Automating Cloud Security and Forensics Through a Secure-by-Design Generative AI Framework
Dalal Alharthi, Ivan Roberto Kawaminami Garcia

TL;DR
This paper introduces a secure-by-design generative AI framework that enhances cloud security and forensic investigations by defending against adversarial prompts and streamlining forensic processes.
Contribution
It presents an integrated framework combining PromptShield and CIAF to improve LLM security and forensic accuracy in cloud environments.
Findings
PromptShield achieves over 93% precision, recall, and F1 scores under attack.
CIAF improves ransomware detection accuracy using ontology-based reasoning.
The framework enhances automation, interpretability, and trustworthiness of cloud forensics.
Abstract
As cloud environments become increasingly complex, cybersecurity and forensic investigations must evolve to meet emerging threats. Large Language Models (LLMs) have shown promise in automating log analysis and reasoning tasks, yet they remain vulnerable to prompt injection attacks and lack forensic rigor. To address these dual challenges, we propose a unified, secure-by-design GenAI framework that integrates PromptShield and the Cloud Investigation Automation Framework (CIAF). PromptShield proactively defends LLMs against adversarial prompts using ontology-driven validation that standardizes user inputs and mitigates manipulation. CIAF streamlines cloud forensic investigations through structured, ontology-based reasoning across all six phases of the forensic process. We evaluate our system on real-world datasets from AWS and Microsoft Azure, demonstrating substantial improvements in…
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