Automating Cloud Security and Forensics Through a Secure-by-Design Generative AI Framework

Abstract: As cloud environments become increasingly complex, cybersecurity and forensic investigations must evolve to meet emerging threats. 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 both LLM security and forensic accuracy. Experimental results show PromptShield boosts classification performance under attack conditions, achieving precision, recall, and F1 scores above 93%, while CIAF enhances ransomware detection accuracy in cloud logs using Likert-transformed performance features. Our integrated framework advances the automation, interpretability, and trustworthiness of cloud forensics and LLM-based systems, offering a scalable foundation for real-time, AI-driven incident response across diverse cloud infrastructures.

• The paper introduces a dual-layered framework that combines adversarially robust prompt validation with automated forensic reasoning to secure cloud security workflows.
• It employs PromptShield and CIAF to enforce ontology-driven input transformation and standardized forensic processes, enhancing detection and evidence integrity.
• Experimental results on AWS and Azure demonstrate macro-averaged precision, recall, and F1-scores above 0.93, evidencing robust performance under adversarial conditions.

Secure-by-Design Automation of Cloud Security and Forensics via Generative AI

Background and Motivation

LLMs are increasingly deployed for automation in cybersecurity applications, particularly in digital forensics, log analysis, and incident response. Despite their capabilities for complex reasoning and data synthesis, two unresolved limitations impede their adoption for mission-critical cloud forensic workflows: susceptibility to prompt injection attacks and the absence of structured, forensic-grade input validation. Existing LLM-powered tools remain vulnerable to adversarial manipulation, which may degrade detection, undermine evidence integrity, or even facilitate evasive malware operations. Concurrently, forensic pipelines often rely on manual, labor-intensive processes for log collection, event reconstruction, and causality inference, which substantially reduce scalability and timeliness.

"Automating Cloud Security and Forensics Through a Secure-by-Design Generative AI Framework" (2604.03912) proposes a unified architecture synthesizing adversarially robust prompt handling and automated forensic reasoning. The dual-layered framework integrates PromptShield—an ontology-driven prompt validation subsystem—and the Cloud Investigation Automation Framework (CIAF), which encodes forensic processes within semantically structured, LLM-driven workflows. The framework targets cloud-native environments and is empirically validated on AWS and Azure datasets, emphasizing classification accuracy, resilience to adversarial input, and the interpretability of forensic analytics.

Framework Architecture and Ontology-Driven Prompt Security

The system architecture encompasses two interdependent dimensions: secure input management for LLMs and automated, ontology-guided forensic investigation. Figure 1 illustrates the two-part design, where prompt injection vulnerabilities are addressed through the interposition of PromptShield in LLM-based dataflows. Part (a) visualizes the surface for adversarial prompt manipulation; part (b) delineates PromptShield’s mediation, employing ontology-based validation and transformation protocols to guarantee that only semantically consistent, expert-aligned prompts reach the LLM.

Figure 1: Prompt injection in baseline LLM architectures and the ontology-driven PromptShield pre-validation mechanism.

Central to PromptShield is a domain-tailored ontology (Figure 2) formalizing relationships among User Prompts, System Prompts, Model contexts, Attribute constraints, and functionally scoped actions. This specification enables deterministic prompt transformation, systematic rejection of ambiguous or adversarial inputs, and dynamic integration of evolving forensic templates. The ontology also supports prompt replacement and chaining, enabling prompt standardization in multi-agent LLM deployments and structured chain-of-thought procedures.

Figure 2: The PromptShield ontology connects prompts, model, attributes, and functions, ensuring robust validation and template instantiation.

The secure-by-design paradigm embodied in PromptShield aligns with contemporary adversarial robustness methodologies. By embedding ontological constraints at the prompt interface layer, the system transfers the defense point from post hoc response filtering to proactive input hardening. This mechanism prevents the propagation of injection-induced misclassification or semantic drift through downstream analytic and forensic stages.

Cloud Investigation Automation Framework (CIAF)

CIAF semantically automates all six classical phases of cloud forensics—from event and evidence identification through to result interpretation and presentation. The framework (Figure 3) tightly couples automated log acquisition, evidence extraction, analytic modeling, and interpretable reporting, utilizing LLMs as the principal agents of inference. Each phase is supported by ontology-driven templates that encode domain expertise, enforce precondition satisfaction, and systematically select and transform relevant forensic features.

Figure 3: End-to-end automation of cloud forensic phases, integrating PromptShield input validation with CIAF for structured, LLM-driven investigation.

CIAF’s integration with PromptShield ensures that all analytic workflows benefit from the same adversarial input guarantees. Expert-encoded causal links and feature dependencies empower both accurate anomaly classification and concise attack reconstruction, which is critical in the context of ransomware incidents and persistent threat campaigns prevalent in cloud systems.

Experimental Evaluation: Adversarial Robustness and Forensic Performance

The system is evaluated using confusion matrices across three primary scenarios: baseline prompt usage, prompt injection attack, and the PromptShield-enabled pipeline. Figure 4 presents confusion matrices for AWS event log classification under these scenarios. Numerical results demonstrate that PromptShield elevates macro-averaged precision, recall, and F1-score to >0.93 under adversarial conditions, compared with severe degradation when prompt injection is present and moderate outcomes with unvalidated prompts.

Figure 4: Confusion matrices: (a) baseline, (b) prompt injection, (c) PromptShield-enabled prompting against AWS logs.

The Azure case study illustrates ransomware detection effectiveness. Feature monitoring selects high-variance metrics (e.g., Working Set, Committed Bytes, Available Bytes), transformed into Likert-scaled representations to optimize LLM interpretability (Figure 5). The ransomware attack produces statistically significant and temporally distinct shifts in these features, which the ontology-coded analytic template exploits for precise classification.

Figure 5: Timeline of critical performance metrics during ransomware execution and system activity.

Classification under attack achieves 0.94 macro-precision/recall/F1-scores (see Figure 6 and table results), substantiating the capability to distinguish legitimate from ransomware-induced system behaviors with minimal false positives.

Figure 6: Confusion matrix for ransomware activity versus legitimate usage on Azure.

These results explicitly demonstrate that PromptShield’s adversarially robust prompt validation, in conjunction with ontology-driven analytic workflows, delivers both security and forensic fidelity. The use of inductive bias through expert-defined templates and causal feature selection not only counters adversarial threat vectors but also systematizes analysis for legal and retrospective contexts.

Theoretical and Practical Ramifications

The dual-layered, ontology-centric approach foregrounds structured template induction, causal validation, and semantic traceability as essential enablers for scalable AI-driven forensic pipelines. Semantically controlled prompt interfaces dramatically constrain the hypothesis space for LLMs, mitigating instruction ambiguity and reducing catastrophic reasoning failures documented in less structured LLM deployments. Such mechanisms inherently enhance model trustworthiness and auditability—two requisites for adoption in regulated forensic practice and legal evidence contexts.

Furthermore, the framework’s design is extensible: the ontology can be incrementally updated to accommodate emerging attack signatures and analytic requirements, enabling adaptation without full retraining or disruptive pipeline reconfiguration. This is critical for multi-cloud and hybrid environments where logs, attack patterns, and regulatory requirements exhibit high heterogeneity.

Future Directions

Research extensions include integrating reinforcement learning to dynamically adapt ontological constraints, function vector-based interpretability metrics for prompt transparency, and broader support for domain-agnostic and multi-modal forensic data. Open questions persist around the optimal trade-off between input constraint rigidity (for robustness) and expressive model flexibility (for generalization to unknown attack landscapes). There is also scope to harmonize PromptShield with collaborative LLM-driven incident response across distributed, multi-tenant cloud infrastructures and to quantify defense efficacy versus the evolving threat space, including novel prompt-based and cross-modal attacks.

Conclusion

The paper presents a comprehensive framework unifying secure-by-design, ontology-validated prompt handling and automated, interpretable cloud forensic investigation. Experimental results empirically establish that PromptShield and CIAF, when combined, deliver human-expert-level accuracy and robust adversarial immunity—even in high-noise, adaptive threat environments. The work substantiates the thesis that ontology-driven, semantically template-guided LLMs constitute a viable architecture for both next-generation cloud security and forensic automation. This trajectory promises improved resilience, transparency, and reliability for AI-centric security operations within the rapidly evolving cloud threat landscape.