Balancing AI, LLMs, and Cybersecurity in Integrity and Asset Management
Artificial Intelligence (AI) and Large Language Models (LLMs) are increasingly influencing how industries manage assets, perform risk assessments, and produce reports. From energy and infrastructure to environmental services and manufacturing, organizations are exploring how these tools can enhance workflows and decision-making.
However, alongside the potential benefits of AI comes a growing need to carefully evaluate cybersecurity, data privacy, and regulatory compliance—especially when working with sensitive operational information.
What Are LLMs, and Why Does Deployment Method Matter?
Large Language Models (LLMs) are advanced AI systems designed to generate human-like text based on the data they are trained on and the prompts they receive. Public examples like ChatGPT have made these tools widely recognizable.
While LLMs can assist in drafting documents, summarizing data, and supporting knowledge management, how they are deployed matters greatly—especially for organizations managing critical or sensitive information.
Local vs. Cloud LLM Deployment:
• Cloud-hosted LLMs process data externally, often in shared environments owned by third parties. This introduces potential risks related to data leaks, loss of control, and compliance challenges.
• Local LLMs, run on air-gapped systems disconnected from the internet, provide organizations full control over data and AI model use. Sensitive information stays onsite, reducing exposure to external cyber threats.
Cybersecurity and Regulatory Considerations
Many industries manage data that, while not classified, is still highly sensitive. Examples include:
• Inspection reports
• Asset integrity records
• Pipeline mapping and geographic information
• Maintenance histories
• Proprietary engineering designs
This type of information may fall under Controlled Unclassified Information (CUI) frameworks or require protection under cybersecurity standards like NIST SP 800-171. Mishandling such data—especially by sending it through external AI tools—could violate internal policies or regulatory requirements.
Key Resources:
• CUI Program – National Archives (NARA)
• NIST SP 800-171 – Protecting CUI in Nonfederal Systems
Regulatory compliance aside, there are also growing concerns about AI-driven phishing, data poisoning, and unauthorized access through cloud APIs—all risks that multiply when AI tools connect to the broader internet.
Where LLMs Add Value (and Where Caution is Warranted)
When thoughtfully applied, LLMs—especially local models—can support:
• Drafting repetitive report sections (executive summaries, background)
• Summarizing internal reports or reference materials
• Creating consistent documentation templates
• Aiding knowledge capture as staff changes over time
However, it’s important to recognize the limitations of these tools:
• LLMs do not perform engineering analysis or risk modeling
• They should not be relied on for final decision-making in integrity assessments
• AI-generated content requires review and validation by qualified personnel
The value lies in using LLMs to support integrity and asset management workflows—not replace expertise.
The Shift Toward Local, Air-Gapped AI Systems
There is growing recognition that air-gapped, locally deployed AI models offer the best balance of efficiency and security for organizations handling sensitive data. By eliminating reliance on external servers and internet-based processing, these systems reduce the cybersecurity attack surface and simplify regulatory compliance.
Local deployments give organizations:
• Data sovereignty
• Full control over AI use
• Better alignment with cybersecurity frameworks and CUI requirements
For industries where operational safety, regulatory compliance, and data protection are non-negotiable, this approach deserves serious consideration.
Final Thoughts
The intersection of AI, asset integrity, and cybersecurity is complex—and evolving quickly. While AI tools like LLMs hold real promise for improving reporting efficiency and knowledge management, they must be deployed with a clear understanding of the risks, especially around data security and regulatory obligations.
Organizations considering AI or LLMs for integrity or asset management programs would benefit from carefully evaluating deployment models, compliance needs, and cybersecurity implications. Those unsure where to begin may find it helpful to consult with professionals familiar with both asset integrity and secure AI applications.
This article is intended as a general guide to emerging considerations around AI, LLMs, and cybersecurity in asset management. Organizations are encouraged to assess their specific operational and regulatory environments when exploring these technologies.
