Linguistic Optimization Procedure for AI Security

Linguistic Optimization to Improve AI Security: Patentable Procedure Based on Agglutinative Languages Applied to the AIsecTest System

The security of artificial intelligence (AI) has become a central concern in both research and the technology industry. As AI systems become more sophisticated, the risks associated with their autonomous decision-making capabilities increase—making the detection of internal vulnerabilities ever more complex. The abstract and opaque nature of advanced models makes introspection—a system's ability to analyze, understand, and validate its own behavior—a major challenge.

To overcome these obstacles, our research introduces an innovative methodology that enhances security evaluation by transforming introspective assessment questions into agglutinative languages, such as Finnish or Hungarian, where semantic clarity and token-based information density are significantly higher.

1. Problem Addressed

• Current AI models struggle to evaluate their own internal security.


• Traditional tests are constrained by the semantic ambiguity of inflected languages (e.g., Spanish, English).


• A method is needed to improve AI models' introspection to detect real vulnerabilities.

2. Our Solution

We have developed a technical procedure that converts security evaluation prompts into agglutinative languages in order to:

• Reduce ambiguity
  


• Maximize semantic information per token
  


• Stimulate deeper introspective responses from AI systems
  

This procedure is embedded in the AIsecTest framework, part of the CiberIA system.

3. What This Procedure Does

➡️ The procedure converts questions from an inflected language to an agglutinative one through six defined phases:

1. Semantic analysis
   


2. Morphological segmentation
   


3. Structural mapping
   


4. LLM adaptation
   


5. Introspective validation
   


6. Integration with the Ψ∑AISysIndex metric
   

⚠️ Note: This is not a standard translation—it is a semantic-technical optimization designed for AI.

4. Competitive Advantages

• ✅ Radical innovation in AI security through linguistic engineering
  


• ✅ Enhanced introspective accuracy over conventional questions
  


• ✅ Compatible with modern generative AIs (GPT, Claude, Mistral, etc.)
  


• ✅ Ready for integration into AI auditing, certification, and testing processes
  

5. Current Development Status

• Complete and operational protocol
  


• Glossaries and structures created in Finnish and Hungarian
  


• 100 validated introspection questions
  


• Ψ∑AISysIndex metric fully developed
  


• Procedure ready for international patent application
  


• Available for licensing, joint exploitation, or partial transfer
  

6. Industrial Applications

• AI security audits
  


• Compliance and regulatory testing
  


• Ethical verification platforms
  


• Cyberwarfare and offensive/defensive AI systems
  


• Autonomous systems in healthcare, finance, and energy sectors
  

7. Patentability Claim

This procedure meets the criteria for patent protection:

• 🔐 Novelty → No known prior art
  


• ⚙️ Inventive step → Non-obvious to field experts
  


• 🏭 Industrial applicability → High utility and scalability
  


• 📦 Protectable as a procedure, method of use, and automatic implementation
  

8. What We Are Looking For

• 
  Patent consultants for international filing (EPO/WIPO)
  


• 
  Industrial partners to integrate the method into real-world products
  


• 
  Investors to support scaling and commercialization
  

🎯 Benefits offered: early access, licensing participation, commercialization rights, and co-development opportunities.

9. Contact

Jordi Garcia Castillon


AI & Cybersecurity Expert


📧 [email protected]


🔗 Simplified Technical Report: https://zenodo.org/records/16738122