Artificial intelligence definition
Artificial intelligence (AI) is a branch of computer science focused on developing systems that can perform tasks traditionally requiring human intelligence – such as classification, pattern recognition, natural language comprehension, workflow orchestration and computer vision. One of AI’s early pioneers, Stanford computer scientist John McCarthy, defined the field as:
“The science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
AI in cybersecurity
With regard to cybersecurity, AI is playing an increasingly important role in both defending against and enabling cyberattacks. On the defensive side, security teams use AI to:
- Detect anomalies and malicious activity across large, complex environments
- Analyze behavior through user and entity behavior analytics (UEBA)
- Automate alert triage and improve signal-to-noise ratios in detection and response (D&R) systems
- Forecast likely attack paths using predictive analytics
- Reduce repetitive tasks for analysts by autonomously investigating alerts in real time
AI can be effective at automatically detecting rare, anomalous, and potentially malicious events that would be difficult or time-consuming for human analysts to catch on their own. However, attackers are also adopting AI – often with alarming speed. Tools powered by AI can:
- Produce convincing phishing emails, malicious code, or deepfake content at scale
- Enhance social engineering efforts by mimicking writing styles or synthesizing voices
- Experiment with AI-generated malware that dynamically evades traditional detection methods
Benefits and challenges of AI in cybersecurity
AI is increasingly seen as a force multiplier in cybersecurity – amplifying the speed, scale, and precision of security operations. When used effectively, AI enables security teams to streamline workflows, surface hidden threats, and respond to attacks faster and with greater confidence. Some key benefits include:
- Automated analysis and response: AI systems excel at handling repetitive tasks like log management and correlation, alert triage, and enrichment, freeing up analysts to focus on high-impact work such as threat hunting or incident response.
Improved signal-to-noise ratio: Within tools like extended detection and response (XDR) platforms, AI helps filter out false positives and prioritize true threats, reducing alert fatigue and speeding time to action.
Adaptive threat modeling: AI can continuously learn from new behaviors and threat patterns, allowing detection systems to evolve in step with attacker tactics.
Smarter penetration testing: AI-powered tools can simulate a wide variety of attack scenarios, automatically probing for weaknesses and identifying gaps in defenses before attackers find them.
Potential challenges
Despite its benefits, implementing AI in cybersecurity isn’t without challenges. Some obstacles that security operations centers (SOCs) may face include:
Lack of high-quality datasets: Training effective AI models may require large volumes of clean, labeled data – often proprietary, sensitive, or expensive to collect. Poor-quality data can lead to inaccurate models or biased outcomes.
Model training manipulation: Attackers can attempt to subtly alter the training phase of a model to introduce vulnerabilities or skew its outputs – especially problematic in large deep learning systems.
Data poisoning: This tactic involves injecting malicious or misleading data into training datasets so the model behaves in unintended ways, like ignoring certain attack types or misclassifying malicious files as safe.
Model inversion: In a model-inversion attack, adversaries attempt to extract private or sensitive information from a trained model, such as recovering passwords, medical data, or proprietary business information. While still difficult to execute, advances in inversion techniques pose a growing concern.
AI vs. machine learning vs. deep learning
Among the most prominent and practical subfields of AI are machine learning and deep learning. These two approaches form the foundation for most modern AI applications, from recommendation engines and fraud detection to generative tools like large language models (LLMs).
Machine learning (ML)
Machine learning is a subset of AI in which systems are trained on historical data to learn its inherent patterns and characteristics. By analyzing structured inputs – such as numerical data, labeled categories, or other tabular information – ML algorithms identify patterns and make predictions without being explicitly programmed for each outcome. ML can perform strongly in tasks like:
- Fraud detection in financial transactions
- Predictive maintenance in industrial systems
- Customer churn prediction in marketing
Deep learning (DL)
Deep learning systems also learn from data but specifically use neural networks to encapsulate knowledge, originally inspired by neurons in the human brain. DL, like ML, can handle structured data but is well-suited for learning from unstructured data, enabling models to:
- Classify and tag images
- Transcribe speech
- Translate languages
- Generate text, code, or even visuals
AI vs. ML
While ML is often used interchangeably with AI in casual conversation, they are not the same. AI is the overarching concept: any system capable of performing tasks that mimic human intelligence. ML is a method within AI focused on training algorithms to learn from data and improve performance over time.
AI vs. DL
Deep learning takes data-driven learning to the next level due to its ability to handle both structured and unstructured data. These models can understand more complex relationships in large datasets, especially unstructured ones, and they scale well with compute power. For example:
- A traditional ML model might use a decision tree to flag spam emails based on known keywords.
- A DL model, by contrast, could analyze a full message, detect tone, context, and patterns across millions of emails to catch evolving phishing tactics.
As a result, DL with neural networks is foundational to today's most powerful generative models and is a key reason AI capabilities have progressed so rapidly in recent years.
AI use cases
Cybersecurity
AI plays a dual role in cybersecurity – as both a powerful defense mechanism and an increasingly sophisticated tool for attackers. To stay ahead, defenders are applying AI in areas such as:
- Antivirus and malware detection: Identifying patterns and behaviors associated with known and unknown threats, including polymorphic malware.
- Anomaly detection: Monitoring network traffic, user behavior, and endpoint activity for deviations that may indicate compromise.
- Threat intelligence and analytics: Analyzing massive datasets to spot trends, correlate events, and enrich alerts with contextual data.
- Incident response: Automating steps in triage, prioritization, and even initial containment.
Speech recognition
Speech recognition remains one of the most widely used AI applications. Systems like Apple's Siri, Amazon's Alexa, Google Assistant, and others rely on AI to:
- Convert spoken commands into written text
- Understand user intent and deliver contextually appropriate actions
- Handle diverse accents, dialects, and background noise
Multilingual support and real-time transcription accuracy have significantly improved with recent deep-learning advancements.
Natural language generation and processing
Natural language generation (NLG) and natural language understanding (NLU) are part of the broader field of natural language processing (NLP) – AI’s ability to read, interpret, and respond to human language. These technologies power systems that:
- Summarize reports, articles, or technical documentation
- Translate content between languages
- Extract entities and intent from support tickets, legal texts, or user queries
- Generate personalized content across industries
Together, NLU and NLG enable AI to engage with users in a way that feels increasingly natural, fluent, and context-aware.
Generative AI assistants and chatbots
Modern generative AI assistants, such as ChatGPT, Claude, Gemini and others with a chat interface are powered by LLMs trained on massive datasets of human language.
These systems are increasingly multimodal, meaning they are capable of understanding not just text, but also images, code, or voice inputs. Key applications include:
- Content generation: Drafting articles, essays, emails, scripts, or generating visual assets like illustrations and UI mockups.
- Customer service: Providing immediate, intelligent responses to user queries — often integrated with CRM and ticketing systems.
- Creative and collaborative tasks: Assisting in brainstorming, story development, or ideation for marketing campaigns, game design, and more.
- Productivity tools: Supporting code generation, spreadsheet manipulation, and document summarization.
As adoption grows, enterprises are embedding AI assistants across departments — from IT and HR to legal and finance — to reduce friction, increase efficiency, and enhance digital experiences.
Generative and agentic AI
Generative AI (GenAI) is one of the most dynamic and widely adopted branches of artificial intelligence today. It refers to the use of large language models (LLMs), including very powerful foundational models, to produce new content based on patterns learned from vast datasets. This content can take many forms, including text, code, images, video, and even audio.
What makes GenAI so powerful is its ability to create human-like outputs that are contextually relevant, syntactically accurate, and, increasingly, multimodal (i.e., capable of interpreting and generating content across formats).
GenAI in cybersecurity
As threat actors embrace GenAI to scale their operations and innovate attack strategies, defenders must also evolve their tools and tactics. GenAI presents several high-impact applications within modern security operations.
Automated code review and refactoring
Generative models can assist in auditing and rewriting source code to identify risky constructs, insecure logic, or outdated libraries. This proactive use of AI helps reduce software vulnerabilities before code is deployed.
Operational efficiency through automation
Security teams are increasingly using GenAI to handle routine-but-time-consuming tasks – such as drafting incident response reports, summarizing threat intel, or generating executive briefs from technical data.
Threat simulation and training
GenAI can be used to craft realistic phishing emails, generate social engineering scenarios, or simulate malware payloads in controlled environments. These breach and attack simulations (BAS) support more effective security awareness training and purple team exercises.
Real-time incident insights
During active incidents, GenAI can assist with analyzing attack sequences, surfacing likely threat-actor tactics, or generating suggested remediation steps – at speeds much faster than manual investigation alone.
Knowledge consolidation and querying
AI agents powered by generative models can assist in querying internal knowledge bases, previous incident data, or compliance requirements in natural language – reducing search time and helping SOC teams move faster during investigations.
Agentic AI in cybersecurity
Agentic AI refers to a class of artificial intelligence systems designed to act autonomously toward a goal – orchestrating workflows, making decisions, taking actions, and incorporating user feedback to improve over time.
Unlike traditional AI models that respond to specific inputs or prompts, agentic AI operates more like a digital agent: It can initiate tasks, reason through steps, and modify its plan based on environmental feedback or constraints.
These systems often combine planning, memory, and tool use, enabling them to perform multi-step workflows with minimal human intervention. For example, an agentic AI could receive a goal like “investigate a potential vulnerability” and then dynamically decide how to gather data, analyze logs, summarize findings, and recommend next steps.
While still emerging, agentic AI shows promise in areas like cybersecurity automation, digital operations, and decision support – particularly when tasks are too complex or time-consuming for rule-based systems alone.