Stream Processing: Enhancing Data Privacy and Security Across Industries
Tags: datascience, database, opensource, learning
Published: 2023-12-05 17:48:30

> [**Sooter Saalu**](https://github.com/soot3) **| Technical Writer**

It is the year 2023, and a distressing frequency of data breaches marks the tech landscape. Prominent platforms like [LinkedIn](https://scrubbed.net/blog/linkedin-data-leak-what-we-can-do-about-it/), [Facebook](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users), and [Reddit](https://techcrunch.com/2023/06/19/hackers-threaten-to-leak-80gb-of-confidential-data-stolen-from-reddit) have all fallen prey to hacking attempts, resulting in the exposure of sensitive information belonging to millions of users. These breaches have highlighted vulnerabilities stemming from social engineering, third-party tool mismanagement, and software/API susceptibilities. This wave of incidents has raised concerns about the reliability of tech-driven enterprises in safeguarding entrusted private data, encompassing emails, addresses, and financial details.

It's the responsibility of the organization to maintain data privacy—that is, protecting user personal information and ensuring it is collected, stored, and used in ways that respect user rights and preferences while safeguarding against unauthorized access. Organizations have unprecedented access to vast amounts of personal data managed in real time through stream processing. Data privacy is becoming more important due to the volume of sensitive information being collected and stored in real time. The potential for misuse, data breaches, and privacy violations has [grown exponentially](https://www.digitalguardian.com/blog/history-data-breaches), highlighting the need for enhanced data privacy and security measures to safeguard individuals' information in this digital age.

This article explores helpful measures you can use to enhance your organization's data privacy and security with stream processing. It will also walk you through examples from different industries where stream processing has been used to address the need for enhanced privacy and security.

## How Can Stream Processing Enhance Privacy?

Organizations are dealing with an increasing volume of real-time data from internal systems (such as sensors, machines, mobile devices, and business services) and from external systems (such as data brokers and business partners). As high data traffic becomes more prevalent, it introduces new vulnerabilities and challenges for the privacy and security of your architecture. Stream processing, a technology designed to handle this constant stream of information, offers solutions to many challenges associated with these ever-increasing volumes of data. Let's explore how you can leverage stream processing to enhance privacy:

* **Data segmentation:** In your database, it's best to separate your sensitive data from the rest. This allows you to impose more stringent safeguards and [least privilege](https://www.digitalguardian.com/blog/what-principle-least-privilege-polp-best-practice-information-security-and-compliance) measures on sensitive data without unnecessarily impeding the overall flow of data within your organization. Personally identifiable information (PII) such as names, email addresses, and phone numbers can be stored separately to be integrated only when needed for organizational operations. You can use stream processing to implement real-time segmentation and data integration, identifying sensitive data and segmenting it as it's generated or received. It can be subsequently directed to specific secure storage or encryption measures without delay.
    
* **Anonymization:** Apart from separating user-sensitive data, you should take further steps to anonymize your users and make it harder to pinpoint specific individuals. This can involve [tokenization and masking techniques](https://www.linkedin.com/pulse/data-masking-tokenization-encryption-which-do-i-choose-atakama-llc/) applied in real time as data flows through the system.
    
* **Encryption:** Protecting your data extends beyond storage; it's equally crucial during processing, when in use, and in transit between services. This necessitates encryption both at rest and in motion, ensuring end-to-end security for your sensitive information. Encryption algorithms can be applied to both the source and destination systems of your stream processing flow to restrict unauthorized access to your data.
    

These three measures are interconnected: data segmentation lays the foundation for effective anonymization, while anonymization, in turn, strengthens your encryption efforts. Data solutions focused on stream processing, such as [RisingWave Cloud](https://www.risingwave.com/cloud/), often provide a standard security and privacy package that helps you effectively utilize data segmentation, anonymization, and encryption within your workflow.

### How Can You Use Stream Processing to Detect and Respond to Threats?

Stream processing has a large advantage over traditional batch processing methods, especially when it comes to analyzing data and implementing change in real time. Stream processing can enhance your workflow by allowing you to create triggers, alerts, algorithms, or procedures that operate in real time as your data undergoes updates and alterations.

Within your systems, you can employ stream processing in two significant ways:

* **Monitoring data for anomalies:** Stream processing can enable continuous monitoring of your data streams and allow you to detect anomalies or suspicious activities in real time. As data arrives, it undergoes real-time analysis, where checks are conducted to identify irregular patterns and potential threats and offer insights in real time. For instance, financial data can be scrutinized for [signs of fraudulent behavior](https://www.risingwave.com/blog/how-financial-broker-leader-leveraged-risingwave-streaming-database-to-build-feature-store-for-fraud-detection/), while web user activity can be assessed for hacking patterns, allowing you to quickly identify denial of service (DoS) attempts and other potential threats.
    
* **Monitoring internal systems:** Stream processing also enables real-time monitoring and management of your infrastructure and its operations. You can implement log monitoring by parsing the data as it's being generated, ensuring that all systems are working as intended. Threshold-based alerting or rule-based triggers can be set up with stream processing to work on accumulated data and aggregated system metrics.
    

These capabilities prove particularly valuable in specialized streaming databases like [RisingWave](https://theankurtyagi.com/everything-about-risingwave-an-open-source-distributed-sql-for-stream-processing/), which excels in incremental calculations and updates by identifying changes to your data and selectively updating only the affected parts. This enables quick pattern recognition with faster and more efficient data processing of your real-time data.

## How Stream Processing Enhances Privacy and Security in Different Industries

Stream processing is widely applicable across various industries, and the ability for real-time monitoring alone empowers organizations to encode algorithmic procedures efficiently into their operational workflows. The following sections highlight some industries where stream processing is applied, with a particular focus on enhancing security and privacy measures.

### Financial Services

The financial industry is [one of the largest adopters](https://www.kai-waehner.de/blog/2023/04/04/the-state-of-data-streaming-for-financial-services-in-2023/) of data streaming technology, compelled by the need to service millions of customers in real time. Stream processing plays a pivotal role in empowering financial services across various domains, including capital markets, payments processing, and retail banking. It accelerates core banking services, expedites transactions, and leverages the power of public cloud and open APIs to enable real-time data sharing.

Stream processing also helps with business continuity by ensuring uninterrupted business operations in times of disasters or downtime. By leveraging the scale and resilience of cloud computing, financial institutions can rely on stream processing to maintain their functionality. This means that even in the face of unforeseen events, critical financial services can continue to operate, thus safeguarding the interests of customers and stakeholders alike.

Financial institutions use stream processing to enact real-time segmentation of data and ensure the immediate identification and isolation of sensitive data. Financial organizations implement granular access controls, providing precise control over who can access particular types of data. In the banking sector, this can be seen where varying levels of access are granted based on job roles and responsibilities. An analyst detecting fraudulent activities relies on transactional data to develop effective algorithms and requires access to detailed transaction histories and account information. However, personally identifiable information (PII), such as names, phone numbers, and email addresses, is not needed and should be masked or segmented from the analyst's data stream.

With stream processing, [dynamic privacy policies](https://www.linkedin.com/advice/3/how-do-you-ensure-data-security-privacy-stream) can be applied based on real-time data attributes, providing adaptable protection measures and ensuring general data is anonymized and pseudonymized in real time, making it harder to identify individuals.

Through continuous data stream monitoring, a financial institution can react to and anticipate [instances of fraud](https://flink.apache.org/2020/01/15/advanced-flink-application-patterns-vol.1-case-study-of-a-fraud-detection-system/), sudden financial spikes, or any other anomalies in its data flow. Retail banking and payment processing domains use stream processing in [algorithmic fraud detection](https://developer.confluent.io/tutorials/credit-card-activity/confluent.html) mechanisms, and it's also used in the development of comprehensive [risk management algorithms](https://www.confluent.io/blog/real-time-risk-management-with-kafka-and-event-streaming/) that measure, monitor, mitigate, and report risk in capital markets.

### Healthcare

In the healthcare sector, data streaming use cases are common in pharmacy, insurance, and patient communications. Stream processing technology enables rapid access to crucial patient data, expedites diagnostics, and offers real-time collaboration and information sharing among healthcare professionals through cloud technologies.

Reliability is key when dealing with healthcare and technology-enabled healthcare solutions. For example, [Babylon Health](https://www.babylonhealth.com/) uses stream processing to [power telemedicine operations at scale](https://videos.confluent.io/watch/KdYTdwEvDn4JZCqmMrFTmz). This ensures uninterrupted healthcare operations and PII- and GDPR-compliant security for its client data. Research-focused pharmaceutical company [Bayer](https://www.bayer.com/en/) uses stream processing to parse clinical trials, patents, reports, and news in real time over the course of its [research](https://www.confluent.io/events/kafka-summit-europe-2021/bayer-document-stream-processing/), building algorithms that evolve as the data changes.

Whether it's patient data, health records, or the results of clinical trials, almost all the data gathered in this sector can be considered private information. Personally identifiable information must be anonymized before results of any kind are shared, especially in clinical trials. Stream processing allows for the encryption of data at rest and in transit, safeguarding it from unauthorized access. Data masking techniques can be applied in real time to further protect sensitive information and ensure data privacy while it's being processed.

The continuous monitoring of data streams also allows healthcare providers to react swiftly to emerging situations and can help to predict potential challenges, such as trends or spikes in patient vitals or medication responses. This capacity is prominently exemplified in healthcare through the utilization of cutting-edge patient monitoring systems that enable early identification of anomalies and proactive intervention. Furthermore, stream processing is instrumental in crafting advanced algorithms for medical research and clinical trials that update in real time as data changes, propelling the healthcare industry forward in terms of innovation and patient care.

### Government

Stream processing in the public sector offers improvements in data-based, real-time decision-making and improved citizen service. [Real-time pandemic dashboards and algorithmic analysis of public sentiments](https://www.mckinsey.com/industries/public-sector/our-insights/accelerating-data-and-analytics-maturity-in-the-us-public-sector) are a few examples of technological innovations that have helped decision-making in the public sector. Stream processing transforms a variety of government services, including emergency response, citizen engagement, and administrative operations. It expedites response times and access to vital information, and it leverages modern technologies to facilitate communication and collaboration among government agencies.

Stream processing also has significant potential to drive automation initiatives within the public sector. In traffic and transportation management, the integration of [IoT](https://en.wikipedia.org/wiki/Internet_of_things) sensors and automated traffic control devices can significantly optimize operations. Real-time citizen engagement ensures the timely dissemination of information, allowing authorities to reach a broad audience through live broadcasts, streamed meetings, and timely updates on crucial decisions. This approach also enables immediate interaction to address citizens' questions and concerns and enhances responsiveness to local needs.

In the age of digital voting and expansive governmental collaboration, there's an urgent need for strengthened privacy and security protocols. This encompasses safeguarding classified documents and ensuring the integrity of voter data to uphold the integrity of the democratic process. In a government context, sensitive information would immediately be a matter of national security. Stream processing can help in detecting and responding to threats in real time with continuous monitoring of network traffic for suspicious patterns and unauthorized access. Stream processing can be used to enforce dynamic access control policies based on real-time data attributes. For instance, it can restrict or grant access to specific resources or systems based on the security posture of a user or device.

### Manufacturing

In [manufacturing](https://www.kai-waehner.de/blog/2023/03/10/the-state-of-data-streaming-for-manufacturing-in-2023/), data streaming technology has revolutionized various production processes, including supply chain management, quality control, and process optimization. It facilitates real-time monitoring of production lines, expedites quality checks, and leverages IoT devices to facilitate communication and coordination between the various stages of production.

With continuous monitoring, manufacturers can take advantage of [predictive maintenance](https://www.ibm.com/topics/predictive-maintenance), which allows them to respond quickly to emergent issues and anticipate potential obstacles days before their occurrence. Stream processing is also utilized in the creation of complex algorithms for automated tasks, process optimization, and [just-in-time manufacturing](https://www.inboundlogistics.com/articles/just-in-time-manufacturing/), thereby advancing the manufacturing industry's efficiency and product quality.

The manufacturing sector also employs stream processing for enhanced security. This includes continuous monitoring of network traffic and implementing least privilege access control for sensitive information and systems. Automated or streamlined incident responses ensure prompt action in cases of security threats. Robust data encryption safeguards the confidentiality of sensitive production data and intellectual property. These measures are implemented throughout the entire production lifecycle, from data collection to final assembly.

### Cybersecurity

Cybersecurity is important across all the industry domains discussed, with internet and cloud-enabled services generating and sharing necessary data across your infrastructure. The integration of stream processing emerges as a pivotal tool in fortifying cybersecurity measures. Since it operates in real time, it bolsters the protective shield around critical systems and sensitive information by actively identifying and thwarting potential threats. It can provide continuous monitoring of security threats through the [meticulous analysis and filtering of audit logs](https://developer.confluent.io/tutorials/audit-logs/confluent.html?session_ref=https://www.confluent.io/blog/introducing-stream-processing-use-case-recipes-for-every-industry/), which allows for the timely identification of anomalous activities. Additionally, stream processing empowers the discernment of [firewall-deny events](https://developer.confluent.io/tutorials/firewall-splunk/confluent.html), offering a heightened level of vigilance against unauthorized access attempts. Its capacity to promptly detect and counter [distributed denial-of-service (DDoS) attacks](https://developer.confluent.io/tutorials/ddos/confluent.html) further solidifies its role as a safeguarding mechanism. Additionally, analyzing and responding to [Secure Shell (SSH) attacks](https://developer.confluent.io/tutorials/SSH-attack/confluent.html) provides an additional layer of defense against potential breaches and underscores its indispensable contribution to cybersecurity frameworks.

> **CONCLUSION**

In this article, you explored how stream processing can enhance your organization's data privacy and security. You saw how stream processing enhances privacy and also supports the detection and response to potential threats. Moreover, you examined cases from various industries that highlight the pressing need for heightened privacy and security measures. Specifically, you delved into how stream processing serves as a pivotal tool in enhancing security and privacy within these sectors.

RisingWave provides an open-source distributed SQL database explicitly crafted for stream processing, aimed at streamlining the development of real-time applications while minimizing complexities and costs. With a PostgreSQL-like experience, RisingWave is optimized for distributed stream processing. [RisingWave Cloud](https://www.risingwave.com/cloud/) extends these advantages further. Check out [RisingWave](https://risingwave.com/database/) to see how it provides a seamless platform for cloud-based stream processing by removing the burdens associated with infrastructure deployment and maintenance.

> **About RisingWave Labs**

RisingWave is an open-source distributed SQL database for stream processing. It is designed to reduce the complexity and cost of building real-time applications. RisingWave offers users a PostgreSQL-like experience specifically tailored for distributed stream processing.

Official Website: [https://www.risingwave.com/](https://www.risingwave.com/)

Documentation: [https://docs.risingwave.com/docs/current/intro/](https://docs.risingwave.com/docs/current/intro/)

Tutorial：[https://tutorials.risingwave.com/](https://tutorials.risingwave.com/)

Slack：[https://risingwave-community.slack.com](https://risingwave-community.slack.com)

GitHub：[https://github.com/risingwavelabs/risingwave](https://github.com/risingwavelabs/risingwave)

LinkedIn：[linkedin.com/company/risingwave-labs](http://linkedin.com/company/risingwave-labs)