The General Data Protection Regulation (GDPR) of the European Union and the California Privacy Rights Act (CPRA) represent landmark regulations designed to protect consumer data privacy. While GDPR became enforceable in May 2018, CPRA came into effect in January 2023, building on its predecessor, the California Consumer Privacy Act (CCPA). Both laws aim to empower individuals with greater control over their personal data while imposing rigorous obligations on businesses.

In an era where data breaches, privacy violations, and regulatory fines dominate headlines, the need for robust privacy engineering has never been more critical. Yet, despite its growing prominence, privacy engineering is failing to meet the demands of businesses and consumers alike. To understand why, let’s explore what privacy engineering is, the challenges it faces, why its current state is insufficient, and the transformative shift needed to make it truly effective.

In today’s business ecosystem, data exchanges are critical for operations. From APIs to FTP connections, Electronic Data Interchange (EDI), and Virtual Desktop Infrastructure (VDI), data transfers happen continually, each using specific protocols and requiring authentication to ensure security and confidentiality. These interactions rely on a vast array of identities, keys, and credentials that need consistent management and periodic rotation to maintain security.

API calls are the backbone of modern software, enabling applications to communicate and share data seamlessly. However, with this integration comes the challenge of understanding and managing the identities used in API calls. These identities, often tied to authentication and authorization mechanisms, are crucial for determining what data is accessed and exchanged.

Federal Risk and Authorization Management Program (FedRAMP) and State Risk and Authorization Management Program (StateRAMP) are pivotal frameworks for securing cloud services used by federal and state governments, respectively. These programs mandate stringent security protocols, emphasizing the need for organizations to manage and disclose third-party involvement in delivering software services to the government.

With the release of iOS 18, Apple has continued to expand its integration of AI technologies, positioning the iPhone as a powerful personal assistant capable of smart recommendations, advanced text and image analysis, and even predictive suggestions. Leveraging on-device machine learning, iOS 18’s AI features tap into user data to provide a personalized experience.

The legal department plays a crucial role in enhancing enterprise security profiles. Historically, legal and cybersecurity departments have been siloed from one another in organizations both large and small. With security now a concern at the Board level, legal’s role in enterprise risk management – advising on threats and potential liability – must include the impact of data security threats.

Prompt injection attacks exploit vulnerabilities in natural language processing (NLP) models by manipulating the input to influence the model’s behavior. Common prompt injection attack patterns include: 1. Direct Command Injection: Crafting inputs that directly give the model a command, attempting to hijack the intended instruction. 2. Instruction Reversal: Adding instructions that tell the model to ignore or reverse previous commands. 3.

Enterprise applications, whether on-premise or in the cloud, access LLMs via APIs hosted in public clouds. These applications might be used for content generation, summarization, data analysis, or a plethora of other tasks. Riscosity’s data flow posture management platform protects sensitive data that would otherwise be accessible to LLM integrations.