AI in Cyber Security: Techniques and Technologies Explained Easy

By this post I aim to provide an accessible overview of how AI is reshaping cyber security and to serve as a resource for professionals keen to understand and approach these techniques and technologies.

Let's dive into how artificial intelligence is completely transforming the world of cybersecurity

We’ll explore the cutting-edge AI techniques that are shaking up security strategies describing what is intended for anomaly detection, behavioural analysis, machine learning-based threat detection, predictive analytics and natural language processing, taking you through some of the most performing AI-driven technologies, from AI-enhanced SIEM and UEBA to SOAR and Endpoint Detection Systems. By integrating advanced algorithms into security protocols, organisations can now identify anomalies, analyse behavioural patterns, and even counter sophisticated attacks in real time. This fusion of AI and cyber security not only increases efficiency, but also helps to create a proactive defence strategy against ever changing cyber threats.

Delve Deeper into the Cutting-Edge World of Cybersecurity

First, we introduce the key AI-driven techniques that form the foundation of modern threat detection and response, such as anomaly detection and behavioural analysis. Next, explore the innovative technologies that harness these techniques, ranging from AI-enhanced SIEM to AI-driven endpoint detection and response, that are reshaping how organisations secure their digital environments.

Key AI-Driven Techniques in Cyber Security

From anomaly detection and behavioural analysis to machine learning-based threat detection, predictive analytics, and NLP, these techniques empower organisations to identify and mitigate threats proactively, safeguarding digital infrastructures: I’ll try to shortly introduce each one of them, with the intent to discover how they play a crucial role in today’s advanced cyber defence strategies.

Anomaly Detection This technique utilises AI algorithms to monitor network traffic and system behaviours, establishing baselines of ‘normal’ activity. Deviations from these norms—such as unusual data flows or unexpected login times—are flagged as potential threats. Anomaly detection is essential for early identification of breaches and can alert security teams before significant damage occurs.

Behavioural Analysis Behavioural analysis involves the continuous monitoring of user and entity activities to identify patterns that may indicate malicious intent. By understanding typical behaviours, AI systems can recognise subtle deviations that might suggest insider threats, compromised accounts, or other suspicious activities, thereby allowing for a more nuanced response.

Machine Learning-based Threat Detection Harnessing the power of machine learning, this technique trains models on vast amounts of data to recognise known threat patterns and predict emerging ones. Over time, these models become increasingly accurate, enabling them to detect even sophisticated attacks that traditional rule-based systems might miss.

Predictive Analytics Predictive analytics leverages historical data and statistical algorithms to forecast potential security incidents. By analysing trends and patterns, AI systems can anticipate where and when cyber attacks are likely to occur, empowering organisations to strengthen their defences proactively.

Natural Language Processing (NLP) NLP is employed to scrutinise and interpret vast quantities of unstructured data—such as emails, chat logs, and social media content—to detect phishing attempts and other forms of social engineering. By understanding context and sentiment, NLP tools can flag communications that might be designed to deceive or manipulate.

Technologies to Power Techniques

Let’s have a look to some AI-powered technologies that put on the field the above techniques, redefining at same time the cyber security market. From AI-enhanced SIEM to UEBA, SOAR, AI-powered Deception Technology, and AI-driven Endpoint Detection and Response (EDR), these innovations integrate advanced analytics and automation to deliver real-time threat detection, streamlined incident response, and robust endpoint protection against ever-evolving cyber threats.

AI-enhanced SIEM Security Information and Event Management (SIEM) systems have evolved with the integration of AI to provide real-time analysis of security alerts. AI-enhanced SIEM tools correlate data from various sources, rapidly identify potential threats, and offer contextual insights that help security teams respond more effectively.

UEBA (User and Entity Behaviour Analytics) UEBA platforms use AI to build behavioural profiles of users and entities within an organisation. By continuously comparing current activities against these profiles, UEBA can detect deviations that might indicate compromised credentials or insider threats, thus providing a deeper level of security insight.

SOAR (Security Orchestration, Automation and Response) SOAR platforms combine AI with automation to streamline incident response processes. They integrate data from multiple sources, automate routine tasks, and coordinate responses across various security tools, enabling faster and more coordinated action when a threat is detected.

AI-powered Deception Technology Deception technology creates decoy systems or data that mimic real assets to lure attackers into engaging with fake targets. AI enhances these systems by dynamically adjusting the decoys based on evolving threat tactics, thereby confusing adversaries and buying crucial time for security teams.

AI-driven Endpoint Detection and Response (EDR) EDR solutions augmented with AI continuously monitor endpoints—such as laptops, servers, and mobile devices—for signs of compromise. These tools not only detect suspicious activities but also employ automated responses to isolate and mitigate threats before they can spread across the network.

Looking Ahead about the Future of AI in Cyber Security

Reflecting on the evolving role of AI in cybersecurity, I recognise a convergence of insights that shape my understanding. We have to appreciate the critical importance of AI’s rapid threat detection capabilities in fortifying our cyber defences, while remaining mindful of the inherent risks as adversaries continually refine techniques to exploit vulnerabilities, but much more, transparency and ethical considerations in deploying AI-driven security measures, must ensure that advancements do not compromise user privacy or fairness.

Market Solutions for Each Technique and Technology

From theory, to analysis, to the practice: here a selection of market solutions showcasing the application of AI-driven techniques and technologies in cybersecurity. These tools, tailored for threat detection, response and proactive defence, highlight the innovation and expertise available to fortify your organisation's digital landscape. I have tried to list them by techniques, but, of course, some solutions are multipurpose, so you will found theme mentioned several times.

Anomaly Detection

1. Darktrace – https://www.darktrace.com/
2. Vectra AI – https://www.vectra.ai/
3. ExtraHop – https://www.extrahop.com/

Behavioural Analysis:

1. Exabeam – https://www.exabeam.com/
2. Securonix – https://www.securonix.com/
3. Gurucul – https://www.gurucul.com/

Machine Learning-based Threat Detection:

1. Cylance – https://www.cylance.com/
2. SentinelOne – https://www.sentinelone.com/
3. Deep Instinct – https://www.deepinstinct.com/

Predictive Analytics:

1. IBM QRadar Advisor with Watson – https://www.ibm.com/security/security-intelligence/qradar
2. Splunk Enterprise Security with Machine Learning Toolkit – https://www.splunk.com/
3. Rapid7 InsightIDR – https://www.rapid7.com/

Natural Language Processing (NLP)

1. ZeroFOX – https://www.zerofox.com/
2. PhishLabs – https://www.phishlabs.com/

AI-enhanced SIEM

1. IBM QRadar – https://www.ibm.com/security/qradar
2. Splunk Enterprise Security – https://www.splunk.com/
3. LogRhythm – https://logrhythm.com/

UEBA

1. Exabeam UEBA – https://www.exabeam.com/ueba
2. Securonix UEBA – https://www.securonix.com/
3. Rapid7 InsightIDR UEBA – https://www.rapid7.com/

SOAR

1. Palo Alto Cortex XSOAR – https://www.paloaltonetworks.com/cortex/xsoar
2. Splunk SOAR (formerly Phantom) – https://www.splunk.com/en_us/software/splunk-soar.html
3. IBM Resilient – https://www.ibm.com/security/resilient

AI-powered Deception Technology

1. Attivo Networks – https://www.attivonetworks.com/
2. Illusive Networks – https://www.illusivenetworks.com/
3. TrapX Security – https://trapx.com/

AI-driven Endpoint Detection and Response (EDR)

1. CrowdStrike Falcon – https://www.crowdstrike.com/
2. SentinelOne Singularity – https://www.sentinelone.com/
3. Carbon Black – https://www.carbonblack.com/