What is AI in cybersecurity?

AI, machine learning, and deep learning: The difference

The terms are often used interchangeably, but they nest inside one another. Artificial intelligence is the broad field of building systems that perform tasks normally requiring human intelligence. Machine learning (ML) is a subset of AI: models that learn patterns from data to make decisions without being explicitly programmed for every case. In security, ML models train on large datasets of network traffic, file behaviors, and logs to distinguish normal from malicious activity.

Deep learning is a more advanced subset of ML that uses multi-layered neural networks to tackle complex problems, such as recognizing zero-day threats or detecting deepfakes. Generative AI, built on large language models (LLMs), creates new content and now assists analysts through natural-language querying and investigation. Understanding this hierarchy matters: a vendor claiming AI could mean anything from a simple statistical model to an autonomous reasoning agent.

How AI is used in cybersecurity

On the defensive side, AI has moved from a nice-to-have to a foundation of modern security operations. Its main applications include:

Threat detection and anomaly detection

AI establishes a baseline of normal behavior for users, devices, and applications, then flags deviations that may signal a breach. Because it analyzes behavior rather than known signatures, it can catch new malware variants, zero-day exploits, and APTs that bypass legacy tools. A user who normally accesses marketing documents suddenly attempting to download financial data gets flagged automatically.

Predictive threat intelligence

Rather than only reacting to current threats, AI analyzes historical data and global threat trends to anticipate future attacks and predict which vulnerabilities attackers are most likely to exploit, shifting security from reactive to proactive.

Accelerated investigation and response

AI correlates alerts across endpoint, network, identity, and cloud into a single incident, reconstructs attack paths, and automates response actions such as isolating a compromised device or blocking a malicious IP, cutting investigation time from hours to minutes and reducing human error.

Alert triage and reduced false positives

By accurately scoring and prioritizing detections, AI cuts through the noise that overwhelms security teams, reducing false positives so analysts focus on genuine threats and avoid alert fatigue.

Analyst assistance and natural language

Generative AI assistants let analysts query data in plain English, summarize investigations, and guide less-experienced staff through workflows, lowering the barrier to effective security operations.

Vulnerability and exposure management

AI moves beyond static severity scores, weighing asset criticality, real-world threat intelligence, and exploit probability to predict and prioritize the exposures that pose the greatest actual risk.

Benefits of AI in cybersecurity

How attackers use AI (and security for AI)

The offensive use of AI is real, and honest coverage has to say so. Adversaries use AI and ML to automate and scale attacks, optimize and generate malware, evade detection, and craft highly convincing phishing and social engineering, including deepfake audio and video for impersonation. Campaigns that once took weeks of manual effort are becoming faster and cheaper.

This is where the second discipline, security for AI, comes in: protecting the AI systems organizations now depend on. As enterprises adopt generative AI and AI agents, those systems become an attack surface of their own. Key AI-specific risks include prompt injection and jailbreaks (manipulating a model's behavior through crafted input), data and model poisoning (corrupting training data to degrade or bias a model), model theft, and shadow AI (employees using unsanctioned AI tools, risking data leakage). A growing practice, AI security posture management (AI-SPM), aims to govern AI use, harden models, and enforce acceptable-use policies, aligned with emerging frameworks such as NIST AI guidance, MITRE ATLAS, the OWASP LLM Top 10, and the EU AI Act.

Challenges and limitations

AI is powerful, but deploying it well means confronting real limits. AI systems are only as good as the data they train on: poor-quality or biased data leads to inaccurate predictions and missed threats. Many models are black boxes, hard to interpret, which is a problem when analysts need to validate an alert or satisfy compliance requirements. That's why explainable AI (XAI) has become important. Attackers actively target the models themselves through adversarial inputs. Implementation requires specialized expertise that's in short supply. And AI still lacks human contextual judgment: it can process data and act fast, but interpreting nuance, understanding business context, and making high-stakes decisions remain human strengths.

The consensus across the field is that AI augments human expertise rather than replacing it.

Where AI lives in the security stack

AI is woven through the tools a modern security program already runs. Seeing where it operates clarifies that adopting AI in cybersecurity is less about buying a single AI product and more about how intelligence is applied across the stack:

• Endpoint (EDR/EPP): Deep-learning models detect known and never-before-seen malware by analyzing file characteristics and behavior rather than waiting for a signature.
• Network (NDR/NGFW): AI inspects traffic, models normal east-west behavior, and flags command-and-control channels, DNS manipulation, and lateral movement.
• SIEM and SOC platforms: AI correlates security events across sources, prioritizes high-fidelity incidents, and increasingly runs autonomous investigation and response.
• Email security: Natural language processing (NLP) analyzes tone, context, and metadata to catch impersonation, phishing, and business email compromise that rule-based filters miss.
• Identity and behavior (UEBA): AI baselines user and entity behavior to surface insider threats, account takeover, and risky non-human or agent identities.
• Vulnerability and exposure management: predictive models forecast which vulnerabilities are most likely to be exploited, focusing remediation where real risk lies.

Expert insight: From AI features to an AI-native SOC

Almost every vendor now advertises AI, but there's a meaningful difference between bolting AI features onto a legacy tool and building security operations around AI from the ground up. The first gives you a smarter alert here and a chatbot there. The second changes the operating model of the SOC itself, letting autonomous agents carry the routine load end to end while humans direct and approve. As agentic AI matures, that architectural choice is what separates teams that merely use AI from teams genuinely transformed by it.

Sekoia is built for the second model. The Sekoia SOC platform runs autonomous AI agents across the full loop: detection agents that combine behavioral analytics, signatures, and AI reasoning; investigation agents that pull process trees, network traces, and threat intel into a unified case timeline in seconds; and response agents, driven by Sekoia Elevate, that isolate hosts, disable credentials, or block activity automatically or with one-click approval. All of this funnels through a single AI engine that correlates activity with context, backed by roughly 1,000 detection rules mapped to MITRE ATT&CK in Sekoia Defend and by predictive, native Sekoia Intelligence (Sekoia was cited in Gartner's 2025 Emerging Technology report on AI-based predictive threat intelligence). Every action an agent takes is logged for analyst review and compliance, human-AI collaboration is built into the workflow, and as a European vendor, Sekoia pairs this with a genuine data-sovereignty posture that matters as much for security for AI as it does for AI for security.

When evaluating AI in cybersecurity, look past the feature list. Ask whether the platform was designed AI-native, and whether it keeps humans meaningfully in control.

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