Luke Probasco

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Meeting the UK’s Telecommunications Security Act with MongoDB

Emerging technologies like AI, IoT, and 5G have transformed the value that telecommunications companies provide the world. However, these new technologies also present new security challenges. As telcos continue to amass large amounts of sensitive data, they become an increasingly attractive target for cybercriminals — making both companies and countries vulnerable to cyberattacks. Fortunately, developers can protect user data which comes with strong security requirements on a developer data platform. By offering features to meet stringent requirements with robust operational and security controls, telcos can protect their customers’ private information. The UK Telecommunications Security Act Amid growing concerns about the vulnerability of telecom infrastructure, and its increasing digital dependency, the UK Telecommunications (Security) Act (TSA) was enacted on November 17, 2021. It was designed to bolster the security and resilience of the UK’s telecommunications networks. The TSA mandates that telecom operators implement rigorous security measures such as end-to-end encryption as well as identity and access management to protect their networks from a broad spectrum of threats, ensuring the integrity and continuity of critical communication services. The act allows the government to compel telecom providers to meet specific security directives. The United Kingdom’s Office of Communications (Ofcom) is a regulatory body responsible for overseeing compliance, conducting inspections, and enforcing penalties on operators that fail to meet the standards. The comprehensive code of practice included in the act offers detailed guidance on the security measures that should be implemented, covering risk management, network architecture, incident response, and supply chain security. The TSA tiering system The TSA establishes a framework for ensuring the security of public electronic communications networks and services. It categorizes telecoms providers into different tiers, with specific security obligations for each tier. The Act outlines three main tiers: Tier 1: These are the largest and most critical providers. They have the most extensive obligations due to their significant role in the UK's telecoms infrastructure. Tier 1 providers must comply with the full set of security measures outlined in the Act. Tier 2: These providers have a considerable role in the telecoms network but are not as critical as Tier 1 providers. They have a reduced set of obligations compared to Tier 1 but still need to meet substantial security requirements. Tier 3: These are smaller providers with a limited impact on the overall telecoms infrastructure. Their obligations are lighter compared to Tiers 1 and 2, reflecting their smaller size and impact. The specific obligations for each tier include measures related to network security, incident reporting, and supply chain security. The aim is to ensure a proportional approach to securing the telecoms infrastructure, with the highest standards applied to the most critical providers. Non-compliance may result in fines Under the TSA, non-compliance with security obligations can result in substantial fines. The fines are designed to be significant enough to ensure compliance and deter breaches. The significance of the fines imposed under the TSA underscores the importance the UK government places on telecom security and the serious consequences of failing to meet the established standards. How MongoDB can help MongoDB offers built-in security controls for all your data—whether your databases are managed on-premises with MongoDB Enterprise Advanced or with MongoDB Atlas , our fully managed cloud service. MongoDB enables enterprise-grade security features and simplifies deploying and managing your databases. Encrypting sensitive data The TSA emphasizes securing telecom networks against cyber threats. While specific encryption requirements are not detailed, the focus is on robust security practices, including encryption to protect data integrity and confidentiality. Operators must implement measures that prevent unauthorized access and ensure data security throughout transmission and storage. Compliance may involve regular risk assessments and adopting state-of-the-art technologies to safeguard the network infrastructure. MongoDB data encryption offers robust features to protect your data while it’s in the network, being stored, in memory, in transit (network), at rest (storage), and in use (memory, logs). Customers can use automatic encryption of key data fields like personally identifiable information (PII) or any data deemed sensitive—ensuring data is encrypted through its use. Additionally, with our industry-first Queryable Encryption , MongoDB offers a fast, searchable encryption scheme that supports equality searches, with additional query types such as range, prefix, suffix, and substring planned for future releases. Authentication and Authorization The TSA contemplates stringent identity and access management requirements to enhance network security. Regular audits and reviews of access permissions should be designed to prevent unauthorized access and to quickly identify and respond to potential security breaches. These measures aim to protect the integrity and confidentiality of telecommunications infrastructure. MongoDB enables users to authenticate to their Atlas UI with their Atlas credentials or via single sign-on with their GitHub or Google accounts. Atlas also supports MFA with various options, including OTP authenticators, push notifications, FIDO2 (hardware security keys or biometrics), SMS, and e-mail. MongoDB Enterprise Advanced users can authenticate to the MongoDB database using mechanisms including SCRAM, x.509 certificates, LDAP, OIDC, and passwordless authentication with AWS-IAM. Auditing Under the TSA, providers must implement logging mechanisms to detect and respond to security incidents effectively. Logs should cover access to sensitive systems and data, including unsuccessful access attempts, and must be comprehensive, capturing sufficient detail to facilitate forensic investigations. Additionally, logs should be kept for a specified minimum period and to be protected against unauthorized access, tampering, and loss. MongoDB offers granular auditing that monitors actions in your MongoDB environment and is designed to prevent and detect any unauthorized access to data, including CRUD operations, encryption key management, authentication, role-based access controls, replication, and sharding cluster operations. Additionally, MongoDB’s Atlas Organization Activity Feed displays select events that occurred for a given Atlas organization, such as billing or access events. Likewise, the Atlas Project Activity Feed displays select events that occurred for a given Atlas project. Network security The TSA outlines several network security requirements to ensure the protection and resilience of telecommunications networks. These requirements encompass various aspects of network security, including risk management, protection measures, incident response, and compliance with standards and best practices. Atlas offers many options to securely access your data with dedicated clusters deployed in a unique virtual private cloud (VPC) to isolate your data and prevent inbound network access from the internet. You can also allow a one-way connection from your AWS, Azure, or Google Cloud VPC/VNet to Atlas Clusters via Private Endpoints . Additionally, you can enable peering between your MongoDB Atlas VPC or VNet to your own dedicated application tier VPN with the cloud provider of your choice or enable only specific network segments to connect to your Atlas clusters via the IP Access list . In summary, the UK TSA is a critical regulatory framework aimed at protecting the nation’s telecommunications infrastructure from cyber threats. For telecom companies, compliance isn’t just a legal obligation but a business imperative. Failure to comply can mean significant financial penalties, reputational harm, and long-term operational challenges, underscoring the importance of adopting robust security measures and maintaining continuous adherence to the Act’s requirements. Visit MongoDB’s Strong Security Defaults page for more information on protecting your data with strong security defaults on the MongoDB developer data platform, as well as how to meet stringent requirements with robust operational and security controls.

August 1, 2024

AI-Powered Media Personalization: MongoDB and Vector Search

In recent years, the media industry has grappled with a range of serious challenges, from adapting to digital platforms and on-demand consumption, to monetizing digital content, and competing with tech giants and new media upstarts. Economic pressures from declining sources of revenue like advertising, trust issues due to misinformation, and the difficulty of navigating regulatory environments have added to the complexities facing the industry. Additionally, keeping pace with technological advancements, ensuring cybersecurity, engaging audiences with personalized and interactive content, and addressing globalization issues all require significant innovation and investment to maintain content quality and relevance. In particular, a surge in digital content has saturated the media market, making it increasingly difficult to capture and retain audience attention. Furthermore, a decline in referral traffic—primarily from social media platforms and search engines—has put significant pressure on traditional media outlets. An industry survey from a sample of more than 300 digital leaders from more than 50 countries and territories shows that traffic to news sites from Facebook fell 48% in 2023, with traffic from X/Twitter declining by 27%. As a result, publishers are seeking ways to stabilize their user bases and to enhance engagement sustainably, with 77% looking to invest more in direct channels to deal with the loss of referrals. Enter artificial intelligence: generative AI-powered personalization has become a critical tool for driving the future of media channels. The approach we discuss here offers a roadmap for publishers navigating the shifting dynamics of news consumption and user engagement. Indeed, using AI for backend news automation ( 56% ) is considered the most important use of the technology by publishers. In this post, we’ll walk you through using MongoDB Atlas and Atlas Vector Search to transform how content is delivered to users. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. The shift in news consumption Today's audiences rarely rely on a single news source. Instead, they use multiple platforms to stay informed, a trend that's been driven by the rise of social media, video-based news formats, and skepticism towards traditional media due to the prevalence (or fear) of "fake news." This diversification in news sources presents a dilemma for publishers, who have come to depend on traffic from social media platforms like Facebook and Twitter. However, both platforms have started to deprioritize news content in favor of posts from individual creators and non-news content, leading to a sharp decline in media referrals. The key to retaining audiences lies in making content personalized and engaging. AI-powered personalization and recommendation systems are essential tools for achieving this. Content suggestions and personalization By drawing on user data, behavior analytics, and the multi-dimensional vectorization of media content, MongoDB Atlas and Atlas Vector Search can be applied to multiple AI use cases to revolutionize media channels and improve end-user experiences. By doing so, media organizations can suggest content that aligns more closely with individual preferences and past interactions. This not only enhances user engagement but also increases the likelihood of converting free users into paying subscribers. The essence of leveraging Atlas and Vector Search is to understand the user. By analyzing interactions and consumption patterns, the solution not only grasps what content resonates but also predicts what users are likely to engage with in the future. This insight allows for crafting a highly personalized content journey. The below image shows a reference architecture highlighting where MongoDB can be leveraged to achieve AI-powered personalization. To achieve this, you can integrate several advanced capabilities: Content suggestions and personalization: The solution can suggest content that aligns with individual preferences and past interactions. This not only enhances user engagement but also increases the likelihood of converting free users into paying subscribers. By integrating MongoDB's vector search to perform k-nearest neighbor (k-NN) searches , you can streamline and optimize how content is matched. Vectors are embedded directly in MongoDB documents, which has several advantages. For instance: No complexities of a polyglot persistence architecture. No need to extract, transform, and load (ETL) data between different database systems, which simplifies the data architecture and reduces overhead. MongoDB’s built-in scalability and resilience can support vector search operations more reliably. Organizations can scale their operations vertically or horizontally, even choosing to scale search nodes independently from operational database nodes, flexibly adapting to the specific load scenario. Content summarization and reformatting: In an age of information overload, this solution provides concise summaries and adapts content formats based on user preferences and device specifications. This tailored approach addresses the diverse consumption habits of users across different platforms. Keyword extraction: Essential information is drawn from content through advanced keyword extraction, enabling users to grasp key news dimensions quickly and enhancing the searchability of content within the platform. Keywords are fundamental to how content is indexed and found in search engines, and they significantly influence the SEO (search engine optimization) performance of digital content. In traditional publishing workflows, selecting these keywords can be a highly manual and labor-intensive task, requiring content creators to identify and incorporate relevant keywords meticulously. This process is not only time-consuming but also prone to human error, with significant keywords often overlooked or underutilized, which can diminish the content's visibility and engagement. With the help of the underlying LLM, the solution extracts keywords automatically and with high sophistication. Automatic creation of Insights and dossiers: The solution can automatically generate comprehensive insights and dossiers from multiple articles. This feature is particularly valuable for users interested in deep dives into specific topics or events, providing them with a rich, contextual experience. This capability leverages the power of one or more Large Language Models (LLMs) to generate natural language output, enhancing the richness and accessibility of information derived from across multiple source articles. This process is agnostic to the specific LLMs used, providing flexibility and adaptability to integrate with any leading language model that fits the publisher's requirements. Whether the publisher chooses to employ more widely recognized models (like OpenAI's GPT series) or other emerging technologies, our solution seamlessly incorporates these tools to synthesize and summarize vast amounts of data. Here’s a deeper look at how this works: Integration with multiple sources: The system pulls content from a variety of articles and data sources, retrieved with MongoDB Atlas Vector Search. Found items are then compiled into dossiers, which provide users with a detailed and contextual exploration of topics, curated to offer a narrative or analytical perspective that adds value beyond the original content. Customizable output: The output is highly customizable. Publishers can set parameters based on their audience’s preferences or specific project requirements. This includes adjusting the level of detail, the use of technical versus layman terms, and the inclusion of multimedia elements to complement the text. This feature significantly enhances user engagement by delivering highly personalized and context-rich content. It caters to users looking for quick summaries as well as those seeking in-depth analyses, thereby broadening the appeal of the platform and encouraging deeper interaction with the content. By using LLMs to automate these processes, publishers can maintain a high level of productivity and innovation in content creation, ensuring they remain at the cutting edge of media delivery. Future directions As media consumption habits continue to evolve, AI-powered personalization stands out as a vital tool for publishers. By using AI to deliver tailored content and to automate back end processes, publishers can address the decline in traditional referrals and build stronger, more direct relationships with their audiences. If you would like to learn more about AI-Powered Media Personalization, visit the following resources: AI-Powered Personalization to Drive Next-Generation Media Channels AI-Powered Innovation in Telecommunications and Media GitHub Repository : Create a local version of this solution by following the instructions in the repository Head over to our quick-start guide to get started with Atlas Vector Search today.

June 13, 2024