Search for: All records

Regulatory documents are complex and lengthy, making full compliance a challenging task for businesses. Similarly, privacy policies provided by vendors frequently fall short of the necessary legal standards due to insufficient detail. To address these issues, we propose a solution that leverages a Large Language Model (LLM) in combination with Semantic Web technology. This approach aims to clarify regulatory requirements and ensure that organizations’ privacy policies align with the relevant legal frameworks, ultimately simplifying the compliance process, reducing privacy risks, and improving efficiency. In this paper, we introduce a novel tool, the Privacy Policy Compliance Verification Knowledge Graph, referred to as PrivComp-KG. PrivComp-KG is designed to efficiently store and retrieve comprehensive information related to privacy policies, regulatory frameworks, and domain-specific legal knowledge. By utilizing LLM and Retrieval Augmented Generation (RAG), we can accurately identify relevant sections in privacy policies and map them to the corresponding regulatory rules. Our LLM-based retrieval system has demonstrated a high level of accuracy, achieving a correctness score of 0.9, outperforming other models in privacy policy analysis. The extracted information from individual privacy policies is then integrated into the PrivComp-KG. By combining this data with contextual domain knowledge and regulatory rules, PrivComp-KG can be queried to assess each vendor’s compliance with applicable regulations. We demonstrate the practical utility of PrivComp-KG by verifying the compliance of privacy policies across various organizations. This approach not only helps policy writers better understand legal requirements but also enables them to identify gaps in existing policies and update them in response to evolving regulations. 

Big Data empowers the farming community with the information needed to optimize resource usage, increase productivity, and enhance the sustainability of agricultural practices. The use of Big Data in farming requires the collection and analysis of data from various sources such as sensors, satellites, and farmer surveys. While Big Data can provide the farming community with valuable insights and improve efficiency, there is significant concern regarding the security of this data as well as the privacy of the participants. Privacy regulations, such as the European Union’s General Data Protection Regulation (GDPR), the EU Code of Conduct on agricultural data sharing by contractual agreement, and the proposed EU AI law, have been created to address the issue of data privacy and provide specific guidelines on when and how data can be shared between organizations. To make confidential agricultural data widely available for Big Data analysis without violating the privacy of the data subjects, we consider privacy-preserving methods of data sharing in agriculture. Synthetic data that retains the statistical properties of the original data but does not include actual individuals’ information provides a suitable alternative to sharing sensitive datasets. Deep learning-based synthetic data generation has been proposed for privacy-preserving data sharing. However, there is a lack of compliance with documented data privacy policies in such privacy-preserving efforts. In this study, we propose a novel framework for enforcing privacy policy rules in privacy-preserving data generation algorithms. We explore several available agricultural codes of conduct, extract knowledge related to the privacy constraints in data, and use the extracted knowledge to define privacy bounds in a privacy-preserving generative model. We use our framework to generate synthetic agricultural data and present experimental results that demonstrate the utility of the synthetic dataset in downstream tasks. We also show that our framework can evade potential threats, such as re-identification and linkage issues, and secure data based on applicable regulatory policy rules. 

A key challenge faced by small and medium-sized business entities is securely managing software updates and changes. Specifically, with rapidly evolving cybersecurity threats, changes/updates/patches to software systems are necessary to stay ahead of emerging threats and are often mandated by regulators or statutory authorities to counter these. However, security patches/updates require stress testing before they can be released in the production system. Stress testing in production environments is risky and poses security threats. Large businesses usually have a non-production environment where such changes can be made and tested before being released into production. Smaller businesses do not have such facilities. In this work, we show how “digital twins”, especially for a mix of IT and IoT environments, can be created on the cloud. These digital twins act as a non-production environment where changes can be applied, and the system can be securely tested before patch release. Additionally, the non-production digital twin can be used to collect system data and run stress tests on the environment, both manually and automatically. In this paper, we show how using a small sample of real data/interactions, Generative Artificial Intelligence (AI) models can be used to generate testing scenarios to check for points of failure. 

Security monitoring is crucial for maintaining a strong IT infrastructure by protecting against emerging threats, identifying vulnerabilities, and detecting potential points of failure. It involves deploying advanced tools to continuously monitor networks, systems, and configurations. However, organizations face challenges in adapting modern techniques like Machine Learning (ML) due to privacy and security risks associated with sharing internal data. Compliance with regulations like GDPR further complicates data sharing. To promote external knowledge sharing, a secure and privacy-preserving method for organizations to share data is necessary. Privacy-preserving data generation involves creating new data that maintains privacy while preserving key characteristics and properties of the original data so that it is still useful in creating downstream models of attacks. Generative models, such as Generative Adversarial Networks (GAN), have been proposed as a solution for privacy preserving synthetic data generation. However, standard GANs are limited in their capabilities to generate realistic system data. System data have inherent constraints, e.g., the list of legitimate I.P. addresses and port numbers are limited, and protocols dictate a valid sequence of network events. Standard generative models do not account for such constraints and do not utilize domain knowledge in their generation process. Additionally, they are limited by the attribute values present in the training data. This poses a major privacy risk, as sensitive discrete attribute values are repeated by GANs. To address these limitations, we propose a novel model for Knowledge Infused Privacy Preserving Data Generation. A privacy preserving Generative Adversarial Network (GAN) is trained on system data for generating synthetic datasets that can replace original data for downstream tasks while protecting sensitive data. Knowledge from domain-specific knowledge graphs is used to guide the data generation process, check for the validity of generated values, and enrich the dataset by diversifying the values of attributes. We specifically demonstrate this model by synthesizing network data captured by the network capture tool, Wireshark. We establish that the synthetic dataset holds up to the constraints of the network-specific datasets and can replace the original dataset in downstream tasks. 

Neurosymbolic artificial intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and the explicit, symbolic knowledge contained in knowledge graphs (KGs) to enhance explainability and safety in AI systems. This approach addresses a key criticism of current generation systems, namely, their inability to generate human-understandable explanations for their outcomes and ensure safe behaviors, especially in scenarios with unknown unknowns (e.g., cybersecurity, privacy). The integration of neural networks, which excel at exploring complex data spaces, and symbolic KGs representing domain knowledge, allows AI systems to reason, learn, and generalize in a manner understandable to experts. This article describes how applications in cybersecurity and privacy, two of the most demanding domains in terms of the need for AI to be explainable while being highly accurate in complex environments, can benefit from neurosymbolic AI. 

Cloud Legal documents, like Privacy Policies and Terms of Services (ToS), include key terms and rules that enable consumers to continuously monitor the performance of the cloud services used in their organization. To ensure high consumer confidence in the cloud service, it is necessary that these documents are clear and comprehensible to the average consumer. However, in practice, service providers often use legalese and ambiguous language in cloud legal documents resulting in consumers consenting or rejecting the terms without understanding the details. A measure capturing ambiguity in the texts of cloud service documents will enable consumers to decide if they understand what they are agreeing to, and deciding whether that service will meet their organizational requirements. It will also allow them to compare the service policies across various vendors. We have developed a novel model, ViCLOUD, that defines a scoring method based on linguistic cues to measure ambiguity in cloud legal documents and compare them to other peer websites. In this paper, we describe the ViCLOUD model in detail along with the validation results when applying it to 112 privacy policies and 108 Terms of Service documents of 115 cloud service vendors. The score distribution gives us a landscape of current trends in cloud services and a scale of comparison for new documentation. Our model will be very useful to organizations in making judicious decisions when selecting their cloud service. 

Machine learning algorithms used to detect attacks are limited by the fact that they cannot incorporate the back-ground knowledge that an analyst has. This limits their suitability in detecting new attacks. Reinforcement learning is different from traditional machine learning algorithms used in the cybersecurity domain. Compared to traditional ML algorithms, reinforcement learning does not need a mapping of the input-output space or a specific user-defined metric to compare data points. This is important for the cybersecurity domain, especially for malware detection and mitigation, as not all problems have a single, known, correct answer. Often, security researchers have to resort to guided trial and error to understand the presence of a malware and mitigate it.In this paper, we incorporate prior knowledge, represented as Cybersecurity Knowledge Graphs (CKGs), to guide the exploration of an RL algorithm to detect malware. CKGs capture semantic relationships between cyber-entities, including that mined from open source. Instead of trying out random guesses and observing the change in the environment, we aim to take the help of verified knowledge about cyber-attack to guide our reinforcement learning algorithm to effectively identify ways to detect the presence of malicious filenames so that they can be deleted to mitigate a cyber-attack. We show that such a guided system outperforms a base RL system in detecting malware.