Cybersecurity Artificial intelligence (AI) is the use of machine learning (ML), deep learning (DL), and other AI technologies to improve cybersecurity’s detection, prevention, and response to cyber threats. In contrast, AI systems can analyze huge amounts of data in real time and make sense of the data, spotting patterns, detecting anomalies, and automating decision-making to prevent, mitigate, or respond to security incidents.
Most traditional cybersecurity tools depend on ‘rules’ and known threats to detect, while AI systems, learning from past data, constantly fine tune as cybercriminals refine their techniques. The ability of AI to analyze, process, and predict future attacks in large datasets and to spot patterns makes it a powerful tool in fighting cybercrime.
AI for cybersecurity innovations
1. Automated Threat Detection and Response
Perhaps most importantly, AI has begun to automate the threat detection and response. In traditional systems, security teams have to search for suspicious activities in the logs, network traffic, and other data manually. It is a time-consuming process and often leads to responses being very late.
Deep learning-enabled systems can be capable of continuously monitoring networks, endpoints, and cloud environments for signs of threats in an automated process. Malicious activity like phishing attempts, malware infections, or normal network traffic are patterns of behaviors used by machine learning algorithms to identify. AI can automatically respond to a threat by isolating compromised systems, blocking malicious IP addresses, or notifying security personnel to investigate further.
Let’s take security information and event management (SIEM), for example. Security teams can identify potential threats more quickly and accurately when they analyze a vast amount of security event data using AI. It enables organizations to confront cyberattacks early, before they grow into something that would damage systems and data.
2. Threat intelligence and predictive analytics
Predictive capabilities also increase in AI, which allows organizations to anticipate the latest developments in cybercriminal attacks. By knowing the history of these attacks and the current threat pattern, AI is capable of predicting the probability of future attacks and vulnerabilities.
Treating this like a modern post-cybersecurity arms race in and of itself, threat intelligence platforms that leverage AI can monitor several sources (the dark web, for example) to watch for the likes of zero-day exploits or new malware variants. Security teams can use these platforms to analyze the cybercriminals’ tactics, techniques, and procedures (TTPs) used to launch attacks before they happen.
Furthermore, AI can aid in predicting possible vulnerabilities in software and hardware systems before they are exploited. AI-powered systems can continuously scan for weaknesses and analyze the latest vulnerabilities so that proactively you can get recommendations for patching and securing critical infrastructure.
3. Anomaly Detection and Behavioral Analytics
For insider threats and advanced persistent threats (APTs) that conventional detection can’t stop, AI-based behavioral analytics is a powerful tool to take. By studying patterns in user activity, file access, and system interaction, aimed AI systems can begin to learn what “normal” behavior looks like on a network.
After the system learns this normal behavior, it can quickly recognize deviations from it—i.e., unauthorized access attempts, device logs coming at unusual times, or pushing files outside of normal behavior. By contrast, anomaly detection powered by AI helps organizations identify threats well before they reach maturity and compromise the data or the system.
For instance, AI-enabled user and entity behavior analytics (UEBA) systems use AI to identify suspected atypical user behavior, analyzing user actions.
4. Malware and Ransomware Detection (updated on July 11, 2018)
The most devastating cybersecurity threats continue to be malware and ransomware attacks. Signature-based detection has been standard in traditional security solutions but only detects known threats. But AI-powered systems that use machine learning and deep learning use the malware behavior to detect it before putting its name into the signature database.
AI analyzes how a file acts when executed and blocks them. Detecting new and evolving threats in real time greatly enhances a defense against attacks that use ransomware to create major disruptions to businesses and individuals.
Challenges with AI in Cybersecurity
Below are some of the key obstacles organizations face when adopting AI for cybersecurity:
1. AI-Powered Cyberattacks
While AI can be used to thwart cyberattacks, it can also be turned to prosecute one. AI can be used by cybercriminals to create increasingly sophisticated attacks, such as automated phishing campaigns, evasive malware, and very intricate and personal social engineering.
Malware powered by artificial intelligence can continue to evolve to work its way out of being detected by traditional security systems. Attackers use AI to generate fake data or impersonate legitimate users, bypassing anomaly detection systems based on artificial intelligence.
AI is an arms race between AI using professionals utilized as defenders in cybersecurity in the arms race and AI using individuals as attackers in cybersecurity in the arms race. But the misuse of AI by cybercriminals presents a threat to organizations that must be kept at bay with vigilance and defense mechanisms that are always evolving in their attempts to stay ahead of new threats.
2. Data Privacy Concerns and Ethics
Many of the best AI-driven cybersecurity tools are only effective when they have access to large amounts of data. That includes user behavior data, network traffic, and system interaction data.
Consider, for instance, an AI system that’s analyzing employee behavior and may wind up accidentally violating privacy rights or providing false positives (where legitimate activity is flagged as suspicious). Since AI tools are used by so many organizations, it’s important they adhere to data privacy regulations like the General Data Protection Regulation (GDPR) and be transparent with personal data.
3. The complexity of AI models
Deep learning algorithms, which are the most prevalent and influential of AI models, tend to be incredibly complex and extremely hard to explain. This is widely called the “black box problem,” the place where it’s difficult to understand how an AI system approaches a selected decision. Given a security team’s central role in mitigating the risk of cyber attacks and ensuring business operations, the lack of transparency about what AI decision has been made can make it difficult to trust and validate AI-based decisions around things that must be done with critical results like blocking traffic or isolating systems.
However, AI-driven automation creates the benefit of improved quality, less risk, and higher volume, yet organizations need to balance the benefits with the necessity for human oversight and intervention. AI-powered systems should be regularly audited by the security team and should have very clear procedures for how AI-generated alerts and recommendations are handled.
4. Skill Gaps and shortage of talent
To enable AI in cybersecurity requires a very specialized skill set built on top of both cybersecurity and machine learning skills. Unfortunately, however, there is a lack of people with this dual expertise. There isn’t an unlimited amount of qualified personnel available to organizations to implement and manage AI-powered cybersecurity solutions, resulting in only partial usage of AI.
However, to overcome this problem, businesses would need to invest in training and frequent development programs for certain security personnel already in the team or a way to get allied with some specialized AI vendors.
Leveraging AI for Effective Cybersecurity: How?
AI can nevertheless offer great advantages in enhancing cybersecurity resilience. Here are some best practices to help organizations make the most of AI in their cybersecurity strategy:
Combine AI with Human Expertise: However, AI can replace some of the cybersecurity tasks, but its presence doesn’t eliminate the need for humans’ oversight. AI should not replace, but rather be a tool security teams can use to solidify their capabilities.
Regularly Update AI Models: AI models, when married to the latest threat intelligence, allow their users to continuously evolve with cyber threats. AI-driven tools must receive regular training and updates to remain accurate.
Invest in Data Privacy and Ethics: Make sure AI tools comply with principles of data protection regulation and ethics. To protect user privacy without overreach in monitoring of employee behavior.
Educate and Train Staff: Invest in education and training programs that complement cybersecurity skills with the knowledge of AI and machine learning.
Conclusion: AI in Cybersecurity: The Future
It’s no secret that AI has turned into a revolutionary power in cybersecurity, providing intelligent solutions to detect, defend against, or recover from cyber attacks. It has the ability to analyze massive data sets and to predict attacks while automating responses, revolutionizing the approach to security that organizations take. While these are new challenges, as with any technology, AI also introduces new opportunities such as adversarial use and ethics concerns, as well as the need for expert professionals.
AI is only going to get larger in the cybersecurity sphere as it grows. If businesses embrace the potential of AI and address the challenges, then they can improve their reaction to threats and make better use of the flexible, new threat landscape of the AI era.
