for malicious purposes, including generating convincing phishing emails and even building out malware.
What Kind of Skills Are Required to Implement AI in Cybersecurity?
AI and cybersecurity are more connected than ever. Individuals with skills and abilities in both are in high demand today. Enterprises and technology companies are searching for people who can understand both cybersecurity and AI enough to understand when and how to apply AI techniques to cybersecurity workflows. Data scientists, analysts, and engineers with a background in cybersecurity are essential. These roles require education and experience in machine learning data modeling, deep neural networks, language modeling, and behavior analysis. Additionally, they must have a good understanding of cybersecurity principles. An AI cybersecurity professional must have strong knowledge in the areas of network security, computer forensics and cryptography, malware detection and defense, and data protection.
How Does AI Improve Managed Detection and Response (MDR)?
The need for always-on security operations has become imperative. However, the complexity of modern operating environments and the speed at which cyber threats enter an environment make it almost impossible for most organizations to successfully manage detection and response on their own. That's where Managed Detection and Response comes in.
AI and ML are already transforming the way security operations centers (SOCs) deliver managed detection and response (MDR) and other managed security services. By leveraging these technologies, SOCs are strengthening their MDR capabilities, operating with greater efficiency, and achieving stronger resilience in the face of ever-evolving cyber threats. AI can help improve the speed and accuracy of MDR by taking on more of the heavy lifting in 24/7 threat detection and analysis.
Here are four key areas where AI is already having a positive impact on MDR:
1. Threat hunting and threat intelligence
Deep neural networks can be used to train machines to detect and identify threats such as malware. AI can collect, process, and enrich threat data from multiple sources across an organization. It can also correlate and contextualize that data to create threat profiles, measure against indicators, and even discover emerging threats. AI also enables proactive threat hunting, where security professionals leverage advanced analytics and automation to search for hidden or unknown threats in an environment.
2. SOC operations
MDR providers see great potential in leveraging AI to optimize and improve their SOC's overall performance and operational efficiency. For example, managed security service providers can monitor and measure against their SOC's key performance indicators (KPIs), including security alert volume, response times, resolution rates, and customer satisfaction levels. AI can help identify and address security gaps, operational bottlenecks, or inefficiencies in a managed SOC's processes, workflows, and tools.
3. Cybersecurity training and development
AI can help assess and improve SOC analysts' relevant skills, knowledge, and competencies. Because AI has the power to learn and continuously improve, MDR vendors can create highly personalized learning paths for personnel. Additionally, organizations can create and deliver realistic and engaging security training scenarios, simulations, and exercises.
4. Security innovation
AI's core mission of continuous improvement makes it well-suited to help with innovation. Today's SOC must be able to quickly adapt and evolve its capabilities in response to changing customer needs and the never-ending threat landscape. By using AI and ML, MDR providers can keep their SOCs ahead of the curve, reducing risk.
In the Security Operations Center (SOC) of the near future, AI, when trained on large-scale user behavioral data, will integrate into SOC workflows to elevate security and operator efficiency. AI will be an invaluable asset to security operations professionals and assist them in identifying threats in real-time.
The Bottom Line on AI in Cybersecurity
Sophos Artificial Intelligence was formed in 2017 to produce breakthrough technologies in data science and machine learning specifically for cybersecurity. The Sophos X-Ops team of highly experienced data scientists, engineers, and security experts is focused on machine learning, large-scale scientific computing architecture, human-AI interaction, and information visualization. AI is pushing the boundaries of machine learning to uncover threats and protect your systems, data, and applications. Learn more about how you can utilize AI to better secure your organization from the next cyberattack.