Unpacking AI, Machine Learning, NLP, GenAI and LLMs in Cybersecurity

Artificial Intelligence (AI) has shifted from hype to practical utility in nearly every industry. In cybersecurity, it represents both an opportunity and a challenge. As organizations rush to adopt GenAI tools and Large Language Models (LLMs), attackers are doing the same. AI is being embedded in security platforms, weaponized by adversaries, and used by business units enterprise wide. The result? A massive and growing attack surface that most companies aren’t prepared to defend.

For cybersecurity providers, the conversation isn’t just about building smarter tools. It’s about enabling secure innovation. To do that, we must first clarify what we mean when we say AI, ML, GenAI, NLP, or LLM because they serve very different functions.

Defining AI Pillars in Cybersecurity

As AI advances at lightning speed, precision is everything, and misunderstanding these concepts can lead to failed strategies and missed threats. So, let’s break these down:

Artificial Intelligence (AI)

AI serves as a strategic enabler in cybersecurity – augmenting security teams, automating tasks, and accelerating decision-making. Think of AI as the category under which all the following technologies fall.

Machine Learning (ML)

ML is a subset of AI focused on pattern recognition and prediction through data. In cybersecurity, ML powers threat detection. It ingests massive volumes of logs and telemetry data to detect anomalies, track attacker behaviors, and flag previously unseen threats. For example:

• Behavioral analytics on endpoints
• Detection of lateral movement across networks
• Predictive models that forecast attack likelihood

ML is about aggregation, analysis, and evidence-based action.

Generative AI (GenAI)

GenAI creates context – text code, images – based on training data. In security, it’s emerging as a support tool: automating reporting writing, simulating phishing campaigns, or generating synthetic attack traffic for testing.

Natural Language Processing (NLP)

NLP is a subset of AI that focuses on enabling machines to understand, interpret, generate, and respond to human language. It’s what allows systems to process language, whereas LLMs are a type of NLP model that uses deep learning to generate human-like responses at scale.

Large Language Models (LLMs)

LLMs are a specific type of GenAI trained on vast amounts of text to perform language-based tasks. They are best understood as communication and translation engines, turning complex threat data into natural language explanations or assisting analysts with query generation in SIEM tools.

In cybersecurity:

• ML is for detection and decisioning.
• LLMs are for communication and interpretation.
• NLP is for understanding and extracting insights from unstructured data.
• AI is the strategic enabler that brings them together.

The confusion arises because people often lump all these technologies together, assuming they serve the same function. But recognizing their distinct roles is crucial, especially when building or securing AI-powered solutions.

The Use of GenAI

Organizations are rapidly integrating GenAI applications – often LLM-based – to automate customer support, draft technical plans, write code, and more. This opens new avenues for productivity but also increases risk.

Many GenAI tools connect directly to business systems, have access to sensitive data, or rely on APIs that lack proper authentication. Moreover, their behavior can be unpredictable, and they can be manipulated via prompt injection or data poisoning.

Security teams must:

• Test these applications like they would any other software asset
• Validate access controls and data flow
• Simulate attacks against the model to understand potential misuse

Whether it’s a chatbot or a Gen-AI R&D assistant, the attack surface has expanded. These apps must be part of your security testing strategy.

AI in Cybersecurity Platforms

For security providers, the use of AI isn’t just about fighting cyber threats. It’s about empowering businesses to detect, respond, and scale securely. AI, ML, NLP, and GenAI are embedded within the cybersecurity stack not as gimmicks, but as force multipliers.

How They Work Together

• ML is the workhorse. It enables platforms to continuously learn from network activity, classify behaviors as malicious or benign, and prioritize alerts. It improves fidelity, reduces noise, and make real-time response possible.
• NLP is the translator. It extracts meaning from unstructured data, like threat intel reports, support tickets, or Dark Web chatter – turning natural language into actionable signals for security teams.
• GenAI/LLMs serve as copilots. They help analysts navigate mountains of threat data by summarizing indicators, generating playbooks, or explaining attacks in plain language – democratizing security understanding across teams.
• AI overall orchestrates these capabilities to deliver speed, accuracy, and automation – critical in today’s threat landscape.

This layered use of AI technologies each serve a distinct purpose and is what makes security platforms smarter, faster, and more scalable.

Shadow AI is Already Here

LLMs and GenAI tools aren’t just in the hands of security teams. They’re in finance, HR, engineering, legal, and customer support. Shadow AI has become as widespread as shadow IT once was.

Each of these use cases presents unique security and compliance challenges. Is the data being shared with third-party APIs? Are prompts stored or logged? Could the model generate biased, inaccurate, or sensitive content?

Security teams should:

• Work cross-functionally to establish safe usage policies
• Audit LLM-based applications and plug-ins
• Provide guardrails and secure, enterprise-ready GenAI tooling

Conclusion

In an age where attackers and defenders alike are armed with AI, the difference between success and failure lies in clarity and control. Cybersecurity leaders must demystify these technologies, not just for their teams, but for organizations and customers.

AI, like all transformative technologies, demands responsible implementation, proactive testing, and continual vigilance. The future of cybersecurity isn’t just about building walls – it’s about understanding the architecture, the language, and the intelligence behind those walls. Because if you can’t explain it, you can’t secure it.

Author

Ann Chesbrough

Vice President of Product Marketing, BreachLock