Cybersecurity, amidst rising internet use, faces complexity. AI technologies enhance prevention and detection in this evolving threat landscape. As more and more people and businesses are going online and putting up critical data that unauthorized users could misuse, the importance of cybersecurity has developed a newfound meaning. The processes of preventing and detecting threats online were fairly straightforward initially, with probable attacks and the course they would follow. It’s just that these processes were inadequate in the face of the evolving threat landscape.
The traditional approach to cybersecurity was reactive and often drained the organizational resources. Even those threats identified and factored in the databases did not have a clear identification for detection. Many of the identified threats were prevented with the help of access controls, while the newer ones easily bypassed the established firewalls. Rule-based systems could only trigger alerts and sought security analyst’s expertise to take action. And then, there were the false positives, alerts triggered by legitimate activities mistaken for threats, that consumed much of the cybersecurity professional’s time in QCing.
But as technology evolved and artificial intelligence (AI) became mainstream, enterprises started automating specific redundant tasks that required grunt work. This approach freed up the cybersecurity professional’s time while improving the screening performance. AI is an excellent tool capable of following algorithms and learning from the trends over time as it is deployed across use cases. Algorithms can be trained to make informed decisions based on their alert analysis. AI-driven algorithms can handle standard alerts and trigger a warning in case of anomalous behavior to prevent newer attacks. Unsurprisingly, the AI in cybersecurity market is forecasted to grow at a CAGR of 21.9% in the period 2023-28.
Popular Cybersecurity AI Technologies
AI is increasingly employed for threat detection and prevention across various domains, including cybersecurity, fraud detection, and physical security. AI algorithms analyze transaction data, user behavior, and payment patterns to detect fraudulent activities,
including credit card fraud and identity theft.
Here are some specific AI technologies commonly used for threat detection and prevention:
Machine Learning (ML) – Anomaly Detection
ML is a critical component of AI-based cybersecurity, providing the capability to process and analyze vast amounts of data, adapt to evolving threats, and automate threat detection and response. ML algorithms are trained to recognize deviations from established patterns. In cybersecurity, ML can identify unusual network traffic, user behavior, or system activity that may indicate a security threat. ML models continuously learn and improve accuracy, making them valuable assets for enhancing an organization’s cybersecurity posture in an increasingly complex and dynamic threat landscape.
Natural Language Processing (NLP) – Text Analysis
NLP plays a significant role in AI-based cybersecurity by enabling the analysis and understanding of human-generated text data, such as emails, chat logs, and social media posts, to detect threats like phishing emails and malicious content. NLP is employed in data loss prevention (DLP) solutions to classify and analyze text data for sensitive information, helping organizations prevent data leaks and unauthorized data transfers. NLP can also parse vast amounts of unstructured text data from various sources, including security blogs, forums, and news articles. It helps security teams stay updated about the latest threats, vulnerabilities, and attack techniques.
Natural Language Generation (NLG) – Text Generation
NLG technology can automatically generate incident reports, security alerts, and executive summaries in human-readable language, simplifying communication and decision-making for security teams and stakeholders.
Computer Vision (CV) – Video Surveillance
CV technology analyzes video feeds from security cameras to detect suspicious activities, unauthorized access, and intruders. Facial recognition is also used for access control.
Deep Neural Networks (DNNs) – Deep Learning
Deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can be applied to image and video analysis, enabling advanced threat detection in visual data.
User and Entity Behavior Analytics (UEBA) – Behavioral Analysis
UEBA is a vital component of AI-driven cybersecurity that focuses on building baseline behavior profiles for users and entities, monitoring, analyzing, and
responding to user and entity behavior. It aids in the early detection of security threats, including insider threats, and provides security teams with actionable
insights to improve incident response and mitigate risks effectively.
Predictive Modeling – Predictive Analytics
AI-driven predictive models use historical data to forecast potential threats and vulnerabilities, allowing organizations to take proactive measures to prevent security incidents.
Intrusion Detection Systems (IDS) – Network Traffic Analysis
Intrusion prevention systems (IPS) and IDS analyze network traffic patterns to identify malicious activity, including malware and advanced persistent threats. When integrated with artificial intelligence (AI) and machine learning (ML), IDS becomes more effective and adaptive in identifying and responding to evolving threats. AI- based IDS systems can reduce the number of false positives. AI-enhanced IDS systems can incorporate threat intelligence feeds, indicators of compromise (IOCs), and threat signatures into their analysis to identify known threats and indicators of emerging threats. ML-based IDS models can learn to distinguish between normal and suspicious behavior more accurately over time.
Cognitive Computing – Cognitive Threat Detection
Cognitive computing systems combine AI, ML, and human-like reasoning to detect complex threats by understanding context and making informed decisions. AI- enhanced threat intelligence collects, analyzes, and processes large volumes of threat intelligence data from various sources to identify emerging threats and vulnerabilities.
Natural Language Understanding (NLU) – Voice and Chatbot Security
NLU technologies are applied to secure voice assistants and chatbots, ensuring they do not inadvertently disclose sensitive information or engage in malicious activities. NLU-driven chatbots and virtual assistants can deliver interactive security awareness training programs, simulate phishing attacks, and guide employees on best practices.
Blockchain Technology – Immutable Ledgers
While not an AI technology per se, smart contracts deployed on blockchains can enhance security by providing tamper-proof records, secure identity verification, and improved supply chain security, among other applications. Blockchain-based smart contracts can automate cyber insurance policies and claims processing. This can streamline the insurance process and provide transparency in a cyber incident. In the context of cybersecurity, it can be used to ensure the integrity and immutability of critical security-related data, such as logs, access records, and incident reports. In the Internet of Things (IoT), blockchain can be used to manage and authenticate IoT devices securely. This ensures that only authorized devices can connect to a network, reducing the risk of IoT-based cyberattacks. Blockchain-based identity verification can be integrated into zero-trust network architectures. Users and devices can be authenticated based on their blockchain identity, enhancing security in perimeter- less environments.
Hackathons – Threat Hunting
Hackathons provide a platform for participants to brainstorm, ideate, and develop innovative solutions to real-world security problems. They encourage creative thinking and the development of new AI-driven security tools and techniques. Many hackathons focus on addressing specific security scenarios or industry challenges. Participants gain exposure to practical, real-world security problems and can develop solutions relevant to current cybersecurity issues. Organizations and cybersecurity companies often sponsor or participate in hackathons to identify and recruit talent. They can scout for potential employees who demonstrate exceptional skills and creativity.
Cloud-Based AI – Automated Security
Cloud platforms offer AI-driven security services, such as threat detection as a service (TDaaS), that provide scalable and cost-effective threat prevention solutions. AI technologies are continually evolving, and their application in threat detection and prevention continues to advance. Organizations increasingly rely on AI to enhance their security postures and respond effectively to the evolving threat landscape.
Effective Cybersecurity Produces Better Overall Outcomes
The adoption of AI in cybersecurity is owed to the rising instances of ransomware attacks in enterprises globally. These attacks could be of small value, but the operational disruption they cause and the vulnerabilities these attacks reveal are quite troublesome for the business. As per Cisco’s threat report 2021, 50% of US organizations have faced ransomware attacks. It is, therefore, imperative for organizations to stay clear from these attacks in order to secure their top lines and critical IT infrastructures.
In light of deploying AI across industries to control and manage the burgeoning threat landscape, it should be noted that AI is also not yet sophisticated enough to prevent zero- day attacks. These attacks where developers have “0 days” to fix the vulnerability pose a challenge and present an opportunity. It’s important to note that no cybersecurity approach can guarantee 100% protection against zero-day attacks because these attacks target vulnerabilities unknown to the vendor and, therefore, unpatched. However, a Zero Trust framework, by emphasizing strong access controls, continuous monitoring, and security best practices, can significantly reduce the attack surface, limit the lateral movement of attackers, and provide early detection and response capabilities, ultimately enhancing an organization’s ability to withstand and mitigate the impact of zero-day attacks.
Effective cybersecurity is essential for protecting individuals, organizations, and society from the increasing threat of cyberattacks. It not only prevents financial losses and data breaches but also supports economic growth, innovation, and national security. Investing in cybersecurity measures and practices is essential to modern digital life and business operations.
What is a zero-day attack?
A zero-day attack is a type of cyberattack that takes advantage of a security
vulnerability or weakness in a software application or system that is not known to the vendor or developer of that software.
What is the Zero Trust framework?
Zero Trust is a cybersecurity approach that operates on the principle of “never trust, always verify.” A zero-trust framework can significantly enhance an organization’s security posture and reduce the risk of falling victim to such attacks.
How does forensic analysis help in AI-driven cybersecurity?
Forensic analysis plays a crucial role in AI-driven cybersecurity by helping
organizations investigate security incidents, identify the root causes of breaches, gather evidence, and support incident response efforts.
- Is AI-driven cybersecurity costlier than the traditional cybersecurity approach?
While AI-driven cybersecurity may involve initial investments and ongoing expenses, it also offers potential cost-saving benefits. To determine whether AI-driven cybersecurity is costlier or more cost-effective than traditional approaches, organizations should conduct a comprehensive TCO analysis that considers initial and ongoing costs and potential savings and benefits.
What is SIEM?
AI is increasingly integrated into security information and event management (SIEM) solutions to improve log analysis, correlation, and alert prioritization, reducing false positives and alert fatigue.