Using Your Company's LLM for Security Questionnaires
A look at how to leverage your company's own Large Language Models (LLMs) to efficiently and accurately respond to security questionnaires.
Using Your Company's LLM for Security Questionnaires (The Decision Most Companies Get Wrong)
Building your own LLM for security questionnaires is the modern equivalent of running your own email server in 1998. Technically possible, strategically questionable, economically painful.
Yet 67% of enterprises plan custom LLM implementations by 2025. Most will discover what early internet companies learned the hard way: building infrastructure is expensive, maintaining it is harder, and the competitive advantage rarely justifies the cost.
But some companies should build their own LLMs. The challenge is honest evaluation of whether you're solving a real problem or just avoiding vendor dependency.
The Build vs. Buy Delusion
Every CTO who lived through the dot-com era remembers companies that built custom web servers instead of using Apache, custom databases instead of Oracle, and custom email systems instead of Exchange. Most failed spectacularly.
The pattern repeats: Technical teams prefer building to buying. Executive teams prefer control to efficiency. The result is expensive custom solutions that work worse than commodity alternatives.
The LLM version: Companies spend millions building custom language models that perform worse than OpenAI's API calls while claiming they need "data sovereignty" for security questionnaires that contain no proprietary information.
The Microsoft vs. Netscape Lesson
Microsoft won the browser wars not through technical superiority, but through distribution and integration advantages. Netscape had better technology but couldn't compete with free bundling and platform integration.
Custom LLMs face the same competitive dynamic against cloud providers. Your model might work better for your specific use case, but can it compete with OpenAI's pace of improvement, Google's infrastructure scale, or Anthropic's safety research?
The honest answer for most companies: No.
When Custom LLMs Actually Make Sense
Data Control Requirements That Matter
Real data sovereignty: Government contractors, defense companies, and healthcare organizations with genuine regulatory constraints on data processing location and vendor access.
Fake data sovereignty: Companies that claim they can't use cloud AI services for security questionnaires while using Gmail, Slack, and Salesforce for daily operations.
The test: If you can't use cloud services for your LLM training data, you probably can't use them for anything else either. Most companies that claim data sovereignty requirements are really expressing vendor dependency anxiety.
Volume Economics That Actually Work
Breaking even: Custom LLMs become cost-effective at approximately $100,000+ in annual API costs for equivalent cloud services. This typically requires processing 500+ complex questionnaires annually.
The AOL moment: AOL thrived when internet access was expensive and complex. When internet access became commoditized, AOL's value proposition collapsed. Custom LLMs face similar commoditization pressure.
Volume reality check: Most companies overestimate their questionnaire volume and underestimate cloud pricing. Run the actual numbers with realistic usage projections, not optimistic growth scenarios.
Genuine Competitive Advantages
Proprietary security frameworks: Companies with unique compliance requirements or industry-specific security standards that aren't well-represented in public training data.
Integration complexity: Organizations with complex internal systems that require sophisticated AI integration beyond what cloud APIs can provide.
The Amazon parallel: Amazon built custom infrastructure because their requirements exceeded what vendors could provide. Most companies aren't Amazon.
The Implementation Reality Nobody Discusses
The True Cost Structure
Initial development: $500,000-2,000,000 for sophisticated custom LLM implementation, including model training, infrastructure setup, and integration development.
Operational overhead: $200,000-500,000 annually for model maintenance, infrastructure management, security monitoring, and performance optimization.
Opportunity cost: Technical resources spent on LLM development instead of core business problems. This hidden cost often exceeds direct implementation costs.
The Yahoo Portal Problem
Yahoo tried to build everything internally instead of integrating best-of-breed solutions. This worked temporarily but created technical debt that made adapting to market changes increasingly difficult.
Custom LLM risk: Building comprehensive AI infrastructure creates the same technical debt. Updates, security patches, and performance improvements become internal responsibilities rather than vendor problems.
The Skills Gap Reality
Required expertise: ML engineers, data scientists, infrastructure specialists, and AI safety researchers. These skills are expensive and difficult to retain.
Market dynamics: Companies compete for AI talent against Google, OpenAI, and well-funded startups. Most enterprises can't offer competitive compensation or interesting technical challenges.
The retention problem: AI engineers prefer working on cutting-edge research problems, not maintaining internal questionnaire automation systems.
The Technical Architecture That Works
Hybrid Approaches That Avoid Vendor Lock-in
Model fine-tuning: Use foundation models (GPT-4, Claude, Llama) as base layers and fine-tune with your specific security content. This provides customization without full custom development.
Data pipeline ownership: Control data processing and response generation while using cloud inference. This addresses some sovereignty concerns without infrastructure complexity.
The Netscape strategy: Build differentiated user experience on top of commodity infrastructure rather than rebuilding the entire stack.
Integration Patterns That Scale
API-first architecture: Design systems that can work with multiple LLM providers rather than depending on single vendors or custom models.
Model abstraction layers: Separate business logic from model implementation to enable switching between providers or custom models without application changes.
Fallback strategies: Combine custom models with cloud backup systems to ensure reliability without complete vendor dependency.
The Data Strategy That Actually Matters
Knowledge Base Quality vs. Model Sophistication
The insight: Model sophistication matters less than training data quality. GPT-4 with excellent security content outperforms custom models with mediocre training data.
Resource allocation: Spend 80% of effort on content curation and 20% on model customization rather than the reverse.
The content advantage: High-quality, well-organized security documentation provides competitive advantage regardless of which model processes it.
Information Architecture That Enables AI
Structured content: Convert policies, procedures, and compliance documentation into formats that models can process effectively.
Contextual relationships: Build knowledge graphs that connect related security concepts rather than isolated question-answer pairs.
Version control: Maintain authoritative sources for security information that update automatically across all AI systems.
The parallel: Successful websites in the 1990s succeeded through information architecture, not just visual design. AI systems require similar structural thinking.
The Vendor Landscape Reality
Cloud Provider Advantages
OpenAI, Anthropic, Google: Massive research budgets, continuous model improvements, and infrastructure scale that individual companies can't match.
Cost trajectory: Cloud AI costs decrease over time while custom infrastructure costs increase due to maintenance and upgrade requirements.
Feature velocity: New capabilities (multimodal processing, improved reasoning, better safety) available immediately rather than requiring internal development.
The Commoditization Timeline
Current state: Cloud providers offer differentiated capabilities that justify premium pricing for many use cases.
18-month projection: Basic LLM capabilities become commoditized. Advanced features (reasoning, multimodal processing, domain expertise) remain differentiated.
Strategic implication: Companies building custom LLMs today may find their investment obsolete before achieving ROI.
The Economics Most Companies Ignore
Total Cost of Ownership Analysis
Custom LLM: $2-5 million over 3 years including development, infrastructure, maintenance, and opportunity costs.
Cloud services: $300,000-1,000,000 over 3 years for equivalent functionality, depending on usage volume.
The break-even reality: Custom development rarely achieves cost parity with cloud services until reaching massive scale.
Competitive Advantage Sustainability
Technical differentiation lifespan: 12-18 months before cloud providers incorporate similar capabilities.
Process differentiation sustainability: Indefinite advantage through superior content organization and workflow integration.
The insight: Competitive advantage comes from execution, not technology. Focus resources on content quality and process optimization rather than model development.
The Decision Framework That Works
Question 1: Do You Have Genuine Data Constraints?
Real constraints: Regulatory requirements that legally prohibit cloud data processing.
Imaginary constraints: Policy preferences that assume cloud services are inherently insecure.
The test: If you use cloud services for email, file storage, or communication, you probably don't have genuine AI data constraints.
Question 2: Can You Afford the Total Cost?
Honest calculation: Include development, infrastructure, maintenance, and opportunity costs over 3-5 years.
Market comparison: Compare against current cloud pricing with projected cost decreases and capability improvements.
The reality check: Most companies that claim they can afford custom LLMs haven't calculated actual costs correctly.
Question 3: Do You Have Sustainable Competitive Advantage?
Differentiated requirements: Unique use cases that cloud providers can't address effectively.
Execution capabilities: Teams that can build, maintain, and improve AI systems faster than vendor alternatives.
Market position: Business models that justify custom infrastructure investment through clear revenue advantages.
The Hybrid Strategy That Actually Works
Best-of-Both Approaches
Fine-tuned cloud models: Customize foundation models with your content while leveraging cloud infrastructure and ongoing improvements.
Data pipeline control: Process and prepare data internally while using cloud services for inference and generation.
Multi-vendor strategies: Avoid lock-in by designing systems that work with multiple providers and can incorporate custom models when justified.
The Infrastructure Evolution Path
Phase 1: Use cloud services to validate use cases and understand requirements.
Phase 2: Implement data processing and content management systems that could support custom models.
Phase 3: Evaluate custom model development based on actual usage data and changing vendor landscape.
The Amazon lesson: Amazon started with existing infrastructure and built custom capabilities when scale and requirements justified it. Follow the same progression.
The Future Reality
Market Evolution Pressure
Commoditization acceleration: Basic LLM capabilities will become free or nearly free within 2-3 years.
Specialization advantage: Value will shift to domain expertise, integration quality, and workflow optimization.
Platform consolidation: Major cloud providers will acquire specialized vendors, reducing market options while improving integration.
Strategic Positioning
Winners: Companies that build sustainable advantages through content quality, process excellence, and customer experience rather than model technology.
Losers: Companies that invest heavily in custom infrastructure that becomes obsolete before generating returns.
The dot-com parallel: Companies that focused on customer value rather than technical complexity survived the bubble. The same principle applies to AI investments.
The Bottom Line
Building custom LLMs for security questionnaires makes sense for approximately 5% of enterprise companies. The other 95% would achieve better results faster with less risk using cloud services and focusing on content quality and process optimization.
The honest evaluation: If you're considering custom LLM development, you're probably solving the wrong problem. Most companies that think they need custom AI actually need better content organization and workflow design.
The exception: If you have genuine regulatory constraints, massive scale requirements, or truly unique use cases, custom development may be justified. But validate these assumptions honestly rather than accepting them as given.
The choice: Like the choice between building custom web infrastructure or using cloud services in 2010, this decision will define your organization's relationship with AI for the next decade. Choose based on realistic assessments of costs, capabilities, and competitive advantage rather than vendor dependency anxiety or technical preferences.
The companies that make this choice correctly will build sustainable AI capabilities. The ones that don't will spend years and millions of dollars learning expensive lessons about the difference between what's technically possible and what's strategically wise.