AI and Cybersecurity IS NO LONGER NICE TO HAVE – IT IS CRUCIAL FOR DEFENDERS AND ATTACKERS
Companies require cybersecurity plans and procedures to secure their networks, computers, and data from malicious cyber incidents, such as breaches and unauthorized access. On the other hand, artificial intelligence entails designing and building autonomous, smart systems that require minimal or zero human interference to operate. That said, modern cyber-attacks have necessitated the combined use of cybersecurity solutions with AI capabilities to secure against new threats and enhance threat detection capabilities. In addition, investing in AI-enabled security platforms has become integral to strengthening data security and reducing the financial or operational repercussions resulting from common cybersecurity threats.
Integrating artificial intelligence in cybersecurity has several advantages. Firstly, AI enables efficient analysis of user behaviors, pattern recognition, and identification of different network security flaws and irregularities. In addition, AI enhances security tools, enabling more responsiveness, resilience, and greater robustness. Besides, AI-enabled security systems use deep learning to escalate complex cyber incidents, enable real-time response and prevention. However, cybercriminals also power sophisticated malware with AI to execute more powerful and devastating attacks.
The Growing Importance of AI in Cybersecurity
75% of IT executives in a survey said they rely on AI-enabled cybersecurity tools to identify and prevent potential threats in their networks. Other than network security, 68% and 71% of the executives reported that AI is essential in endpoint security and data security, respectively. More organizations have adopted AI in cybersecurity to protect sensitive information from rising cases of cyber-attacks and data breaches. For example, a recent study found that an attack occurs every 39 seconds, indicating that companies are required to respond in real-time to thwart attempted attacks.
Since current cybersecurity technologies cannot guarantee optimized protection, integrating AI in cybersecurity can complement human teams by simplifying the threat detection, analysis, and prevention workload. Whereas security analysts would take days to process threat data, AI systems are designed to process big data quickly while detecting interesting or strange information, such as unusual user behaviors, malicious activities, exposed attack surfaces, and possible threats. Therefore, AI is a building block for achieving a strong cybersecurity posture since the advanced threat hunting capabilities identify security issues rapidly.
Top Trends in AI Cybersecurity
The rapid growth of AI and machine learning has continued to define organizational cybersecurity daily. As a result, the following are some of the top trends resulting from increased reliance on the technology as the cybersecurity industry battles sophisticated bad actors.
- Responding to attacks: At least 69% of cybersecurity professionals believe that combining artificial intelligence and cybersecurity is key to responding to cyberattacks. Additionally, many telecom organizations (80%) count on AI cybersecurity systems to detect sophisticated threats and prevent attacks.
- Greatest fraud detection potential: Cybersecurity systems based on AI has the greatest potential for detecting online fraud, malware, and network intrusions. AI cybersecurity has pattern recognition capabilities and can analyze network traffic patterns to detect anomalous behaviors and restrict unauthorized or unauthenticated system access.
- Enhanced threat detection and incident response: The use of AI in cybersecurity enhances an organization’s ability to detect different types of threats and respond to incidents with zero or minimal human intervention. Due to this, 51% of cybersecurity experts use AI-based intelligent machines to predict, detect, and prevent unknown threats.
- Companies depend on AI as a foundation for cybersecurity automation: Enterprises rely on AI-driven cybersecurity automation to support emerging digital business models and the adoption of new technologies. In addition, using AI-based cybersecurity automation frameworks can enable the correlation of threats data on vast amounts of data, providing opportunities for creating a sophisticated cybersecurity space with minimal business interruptions.
- Companies have increased spending on machine learning and AI for cybersecurity: Most organizations have increased spending on machine learning and AI for cybersecurity since they play a significant role in protecting personal data. A recent study found that 26% of US cybersecurity teams believe that their organizations can do more to leverage AI benefits to avert security risks. On the other hand, 84% of the study respondents believe that hackers use adversarial machine learning to execute intelligent and hard to detect attacks. Considering all the figures, companies cannot regard AI-driven cybersecurity as a luxury but rather a necessity to protect against modern attacks.
Role of AI in Strengthening Cybersecurity
As more organizations adopt online activities and increasingly depend on the Internet of Things (IoT), there have been increased challenges in protecting against unknown threats. Also, the rollout of 5G networks and advanced cloud services have scaled in size worldwide, such that cyber adversaries are always innovating complex techniques to exploit the increasing vulnerabilities in an expanding threat landscape. Fortunately, the rate at which enterprises are adopting AI and machine learning cybersecurity technologies has increased as they turn away from the traditional methods to modern threat detection and prevention capabilities.
Furthermore, today’s world generates at least 1.145 trillion MB every day, an amount beyond what human analysts, traditional techniques, and human intelligence can monitor or defend. As a result, an AI-driven, self-learning cybersecurity system using neural networks has become integral to modern businesses in securing sensitive data and critical networks against attacks. AI technologies can continuously gather data across organizational information systems to analyze it for hidden threats, predict data or network breach risks, and respond to threat alerts appropriately in real-time.
AI plays an important role in cybersecurity since IBM notes that AI-powered security applications provide unmatched accuracy in detecting threats. Accurate threat detection is pertinent to realizing a proactive security approach where companies can mitigate identified threats in real-time rather than waiting for a disaster to occur. For instance, companies can integrate advanced AI capabilities to evaluate risks and manage vulnerabilities in their security technologies. Also, AI-driven security technologies assist in assessing risks in specific user sessions, monitor suspicious behaviors, and verify/prioritize risks accordingly.
Common Applications of AI in Cybersecurity
1. Developing Robust Systems
The need to detect and respond to adverse cyber events requires robust self-learning systems to establish context for distinguishing between normal and bizarre behavior. In AI-enabled cybersecurity, robust systems imply the ability to detect anomalies while profiling everything else. In addition, AI in cybersecurity systems cuts through the noise from benign false positives common in traditional security apparatuses.
Cybersecurity experts can also incorporate AI in cybersecurity systems’ development to enhance cybersecurity controls. For instance, fully automated vulnerability management and assessments utilize AI capabilities to reduce the detection and response times, a desirable attribute in protecting against modern stealth attacks.
Moreover, code review to detect malware is an AI application in cybersecurity for developing robust systems. Code review is a recommended security practice for application development, but manual processes take time and may fail to identify some security flaws. Using AI to automate source code review increases the number of security vulnerabilities found while lowering the detection time significantly.
It is essential to note that incorporating AI in developing robust cybersecurity systems results in a tactical impact – reducing security flaws and enhancing network/computer security. Also, implementing AI-driven systems has a strategic effect since they lower zero-day attacks’ impacts. Attackers execute zero-day attacks to exploit security weaknesses yet to be detected, but AI-enabled cybersecurity tools can identify and alert such vulnerabilities in real-time.
2. Increasing Cybersecurity Resilience
Resilience is a network or system’s ability to facilitate anomaly and threat detection as a way of tolerating an attack to prevent downtime. A critical system can be resilient if it can adapt to adversarial events by adapting to the new environment to maintain normal functions and operations. Therefore, system cybersecurity resilience implies an automatic shift of core processes to adapt to changing security environments. In this regard, one of the common applications of AI in cybersecurity is threat and anomaly detection because:
- Thousands of unique malware variants are emerging every day.
- Human beings alone can’t identify and classify new threats since they are becoming more complicated every day.
- Traditional threat detection measures, such as using database signatures to match suspected threats with known threats, are ineffective in detecting advanced malware strains that evolve by the second.
AI cybersecurity solutions can enable organizations to automate cyber defenses to enhance resilience to attacks. These include compromised networks or computers, malware attacks, phishing scams, and online frauds. Also, companies can leverage AI-driven predictive analytics to determine the possibility of attacks, assisting them in bolstering their defenses in advance. Predictive analytics analyzes data from multiple sources to establish attack vectors and enable automatic management of big data. As such, it reduces human errors and simplifies the security analysts’ workloads.
3. Attack and Threat Response
Responding to threats and attacks requires a security system first to determine the malicious event happening in a network, identify all vulnerabilities, and prioritize them to determine the most severe requiring an immediate response. Implementing AI-enabled cybersecurity threat detection and response systems is pertinent to the deployment of real-time response security measures.
For instance, AI can respond to attacks by deploying autonomous lures, which create an environment similar to that an attacker attempts to compromise. As a result, the AI solutions can deceive the attackers into understanding the payloads to be used, thus providing a suitable response to stop the attack from occurring in the protected environment. Besides, AI-powered solutions use dynamic network segregation to isolated crucial assets in controlled network areas or redirect intrusions away from valuable assets.
AI is Also Used for Malicious Cybersecurity Reasons
Experts term the use of AI in cybersecurity as a double-edged sword, and for a good reason. Cyber-attackers use AI solutions to develop new threats to increase the success rate of an attack. For example, the ready availability of inexpensive AI tools enables malicious actors to expand existing threats. AI tools can automate tasks to enable attackers to impersonate people or systems in real-time. In such a case, they can execute an attack like password harvesting without using noisy tools that leave traces, making it harder to detect the attacks.
Also, AI-driven cyber-attacks are challenging to detect and prevent because of the following reasons:
- They are evasive: It is more challenging to detect AI-powered malware since they leverage AI capabilities to hide in benign applications. The malware can use various attributes to identify a target system and conceal their intent until they execute where they will cause the most damage.
- They are pervasive: The pervasive potential of intelligent technologies is a defining point for AI-enabled machines and malware. AI-driven smart malware can recognize and react to a different network, system, or user patterns, enabling them to identify targets and execute without the need for human instructions.
- They are adaptive: AI technologies are adaptive, implying that they can learn the surrounding environment and become creative to adapt accordingly. Therefore, malware crafted based on AI technologies can adapt to evade detection and increase the success rate of an attack once executed.