When the AI Hype Cycle Meets the Cybersecurity Reality

There is a lot of talk about AI in cybersecurity right now. It is exciting, and for good reason. AI is driving real innovation across security operations. But like any technology shift, it is easy to get caught in the noise.

AI works best when it is applied with context, precision, and intent. That means knowing your environment, the cybersecurity risks you are addressing, and the outcomes you are working toward.

Security teams are not looking for magic. They are looking for the right tool to solve the right problem. And when it comes to AI in cybersecurity, the key is understanding how different models actually work and where they belong.

In this post, I will walk through:

• The four types of AI in security operations today
• How each one supports cyber resilience
• Why human analysts still matter
• How visibility makes the entire system work
  
  

Making Sense of the Hype Cycle

We believe in the power of AI, but we also believe in deploying it intentionally.

Too many security vendors pitch generic AI without tying it to specific outcomes. That leads to mismatched expectations and inconsistent results. The problem is not the AI itself. The problem is how it is applied when visibility and context are missing.

AI in cybersecurity is not one-size-fits-all. Different models serve different purposes. When aligned properly, they improve accuracy, reduce response time, and support better outcomes for security operations teams.

Four Types of AI in Security and Where They Belong

1. LLM Workflows: Language Assistants, Not Operators

Large Language Models (LLMs) are ideal for working with unstructured text. In cybersecurity, they are often used to summarize threat reports, assist with documentation, or translate technical alerts into business language.

They do not act or decide. They support communication, which is critical when translating between analysts, leadership, and compliance teams.

Where they help:

• Incident report summaries
• Policy and playbook creation
• Security posture updates for stakeholders

LLMs improve efficiency and reduce time spent on documentation. They help analysts stay focused on what matters most.

2. RPA: Automation for Repeatable Tasks

Robotic Process Automation (RPA) is great for executing known, rule-based responses. If a certain event is detected, a predefined action is triggered.

It is efficient, consistent, and scalable. This makes it ideal for high-volume environments.

Where it helps:

• Disabling accounts after token misuse
• Quarantining endpoints based on alerts
• Automating responses to compliance triggers

RPA does not adapt or learn. It works best in environments where repeatable actions need to happen fast and without deviation.

3. AI Agents: Decision-Makers with Tools and Memory

AI Agents can access tools, use logic, and apply short-term memory. They are like junior analysts that work at machine speed, curating data around detections, building cases, and recommending action.

Where they help:

• Correlating data from multiple security tools
• Enriching detections with historical or behavioral context
• Creating triage packages for analyst review

AI Agents reduce investigation time and provide analysts with a clearer, more complete picture before action is taken.

4. Agentic AI: Orchestration Across Systems

Agentic AI is a system of coordinated agents that detect, prioritize, plan, and act based on the environment, tools, and workflows available. This is the most advanced and adaptive layer of AI in security operations today.

Where it helps:

• Managing detection-to-response workflows
• Adapting to different customer tool stacks
• Executing tailored response actions at scale

Agentic AI enables security teams to move from reactive triage to proactive orchestration, using real-time insight to reduce dwell time and improve decision-making.

Visibility First: Where AI Gets Its Power

Before AI can act, it needs to see.

At UncommonX, everything starts with agentless discovery. Our patented approach identifies and profiles every asset in your environment, across cloud, on-premises, and hybrid infrastructure.

This is not just an asset list. It is live intelligence.

Our AI fingerprinting automatically classifies devices, builds behavioral baselines, and provides the context needed for AI to make relevant and accurate decisions.

Without visibility, even the smartest AI is guessing. With it, AI becomes a force multiplier for your team.

Smarter Prioritization Through Relative Risk Rating (R3)

Every organization is different. A vulnerability that is critical in one environment might be irrelevant in another.

That is why we use an adaptive risk scoring model. Our AI-enhanced R3 framework factors in asset role, behavioral history, threat intelligence, and changes in your environment to assign relative risk scores.

This allows you to:

• Focus on what truly matters
• Reduce alert fatigue
• Prioritize actions based on business impact

Smarter prioritization supports faster, more confident decision-making across your security team.

Real-Time Context, Real Results

Once the system has visibility and a sense of relative risk, AI can act with purpose.

• LLMs assist with writing, summarizing, and translating insights\
• AI Agents perform triage and present context
• RPA handles fast, deterministic responses
• Agentic AI orchestrates actions across systems
  
  

These tools are not siloed. They work together, using real-time data and continuous learning to support adaptive and efficient cybersecurity workflows.

Why Analysts Still Matter and Always Will

AI can enhance analysis, improve accuracy, and automate routine tasks. But it does not replace human judgment, creativity, or experience.

Analysts bring context that AI cannot replicate. They understand the nuances of business impact, the complexity of organizational priorities, and the gray areas in risk.

In the UncommonX model, the analyst:

• Makes the final call
• Oversees edge cases and exceptions
• Trains AI systems through feedback

From Insight to Impact

AI is not a silver bullet. It is a set of tools. Each has a purpose. Each works best when applied with visibility, context, and clarity.

The organizations seeing real results from AI are not just deploying it everywhere. They are matching the right models to the right problems, starting with discovery, and keeping people in the loop. At UncommonX, that is the model we follow. Visibility first. Context always. Analysts at the center.

In future posts, we will explore how Agentic AI is changing the response lifecycle and helping teams move from reactive workflows to intelligent, real-time action. If this aligns with the challenges you are facing, we would love to connect. Contact us today.