Engineering Secure Enterprise Autonomy
Keywords:
Generative AI, Autonomous Systems, Enterprise Security, Risk Management, Regulatory Compliance, Data Security, Model Governance, Auditability, Decision Systems, Threat Modeling, Security Design, Language Models, Data Provisioning, Model Utilization, Operational Optimization, Policy Alignment, Enterprise Transformation, Security Frameworks, AI Governance, Intelligent Systems.Abstract
An integrated framework for secure transformation and operational optimization enables enterprises to harness Generative Artificial Intelligence (GAI) alongside autonomous systems while managing security and regulatory compliance. Key development goals include limiting risk exposure incurred by data provision, model utilization, and decision-making across generative AI applications; promoting security by design in language model deployment; aligning enterprise policies with relevant laws, industry standards, and certification requirements; ensuring comprehensive auditability of data, models, and decisions; and formally incorporating GAI capabilities within autonomous decision systems. The approach is evaluated through evidence from two application scenarios. Capabilities for language-led operations, integrating all requisite data, and completing digital tasks while meeting security requirements are confirmed. Security-aware autonomous decisions seamlessly integrated within enterprise risk management frameworks are also demonstrated. Close examination of the governing integration architecture substantiates the framework’s contribution and adaptability to other areas requiring intelligent enterprise transformation.
The recent emergence of GAI in a variety of enterprise applications presents significant opportunities for operational excellence. At the same time, a distinct shift in the profile of enterprise GAI applications since late 2022 imposes urgent new security requirements that must be addressed through appropriate risk assessments, threat modeling, and security-by-design deployment of language models. Enterprises are now increasingly exposed to advanced persistent threats and malicious actors with novel capabilities. Data may need to be supplied to language models to meet specific objectives, such as translating proprietary information or language-driven operational excellence. In this context, the role of GAI capabilities in any autonomous decision system must be well defined and the associated risks minimized to facilitate safe exploitation.
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