The tech landscape is shifting — moving beyond simple process automation toward decision-making automation and deep customization tailored to specific business needs.
Large language models (LLMs) have been widely accessible for some time, yet their impact on the IT domain remains limited. While they’ve found success in areas like help desks and SaaS platforms, integration into security software remains a challenge.
One core reason is that LLMs are built for natural language understanding, not for interpreting technical artifacts like flow packets, logs, or structured alerts. These limitations make them poorly suited to security use cases without significant adaptation.
To unlock their true value in IT and security, businesses must focus on custom AI models specifically designed for their unique environments.
AI Model Efficiency
Enterprise AI is trending toward efficiency over brute force. Not every problem requires a full-scale LLM. Often, the smarter move is to distill larger models into leaner versions that focus on targeted use cases — balancing effectiveness with explainability, security, and cost control.
These smaller, more focused models can solve business-specific challenges without excessive compute usage, keeping operational costs down. That makes it easier to offer value without transferring high expenses to end users. As part of this process, organizations should prioritize the areas where customers experience the most friction.
Agentic AI and Context-Aware Automation
Agent-based AI has captured attention for good reason. The best AI is invisible — seamlessly integrated into workflows in ways users don’t even notice. This is possible when AI systems are context-aware and woven throughout applications.
When GenAI models with reasoning capabilities are combined with relevant data and context, they can drive significant business outcomes. Intelligent agents can collect live data from APIs and internal systems, sort and evaluate it, apply context, and then execute actions. In IT environments, multiple agents can specialize in various operational tasks, streamlining both decision-making and execution.
Pairing Right-Sized LLMs With Tailored IT Models
LLMs are not ideal for interpreting technical data on their own, but they can still offer value when integrated into a broader system of models tailored for IT workflows.
Security Use Case:
A multi-model approach works well in cybersecurity. For example, anomaly detection models can analyze email traffic to identify irregular behavior. A decision-tree system might then assess header data to contextualize those anomalies. From there, a small LLM could review the email content to understand its intent. Next, agents investigate embedded links, retrieve data, and feed it into phishing detection models that evaluate domain and site metadata. File attachments are tested in sandboxes, and ultimately, a decision tree concludes whether the email is suspicious.
IT Operations Use Case:
Custom AI workflows can also optimize cloud spending and server utilization. Machine learning models can forecast demand based on usage and real-time data, helping reduce overprovisioning while maintaining performance. If the system senses anomalies in usage trends, it could trigger alerts or suggestions using causal knowledge graphs and reasoning agents.
These agents work hand in hand with statistical models and adaptive decision trees, turning raw infrastructure data into actionable insights — shortening response times and enhancing operational efficiency.
Leveraging Open Source Models Without Sacrificing Privacy
A critical factor in deploying bespoke AI is maintaining control over your data. The best way to do this is by owning the entire tech stack — from infrastructure to the models themselves.
Using open source LLMs like DeepSeek, LLaMA, Qwen, or Mistral on self-hosted systems ensures that sensitive information remains secure within your environment. These models can be integrated with retrieval-augmented generation (RAG) to deliver responses that rely solely on your internal data, not public or anonymized third-party datasets.
With complete stack ownership, you gain full control over permissions, context, and access. This allows AI agents to function as secure components within your existing systems — assisting in IAM, PAM, search, and data governance without violating privacy policies.
It’s crucial that AI systems only interact with data within your protected infrastructure, that every access is auditable, and that permission structures are strictly enforced.
Key Takeaways
To harness the full potential of GenAI in IT, companies should adopt tailored, agent-powered solutions rather than general-purpose LLMs. As the industry shifts toward decision-centric automation and hyper-customized systems, it’s critical to optimize model size, reduce unnecessary compute costs, and ensure strict data privacy.
Custom-built AI for IT workflows provides the clearest path forward — offering control, efficiency, and meaningful business outcomes.
