Technical Majesty of Palantir Foundry OS: A Deep-Dive into Enterprise Innovation

Revolutionizing Enterprise Operations with Foundry: A Deep Dive into Seamless Integration and Innovation

In an era where Data is King, the ability to not just manage but master this vast resource is pivotal for any enterprise aiming to stay ahead. Enter Palantir Technologies Foundry - a platform that doesn't just integrate data but transforms it into a dynamic, operational powerhouse. In this exploration, we'll delve into the depths of Foundry, providing a guide to its capabilities, from data ontology to operational application, turning complex systems into actionable insights.

Foundry is a software as a service (SaaS) product that harnesses data from various industrial data sources such as enterprise resource planning (ERP), a manufacturing execution system (MES), and data lakes. The data is rapidly integrated with fully automated pipelines and low-code tools that allow you to train and build machine learning models.

Foundry was named by Forrester as a leader in the The Forrester Wave™: AI/ML Platforms, Q3 2022. Scoring the highest marks possible in product vision, performance, market approach, and applications criteria.

You can build or customize manufacturing applications supporting everything from shop floor scheduling to a global operations center. In addition to manufacturing, Foundry also caters to financial applications, providing robust solutions for Anti-Money Laundering (AML) and transaction monitoring. With advanced analytical capabilities, users can detect suspicious activities, ensure compliance with regulatory requirements, and mitigate risks associated with financial transactions.

Palantir has partnered with all the major cloud providers. Here is for example, AWS Well-Architected Diagram Framework and Palantir Foundry as an illustration.

Palantir allows you to created full-featured web applications which provide real-time visibility, decision-making tools, and the ability to resolve operational decisions, making it easier to manage both manufacturing and financial operations seamlessly.

In the digital age, where data is both an asset and a complexity, Palantir Foundry OS stands as a beacon of operational transformation. This article will delve into the intricate technical architecture of Foundry, providing an unparalleled look at how it redefines enterprise operations through its modular, scalable, and secure design. We'll explore the deep technical capabilities, visualize the flow from data to action, and understand how Foundry is not just an operating system but an operational revolution.

Mind you as I am just going to scratch the surface and will attempt to also show some deep dive code examples as to putting Data as King into practice.

The Foundry Platform: A Symphony of Data and Action

At the core of its capabilities lies the Foundry Data Catalog, a sophisticated tool designed to empower teams by centralizing and curating data assets. This not only simplifies data discovery but also accelerates decision-making processes by ensuring access to high-quality, well-organized datasets.

Foundry provides this kind of higher-level abstraction and does not require technology skills to produce or consume data, allowing producers and consumers to work directly together rather than through intermediaries. At the same time, it is a highly collaborative Data Operating System.

The Data Catalog is an interactive repository that centralizes curated datasets and resources, facilitating efficient data discovery and collaboration across teams and of different kind of users. It enables users to browse, search, and access high-quality data assets, streamlining analytical workflows and promoting data-driven decision-making.

Producers might be Data Engineers, Data Scientists, or for example Machine Learning (ML) Engineers. Consumers could be Financial executives, Business Intelligence users, Data Analysts, or other Data Scientists, and ML Engineers. The choice is ours depending on the use cases.

By connecting analytical outputs with real-time decision-making processes, Foundry's Decision Orchestration ensures that insights are not only generated but also effectively applied to drive optimal business outcomes.

Key Features of the Data Catalog:

• Curated Collections and Files: The Data Catalog organizes resources into Collections and Files. Collections group related files pertinent to specific topics, audiences, or purposes, while Files encompass datasets, analyses, modules, and other relevant resources. This structured organization aids users in quickly locating and utilizing pertinent data.
• Interactive Browsing: Users can explore the Data Catalog through an intuitive interface, allowing for seamless navigation across various data assets. The platform supports keyword searches, filtering, and sorting, enhancing the efficiency of data discovery.
• Access Control and Permissions: While Collections are visible to all users, access to specific Files is governed by permissions. This ensures that sensitive data remains protected, and users can only interact with resources they are authorized to access. Much more on this later on... Keep reading.
• Integration with Analytical Tools: The Data Catalog integrates seamlessly with Foundry's suite of analytical tools, enabling users to directly analyze datasets within the platform. This integration streamlines workflows by reducing the need to export data to external tools.
• Enhanced Collaboration: By providing a centralized repository of curated data, the Data Catalog fosters collaboration among team members. Users can share insights, analyses, and datasets, aligning efforts towards common goals and ensuring consistency in data usage.

Data Integration and Ontology

Foundry isn't just another data platform; it's an orchestrator of information. With over 100 microservices, it brings together data from SAP, IoT devices, open APIs, and more into a cohesive ontology. This semantic layer doesn't just store data; it, providing a foundation where every piece of information is interconnected, secure, and actionable. Foundry is a marvel where the microservices work in concert to provide a unified view of an enterprise's data landscape, where each is designed for high availability and redundancy.

Data is King - Data Connections

Palantir Foundry's Data Connection is a robust application designed to facilitate seamless synchronization of data between external systems and Foundry. It supports a wide array of data sources, including cloud-based object stores, file systems, databases, and data warehouses, enabling users to integrate, model, and utilize data efficiently within the platform.

It includes 200+ data connectors, leveraging an extensible plugin-based paradigm, a flexible ingress topology, which can leverage agent-based, REST, JDBC, and other approaches. It allows easy-to-configure schedules, success criteria, and permission models and is Multi-modal (structured, unstructured, semi-structured, streaming, IoT, geospatial, etc.)

Key Features of Data Connection

• Source Management: Data Connection allows users to configure and manage connections to various data sources, referred to as "Sources." Each Source represents a single connection, encompassing the necessary configurations and credentials required for successful authentication.
• Runtimes: Depending on the networking setup between Foundry and the target system, Data Connection offers different runtimes to execute capabilities. This flexibility ensures compatibility with diverse network configurations, enhancing data integration processes.
• Data Syncing: This data OS supports various data synchronization methods, including batch, streaming, and Change Data Capture (CDC) syncs. This versatility ensures that data remains current and consistent across systems.
• Source Exploration: Data Connection provides intuitive interfaces for exploring and selecting data from connected sources. Users can preview data, apply filters, and configure syncs to import data into Foundry effectively.
• Security and Access Control: With granular security features, Data Connection enables federated management of data syncs across different teams. Access controls can be defined through role-based or classification-based permissions, ensuring data governance and compliance. I'll provide some code examples below. Keep reading !

Foundry on the Cloud

Foundry's services are deployed in a fault-tolerant manner, allowing for HA, redundancy, zero-downtime upgrades, and auto-scaling.

It's not merely about collecting data; it's about making sense of it. With tools like Monocle to visualize AIP (Palantir's Artificial Intelligence Platform) architecture, you visualize the data lineage - how data from varied sources like Transactions, SAP systems, Treasury.gov APIs, and even synthetic sensor data integrates into a coherent, semantic model. This ontology isn't static; it's a living, breathing framework where each data point is connected, secured, and ripe for ingestion and transformation.

Microservices and Resilience - Foundry's services are deployed in a fault-tolerant manner, allowing for zero-downtime upgrades. This is achieved through sophisticated monitoring and rollback strategies, ensuring that the platform remains operational even during updates.

Compute and Storage Flexibility - Foundry doesn't bind itself to one type of storage or compute. It deploys a mix of technologies across architectural tiers, using Apache Spark for batch processing and Ververica | Original creators of Apache Flink® for streaming, all run within a Kubernetes environment. This allows Foundry to handle dynamic workloads efficiently.

Real-Time Data Processing with Flink

Palantir Foundry's integration with Apache Flink revolutionizes real-time data processing by enabling low-latency, high-throughput data streams directly within Foundry's ecosystem. For those who do not know this yet, Apache Flink is an open-source distributed streaming data-flow engine written in Java and Scala with the initial release just 13 years ago.

This synergy allows for dynamic, stateful transformations on streaming data, turning complex operational data into actionable insights in milliseconds. Foundry's use of Flink not only ensures that data pipelines are resilient and scalable but also empowers organizations to make decisions based on the most current data, enhancing operational agility and strategic foresight.

• Security and Lineage: Foundry ensures that security isn't an afterthought. Every piece of data carries its permissions through the lineage, ensuring that access controls are both granular and robust. The platform allows for role-based, classification-based, and purpose-based access controls, making sure that data security flows seamlessly from source to application.
• Data Health and Quality: With Foundry, you're not just integrating data; you're nurturing it. Health checks at every step of the data pipeline mean you're always aware of the state of your data, ensuring it's refreshed, accurate, reliable and growing.

Quiver - Empowering Data Exploration

For the vast majority of users who aren't data scientists or engineers, Foundry offers intuitive operational tools. Here's where Quiver comes into play.

Quiver is Foundry's analytical suite designed for users who need to delve into data, particularly time series data. It allows for interactive exploration, visualization, and even the derivation of new data points through point-and-click operations, making complex data analysis accessible to executives, business analysts or operational staff.

Imagine a scenario where a supply chain manager needs to analyze production trends over time. With Quiver, they can:

• Visualize Trends: Plot historical data to see patterns in production output or machine performance.
• Derive Insights: Use formulas or simple machine learning to predict future demand or identify anomalies in production schedules.
• Collaborate: Export these analyses or share them within Foundry, allowing others to build upon or integrate these insights into operational dashboards or strategic planning sessions.

Quiver supports a blend of no-code and code-based operations, where users can start with visual interfaces but also export their work into Python for further customization by data scientists if needed. This dual approach democratizes data analysis while keeping the door open for more advanced manipulation.

Beyond Quiver - Foundry's operational applications like Object Explorer for browsing data, Foundry Map for geospatial analysis, or Workshop for building custom operational workflows, complement Quiver by providing a full suite of tools that cater to different needs, from exploration to action.

Visualizing the Data Journey

Operational Workflows with Workshop

Palantir Foundry's Workshop is a low-code application-building tool that enables users to create interactive and high-quality applications for operational workflows. It leverages the Object Data Layer, allowing application builders to utilize rich data characteristics and relationships.

Key Features of Workshop:

• Object Data Integration: Workshop utilizes the Object Data Layer as its primary building block, enabling application creators to leverage rich characteristics such as links between object types.
• Consistent Design: All Workshop components follow a unified design system, ensuring a consistent look and feel across applications.
• Interactivity and Complexity: Applications built in Workshop are dynamic and interactive, leveraging high-quality layouts and an easy-to-use events system to provide a user-friendly experience.
• Widgets and Layouts: Workshop offers a variety of widgets and layout options, such as Object Tables, Filter Lists, and Object Views, to help build comprehensive applications.
• Actions and Functions: It supports Actions for writeback to object data and Functions for implementing business logic, enhancing the application's capabilities.

Data Lineage with Monocle

Understanding how data moves and transforms through these operational workflows is crucial for maintaining data quality and compliance. This is where Monocle's data lineage capabilities become essential, as they provide comprehensive visibility into how data flows through your entire Foundry ecosystem.

Monocle's lineage capabilities extend beyond just tracking data flows, seamlessly integrating with popular business intelligence and analytics tools to provide end-to-end visibility of how data is being used across the organization. This integration enables business analysts and data scientists to understand the full context of their data assets, from source systems through transformations to final analytical outputs, ensuring reliable and trustworthy insights.

• It provides a visual representation of data pipelines, showing how data moves from source to consumption across different stages, including transformations and dependencies.
• Monocle helps in debugging, maintaining, and optimizing data workflows by giving a clear view of how data is processed, which datasets are used, and how they are connected.
• It also allows for scheduling and monitoring of data jobs, ensuring that data updates or transformations occur as planned.

Let's imagine a spider web of data nodes where each node represents a data transformation step. Monocle visualizes this journey, showing how data from say a SAP system or a public API like Treasury.gov is processed, transformed, and finally incorporated into the ontology.

Each node in Monocle can be inspected to reveal the underlying PySpark or SQL code, offering transparency into data transformations. This query for instance would help visualize how data from various sources is transformed into the target table within Foundry's ontology.

Data Visualization Reimagined: Inside Palantir's Secret Weapon - Contour

While Monocle provides visibility into data lineage and integration with BI tools, Contour takes this a step further by enabling direct visualization and analysis of this data within the Foundry platform. Through its intuitive interface and powerful visualization capabilities, Contour allows users to transform their data understanding into actionable insights and shareable dashboards.

Contour is a point-and-click analytics tool within Palantir Foundry, designed for users to analyze large-scale tabular data and create interactive dashboards without needing to write code. use datasets that have been processed or transformed through other parts of Foundry, like those managed by Monocle, but it primarily focuses on the end-user analysis and visualization of this data.

• It allows users to visualize, filter, and transform data by setting up analytical paths where each path can involve a series of operations on datasets.
• Contour supports the creation of charts, histograms, and other visualizations, enabling users to explore data deeply and share insights through dashboards.
• Users can also save the results of their analyses as new datasets within Foundry, which can then be used or further processed by other Foundry tools.

Contour is similar to Quiver in certain ways, as both tools enable users to perform tasks like data analysis, creating charts, and publishing interactive dashboards. However, the key distinction lies in the type of data they handle and their underlying computational methods. Quiver is the go-to choice when working with ontology objects or timeseries data, whereas Contour is designed specifically for working with datasets.

Ontology Management

Picture a network graph where nodes are data objects (like suppliers or plants) and edges are relationships or actions. This graph can dynamically change as new data or relationships are added.

The Ontology natively models actions within a cohesive, decision-centric model of the enterprise, enabling human and AI-driven actions to be safely staged as scenarios, governed with the same access controls as data and logic primitives, and securely written back to every enterprise substrate.

The ontology is not just a data model; it's an active framework where objects have properties, links, and actions pre-configured, allowing for complex business logic to be embedded directly into the data structure.

Modeling and Digital Twins

The true power of Foundry lies in its ability to marry data with intelligence. Whether you're a data scientist using Foundry's native tools or prefer external platforms such as, Alteryx, DataRobot or Amazon SageMaker, the integration is seamless. Foundry's approach to modeling is about creating digital twins - virtual representations that allow you to simulate, predict, and adapt in real-time.

Modeling and Digital Twins - Imagine a world where every piece of data has an intelligent counterpart. Foundry allows you to integrate machine learning models directly into your data ontology. The platform ensures the models are not just part of your data but are actively influencing real-world decisions through simulations and what-if scenarios.

Model Objectives - Through the model objectives library, you define not just a model but the problem it aims to solve. This approach is akin to mission control for your modeling efforts, where you test, compare, and deploy models based on their performance against specific business objectives.

Simulation and Strategy - Vertex in Foundry exemplifies how you can simulate different scenarios across your entire value chain. Vertex is a comprehensive toolkit designed to create, visualize, and interact with digital twins of real-world organizations. It enables users to model complex systems, simulate various scenarios, and optimize decision-making processes by providing a dynamic representation of their operations.

Imagine instantly understanding the impact of a supplier going offline or a sudden surge in demand. This isn't just about data visualization; it's about strategic foresight and operational agility.

Picture a dynamic graph that evolves as you tweak variables or simulate scenarios, showing immediate impacts across an entire business ecosystem.

Vertex allows for complex what-if analyses, chaining multiple models together for comprehensive simulations, providing strategic insights into operational changes.

Key Features of Vertex:

• System Graphs: Vertex allows users to build and explore system graphs that represent the relationships and interactions within their organization. This visualization aids in understanding cause and effect across the digital twin.
• Process Flow Diagramming: With point-and-click capabilities, users can create dynamic, integrated digital twins of their operations. This feature supports the rapid configuration of object-backed system graphs or production diagrams for various workflows, enhancing operational transparency.
• Simulation and Analysis: Vertex enables the execution of "what-if" analyses and simulations across entire networks. Users can configure and run simulations to apply real-world contexts, accurately quantifying the impact of proposed changes and facilitating informed decision-making.
• Scenarios: This feature allows users to interact with their modeled universe by simulating different operating conditions. By evaluating actions alongside modeled inputs, users can understand the real-world interactions of their digital twin and optimize outcomes.
• Object Relationship Exploration: Vertex provides tools to visualize and explore object relationships within the system graph. This capability helps in identifying key connections and dependencies, enhancing system understanding and optimization efforts.

Operational Applications

For the 70-80% of users who are not tech-savvy, Foundry offers a suite of applications that turn complex data interactions into intuitive, business-focused actions. From supply chain control towers to personalized dashboards, these applications are built on a low/no-code framework, ensuring that insights lead to actions without the need for deep technical knowledge.

Here's where 70-80% of Foundry users operate - in the realm of actionable insights. Foundry's applications like Object Explorer, Quiver, and the Foundry Map are designed for non-technical users to navigate and utilize complex data structures effortlessly.

The Map application provides powerful geospatial and temporal analysis and visualization capabilities, allowing you to integrate data from across Foundry into a cohesive geospatial experience

Application Elements

The Power of Low/No-Code Development: In an era where time is more valuable than ever, Foundry's no-code application builders like Workshop and Slate allow businesses to adapt and innovate at breakneck speeds.

Slate enables users to design, automate, and manage processes without requiring extensive programming expertise, making it accessible to both technical and non-technical users. Slate is particularly valuable for rapidly creating operational tools and dashboards, integrating seamlessly with Foundry's robust data capabilities. Palantir has an awesome 2.5hr Slate course anyone can take, yet might be taken offline on New Year's 2025. It is a real KISS Principle course, for the non-techies.

The Power of Low/No-Code Development democratizes tech innovation across all levels of an organization, sparking a culture of empowerment and creativity. With tools like Workshop and Slate, Foundry democratizes application development. You can build custom operational apps that leverage your ontology in hours, not months. This isn't just about reducing technical debt; it's about empowering every employee, and officer to innovate.

Actionable Insights - Every alert, every dashboard, every piece of actionable intelligence in Foundry is tied back to the ontology. When an alert is raised, it's not just a notification; it's a prompt for action, with pre-configured responses that guide users through complex decision-making processes.

Digital Twins in Action - The Game Changer - By simulating various scenarios in real-time, like sudden supply chain disruptions, Foundry enables businesses to strategize proactively. This was showcased in manufacturing with Airbus optimizing supply chains, in healthcare with the NHS managing patient data, in energy by enhancing battery production efficiency, in defense aiding US Army modernization, and in public safety for disaster response. Moreover, United Airlines , CVS Health , AIG , Swiss Re , and Westpac are all using Foundry in different capacities.

These applications prove Foundry isn't just a tool but a strategic asset, saving resources and potentially lives through data-driven decisions.

These real-world application of digital twins showcases how Foundry isn't just a tool but a strategic asset. Here is a quoate I truly like by Kai Altstaedt , of Airbus and author of Palantir Foundry by Use cases & Reconnaissance and IoT Data Processing with the Palantir Foundry, both highly recommended.

"You're essentially mining that raw data to create something valuable, much like processing petroleum to gas to get gasoline." Mr. Kai Altstaedt

Security and Compliance by Design - With data lineage linked directly to security permissions, Foundry ensures that every action, every piece of data, is governed by your organization's rules, making compliance not an afterthought but a fundamental part of the data ecosystem. Foundry ensures that security isn't an afterthought. Every piece of data carries its permissions through the lineage, ensuring that access controls are both granular and robust. The platform allows for role-based, classification-based, and purpose-based access controls, making sure that data security flows seamlessly from source to application. Foundry's approach to security ensures that every piece of data, every model, every action is handled with the highest integrity, making compliance not just possible but intrinsic to the platform's design.

Thought-Provoking Insights

Data as a Living Entity - We've moved past static datasets. With Foundry, data is alive, interactive, and continuously evolving. This shift challenges us to rethink how we approach problem-solving, moving from reactive to predictive and adaptive strategies. With Foundry, we move beyond static data to kinetic data - data that drives action. This paradigm shift invites us to rethink how we approach business strategy, focusing on dynamic, real-time responsiveness.

Decision Orchestration - What is it ? Decision Orchestration facilitates the synchronization of decisions back to source systems, ensuring that actions derived from data analyses are promptly implemented within existing operational frameworks. It synchronizes decisions back to source systems, ensuring that data-driven actions are promptly implemented within existing operational frameworks

It captures decisions made during operations, simulations, or AI-driven analyses, feeding this information back into the data platform to enhance organizational learning and adaptability. This natually promotes collaboration between data, analytics, and operational teams, ensuring that insights are effectively translated into actionable strategies.

Additionally, and another huge strength going forward is deployment of AI-powered decision-making applications, enabling organizations to leverage advanced analytics for improved operational efficiency.

Human-Centric AI - Empowerment Over Automation: While AI and automation are transformative, Foundry emphasizes human-in-the-loop processes. It's not about replacing the human touch but enhancing it, making every decision point smarter, quicker, and more informed. Foundry's integration of AI isn't about replacing humans but augmenting human decision-making. It's a model where AI supports, enhances, and integrates with human insight, creating a symbiotic relationship.

Foundry's integration of AI isn't about replacing humans but augmenting human decision-making.

The Future is Integrated: Silos are the enemy of progress. Foundry's integrated approach shows that the future of business isn't in discrete tools but in platforms that weave together data, intelligence, and action into a seamless operational fabric. Foundry demonstrates that the future of enterprise technology lies not in siloed systems but in integrated platforms where data, intelligence, and operations are intertwined.

Visualize a Deep Dive into Foundry's Technical Capabilities

Data Engineering with Code Repositories

Foundry provides an IDE-like environment for data engineers, where code is version-controlled much like software development. Here, data pipelines are not just written but managed with full lifecycle capabilities from branching to merging, as Git-like paradigms for data versioning is fully integrated into Foundry's IDE.

Security and Compliance

Think of security as a color-coded overlay on your data lineage graph, where permissions propagate from source to destination, ensuring that data access is controlled at every step.

Security in Foundry is integrated into the metadata, allowing for dynamic permission propagation. This means you can set permissions at the source and watch them flow through transformations, ensuring compliance without manual intervention.

Model Deployment and Management

Envision a dashboard where different models, each with its lineage, performance metrics, and deployment status, are compared side by side, showing which model fits best for a particular business challenge.

Foundry's model objectives library allows for the definition, testing, and deployment of models in a manner that integrates them directly into the ontology. This means models can be invoked as part of business processes, updating data in real-time based on model outputs.

We cannot speak of deployments whether to Dev, UTA or Prod without speaking about the most crucial aspect of organizational data govenance and security.

Cyber-Security and POLP

As data is the most integral to driving federal, state government and financial business success, safeguarding it is paramount. The principle of least privilege (POLP) is a security practice that limits user permissions and account creation to the bare minimum resources needed to perform an authorized activity. This is a must especially today.

The National Institute of Standards and Technology (NIST) defines least privilege as granting each entity in a security architecture the minimum resources and authorizations it needs to perform its function.

Less is more while the kiss principle applies here more than ever

Some examples of least privilege include: 

• Payroll processing clerks only have access to the payroll application. 
• Marketing specialists don't need access to employee salary data. 
• Entry-level government workers don't have access to top-secret documents. 

POLP improves security without hindering productivity. It also makes it easier to track and audit access permissions, and fosters a security-first mindset. Organizations can use least-privileged user accounts (LPUs) and guest user accounts to reduce risk: 

• LPUs: Offer users the bare minimum privileges necessary to complete routine tasks. 
• Guest users: Have less privileges than an LPU and are granted limited, temporary access to the organization's network. 

Securing PII Data in Palantir Foundry

Foundry offers robust, enterprise-grade security features designed to protect sensitive information, ensure compliance, and facilitate secure collaboration. The security capabilities of the Foundry OS platform provide organizations with the tools necessary to securely manage their data and workflows while enabling transparent access control and governance.

Security Features Overview

Foundry offers a comprehensive suite of security measures that span data protection, user access management, and governance. These capabilities are designed to ensure that sensitive data is protected at all stages of its lifecycle, from storage and processing to sharing and analysis. Below are some key features that underpin the platform’s security framework:

• Granular Access Controls: Foundry provides fine-grained access control mechanisms, allowing administrators to define permissions based on roles, classifications, or user attributes. This ensures that only authorized individuals can access specific datasets, modules, and tools. The system supports both centralized and decentralized access models, giving teams flexibility while maintaining stringent security measures.
• Data Encryption: Palantir Foundry employs end-to-end encryption for data both at rest and in transit. This ensures that sensitive data is encrypted as it is stored in the platform and when it moves across networks, offering an added layer of protection against unauthorized access or breaches.

• Audit Trails and Monitoring: The platform maintains detailed logs of all user activities and data access, providing a full audit trail for compliance and security monitoring. This helps organizations track potential security threats or unusual activities and ensures that they can respond swiftly to potential incidents.
• Role-Based Access Control (RBAC): Foundry implements RBAC to manage user permissions efficiently. This allows users to be assigned roles that limit their access to only the necessary resources for their tasks, helping minimize risk and reduce exposure of sensitive information.
• Compliance and Certifications: Palantir Foundry is compliant with a variety of industry standards and regulations, including AML, GDPR, SOC 2, SOX, HIPAA industry standards and internal policies of an organization. This ensures that the platform meets the stringent requirements of highly regulated industries, offering enterprises confidence that their data handling practices are secure and compliant.
• Collaborative Security Features: Foundry’s security features enable secure collaboration across teams and organizations. By offering customizable access permissions and detailed tracking, users can collaborate on data analysis while maintaining the integrity and confidentiality of sensitive information.

In the financial sector, adhering to stringent compliance regulations is crucial for maintaining trust and operational integrity. Foundry offers robust solutions tailored to meet various financial compliance standards, including Anti-Money Laundering (AML) requirements. Let's explores how Foundry addresses these challenges, ensuring secure and compliant data management.

Financial Compliance Regulations and AML

Palantir Foundry is designed to assist leadership in federal, state government, financial institutions and many businesses in meeting a range of compliance requirements.

Some of these regulations might include:

• Anti-Money Laundering (AML): Foundry provides AI-based solutions to enhance AML processes, enabling institutions to detect and investigate suspicious activities effectively.
• General Data Protection Regulation (GDPR): Ensures that personal data is processed in compliance with GDPR standards, safeguarding individual privacy rights.
• Health Insurance Portability and Accountability Act (HIPAA): Facilitates the secure handling of Protected Health Information (PHI), ensuring compliance with HIPAA regulations.
• International Traffic in Arms Regulations (ITAR): Supports compliance with ITAR, governing the export and import of defense-related articles and services.
• Federal Risk and Authorization Management Program (FedRAMP): Provides a standardized approach to security assessment, authorization, and continuous monitoring for cloud products and services.

By aligning with these regulations, Palantir Foundry helps organizations mitigate risks associated with non-compliance and enhances their ability to manage sensitive data securely.

Securing PII Data in Palantir Foundry

Managing PII Data with Best Practices

Many organizations, such as government entities, banks, hospitals, insurance companies, and telecoms, require access to personally identifiable information (PII) data to perform their essential services. In finance, examples of PII fields include: full name, Social Security number (SSN), bank account number, credit card number, debit card number, routing number, address, phone number, email address, date of birth, and account login credentials.

There are various Foundry techniques such as using markings, encryption, and checkpoints to restrict access to sensitive information, ensuring that data is accessed and used legitimately.

Implementing Markings for Data Security

The Markings system serve as a method for restricting access to resources. They are a form of mandatory controls that restrict access to resources such as datasets or code repositories. Users must hold the necessary marking clearance to interact with marked resources, similar to how top-secret documents require specific clearance levels in intelligence operations. Foundry’s marking system offers a more generalized approach, allowing users to tag various resources, which helps manage data privacy effectively.

Applying markings to a dataset means that users without marking access cannot view the associated data.

• Functions similarly to security clearance levels (e.g., "Top Secret" analogy)
• Restricts access to resources including datasets, code repositories, and analyses
• Users must have explicit marking access to view or interact with marked resources
• Markings propagate automatically through downstream dependencies
• Can be organized into categories (e.g., top clearnance, sensitive information, geographical regions, etc.)

In Foundry, we apply markings to transactions or customer data, specifically to the guest object type, thereby protecting sensitive PII data from unauthorized access.

• Once a marking is applied to a dataset, users who do not have access to that marking will be restricted from viewing any associated data.
• For instance, customer service agents may retain access to complaint information but could lose access to PII data like guest names and other identifying details.
• Ensuring customer service personnel can still perform their duties while also protecting sensitive information is essential.

Introducing Encryption Channels

Cipher is a service that allows users to obfuscate data using cryptographic operations (encryption, decryption, or hashing). Cipher manages algorithms and cryptography keys through Channels and Licenses. These concepts allow for secure management and enable new users (including users who do not code or those without specialized knowledge) to deploy privacy-enhancing tools in legible and reliable ways.

Encryption channels are introduced as a method to encrypt data, where a key allows only certain users or groups to perform decryption and encryption actions.

Encryption channels create mechanisms for encrypting data with specific keys, enhancing data security through a "Cipher" system.

Palantir Foundry offers robust encryption capabilities to ensure the security and privacy of data. One of the key features is Cipher, which provides an additional layer of encryption on top of Foundry's existing storage and network-level protections. Cipher allows users to configure privacy and governance protections within operational workflows.

• Enables field-level encryption of sensitive data.
• Uses deterministic encryption with managed keys.
• Supports granular access control through licensing system.
• Allows for selective decryption of specific fields

Setup Process

1. Channel Creation

• Create a new cipher channel through the Cipher application
• Select deterministic encryption as the encryption method
• Allow Foundry to generate the encryption key automatically
• Channel serves as the core encryption mechanism for PII fields

2. License Configuration

Here is a Python config example

# Admin license requirements 
- Encryption permissions: Enabled 
- Decryption permissions: Not required 
- Access restrictions: Limited to admin users        

The Licensing System

In Foundry we need to create an encryption channel and a corresponding license that permits users to access specific PII data securely.

Using these licenses, the transformation scripts can then be modified to encrypt sensitive columns in the data, ensuring enhanced protection of personal information while still providing relevant access for operational needs.

Foundry provides 3 types of licenses, Admin, DML and Operational each offering different capabilities.

Administrative License

• Enables encryption capabilities
• Restricted to admin users
• Protected by same marking system as sensitive data
• Enables decryption for end users
• Supports rate limiting (e.g., 70 decryption operations per day)
• Can be assigned to specific user groups
• Tracks usage and enforces quotas

This is a Python config example of creating an admin license for encryption capabilities

# Admin license requirements 
- Encryption permissions: Enabled 
- Decryption permissions: Not required 
- Access restrictions: Limited to admin users        

Data Manager License

A Data Manager License (DML) in Foundry is a specialized permission system that enables users to manage and control data access across the platform. It's a critical component for implementing data governance and security policies. Core functionalities include granting/revoking dataset access, managing project-level permissions, and controlling dataset visibility

Three main permission levels exist:

• View Access (read data and metadata)
• Edit Access (modify properties and settings)
• Admin Access (manage permissions and security)

DML enables implementation of data governance policies, including data classification, retention policies, and quality rules. It supports compliance management through audit logging, regulatory requirement tracking, and data protection standards. Includes built-in monitoring capabilities for tracking data usage patterns and access histories. It allows for role-based access control through user group mappings and inheritance rules. Most importantly, it requires regular maintenance through access reviews, permission updates, and compliance checks.

Operational User License

An Operational License is a specialized permission that allows users to decrypt and access specific encrypted data fields on an as-needed basis. It's designed for end-users who need occasional access to sensitive data (i.e. customer service agents accessing customer information.) It supports quota management by limiting how many times a user can decrypt sensitive fields within a specified time period and includes audit trail capabilities, tracking who decrypted what data and when.

Key features include:

• Field-level decryption permissions
• Rate limiting (e.g., number of decryptions per day)
• Usage tracking and auditing
• Time-based access controls

It can be assigned to specific user groups and roles, allowing for role-based access control and works alongside marking systems, providing an additional layer of security for sensitive data. Lastly, it enables organizations to implement "need-to-know" access principles while maintaining operational efficiency ensuring proper usage and compliance.

Code Encryption Process - The Implementation Process

The encryption process begins by creating a Cipher channel in Foundry, which serves as the encryption mechanism and key management system. The admin license must be configured first to enable encryption capabilities. It requires encryption permissions and protected by marking systems.

We than apply core implementation using Python transforms with specific decorators and apply encryption at the column level using the crypto.encrypt() function. Here's a short code example.

The code above specifies that for each of the columns I want to encrypt

After encryption, the schema updates are required to change field types to "Cipher text" (e.g., changing from String or Date to Cipher text.) Marking propagation can be controlled through transform configuration to ensure appropriate access controls downstream. The process includes built-in validation to ensure successful encryption and proper schema updates.

Some of the performance considerations are:

• Encryption occurs during transform execution
• Schema changes trigger reindexing
• Decryption happens on-demand
• Batched encryption for large datasets

Best practices should include:

• Separate transform logic from encryption configuration
• Clear documentation of encrypted fields
• Version control of encryption transforms

The process supports both one-time encryption of existing data and ongoing encryption of new data through data pipelines

Post-Merge Code Functionality

Once the code changes are finalized, they must be committed in a separate pull request (PR) before merging into the master branch. The build process subsequently updates the code, encrypting sensitive data columns so that details like last names, Credit Card, Social Security, Birth dates, Legal Entity, other Financial fields are all obscured.

Data Access Changes After Encryption

After encryption, there’s no longer a necessity to restrict access to the entire dataset, as sensitive values are now protected. The code is updated to halt the propagation of guest Personally Identifiable Information (PII) marking on downstream data sets to prevent unnecessary restrictions.

Checkpoints in Data Handling

This feature is particularly useful for critical activities like downloading files or decrypting sensitive data, ensuring that users remember data handling expectations. The checkpoints application in Foundry allows you to track certain actions users take throughout the platform and prompt them for a justification for that action. Checkpoints can also have a frequency whereby they trigger or do not.

By logging user justifications, organizations maintain accountability and transparency for data access actions.

Setting Up a Checkpoint for Decryption

The process of configuring a checkpoint begins in the checkpoints application, where the user can click on "Configure New Checkpoint." The checkpoint is designed to apply to specific groups of users, ensuring that only relevant personnel need to provide justifications when decrypting sensitive information. When setting up the checkpoint for decrypting personally identifiable information (PII), it is important to create a prompt that clearly asks users to explain their reasoning, helping to maintain data protection protocols.

User Interaction with the Checkpoint

Once the checkpoint is established, users, such as customer service agents, engage with it in their workflows. For instance, when attempting to decrypt a phone number, the user is required to provide a justification for their need to access that information, which is then logged for future reference. This procedure strengthens the integrity of data management by ensuring that all requests are purposeful and recorded.

Review and Audit Capabilities

The logged information from checkpoints can be reviewed by authorized personnel, allowing them to see user justifications and other relevant metadata like the cipher channel used. The checkpoint log can be filtered by various criteria such as date, resource, or user, aiding in data auditing processes. This capability not only supports compliance efforts but also enhances security by providing a detailed history of data access events.

In Conclusion

Palantir Foundry OS is not just a bunch of tools; it's a redefinition of how data should function within an enterprise - as an active, intelligent participant in everyday business operations. From its modular architecture to its comprehensive security model, Foundry offers a glimpse into the future of enterprise software where data, models, and operations converge into one cohesive, dynamic system.

In a whirlwind tour of Palantir's Foundry OS platform, we've just scratched the surface of what's possible when data, models, and business operations converge into a singular, dynamic system. This isn't just about managing data; it's about transforming how businesses function at their core.

Foundry is a paradigm shift in how enterprises can leverage data to not just survive but thrive in complex, ever-changing environments. Whether you're a data engineer, a business analyst, or an operational manager, Foundry invites you to join a revolution where data doesn't just inform but transforms.

It takes you through a journey from data chaos to operational clarity, from static models to dynamic digital twins, and from isolated applications to integrated, actionable insights. Foundry invites businesses to redefine their operational DNA, making data not just a resource but the core of innovation, strategy, and execution.

Foundry is a revolution where data doesn't just inform but transforms.

This expanded and technically enriched article not only informs but also invites you as professionals to envision how Foundry can be pivotal in transforming your technical landscape in the cloud while securing the most important King, your Data.

I certainly hope this article assisted you. Do let me know your thoughts. If you can answer this poll, I would certainly appreciate it. Which Foundry tools and features do you find most technically impressive ?

A) Data Lineage Visualization

B) Ontology-Driven Security & Compliance

C) Model Deployment Integration

D) No-Code Application Development

E) AI and Machine Learning

G) ALL OF THE ABOVE

With that allow me to wish all Season's Greetings, Happy Hanukkah, Merry Christmas, Happy Kwanzaa, Happy Boxing Day, a warm Winter Solstice and to the FRIENDS Fans, a most happiest Festivus with Peace on Earth to all !!!

#Data #Management #Leadership #Palantir #DataArchitecture #EnterpriseTech #AIOperations #SecurityInData #TechnicalInnovation #DataInnovation #Security #DevOpSec #EnterpriseAI #DigitalTransformation #NoCode #OperationalExcellence #GenAI #LLM #AWS

Disclaimer: The views expressed in this article are solely those of the author and do not necessarily reflect the views of AWS, Palantir, or any other organizations mentioned. Graphics used are the property of their respective owners and are used with permission to educate the masses on proper implentations of data.

Refrences:

Palantir Foundry by Use cases: An introduction to Contour, Reports, Fusion and Recipes in the Palantir Foundry 2nd Edition - by Kai Altstaedt

Palantir Documentation, Whitepapers, Webminars, Palantir Developers Community, Palantir Learn, AWS.

Glossary:

AI (Artificial Intelligence): The simulation of human intelligence processes by computer systems. These processes include learning, reasoning, and self-correction.

AIP (Artificial Intelligence Platform): Refers to Palantir's platform for building, deploying, and managing AI and machine learning models.

API (Application Programming Interface): A set of protocols and tools for building software and applications, allowing different systems to communicate and share data seamlessly.

Batch Processing: A method where data is processed in groups or batches, rather than in real-time, often used for tasks like data analysis or large-scale data imports.

CDC (Change Data Capture): A process that captures changes made in the source system in real-time and applies them to the target system, ensuring data synchronization.

Cipher: In the context of Foundry, a service for encrypting and decrypting data, providing an additional layer of security for sensitive information.

Data Catalog: A system or tool within Foundry for organizing, managing, and providing access to data assets, making them easily discoverable and usable.

Data Lineage: The lifecycle of data within a system, showing its origins, where and how it moves, transforms, and is consumed.

Data Ontology: A structured framework or model that defines data and the relationships between different data entities, used in Foundry to manage data semantics.

Digital Twins: Virtual representations of physical objects or systems, used in Foundry for simulation, analysis, and operational optimization.

Encryption: The process of converting data into a code to prevent unauthorized access, used at rest and in transit to protect data.

ERP (Enterprise Resource Planning): Business management software that integrates various functions like accounting, HR, and manufacturing into one system.

Flink (Apache Flink): An open-source stream processing framework for distributed, high-performance, always-available, and accurate data streaming applications.

Foundry OS (Operating System): Palantir's platform that integrates data management, operations, and analytics into a single system.

IoT (Internet of Things): The network of physical objects embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet.

JDBC (Java Database Connectivity): An API for Java that defines how a client may access a database.

Kubernetes: An open-source system for automating deployment, scaling, and management of containerized applications.

Low-Code / No-Code: Development approaches that require little to no coding knowledge, allowing for rapid application development within Foundry.

Markings: In Foundry, a security feature that applies access restrictions to data or resources based on clearance levels.

MES (Manufacturing Execution System): Software that manages and monitors work-in-process on a factory floor.

Microservices: A style of software architecture where complex applications are structured as a collection of loosely coupled services.

Monocle: A Foundry tool for visualizing data lineage and pipeline dependencies.

Ontology: In Foundry, it's the structured model of data, defining relationships and providing context to information.

PII (Personally Identifiable Information): Data that can be used to identify an individual, such as names, addresses, or social security numbers.

POLP (Principle of Least Privilege): A security concept where users are given the minimum levels of access necessary to perform their job functions.

PySpark: The Python API for Apache Spark, used for big data processing and analytics within Foundry.

Quiver: An analytical tool in Foundry for time series data exploration, visualization, and analysis.

RBAC (Role-Based Access Control): A method of regulating access to computer or network resources based on the roles of individual users within an enterprise.

REST (Representational State Transfer): An architectural style for designing networked applications, particularly web services.

SaaS (Software as a Service): A method of software delivery where a provider licenses an application to customers for use as a service on demand.

Slate: Foundry's no-code platform for building and automating operational processes.

Vertex: A Foundry tool for creating, visualizing, and interacting with digital twins and system simulations.

Workshop: A Foundry tool for building low/no-code custom applications leveraging data from the ontology.