Canonical and Google Cloud have launched Ubuntu Pro on Google Cloud. This is a new Ubuntu offering available to all Google Cloud users. Ubuntu Pro on Google Cloud reportedly allows instant access to security patching covering thousands of open source applications for up to 10 years and critical compliance features essential to running workloads in regulated environments.

Google Cloud has long partnered with Canonical to offer innovative developer solutions, from desktop to Kubernetes and AI/ML. In the vein of this collaboration, Google Cloud and Canonical have created a more secure, hardened, and cost-effective devops environment; Ubuntu Pro on Google Cloud for all enterprises to accelerate their cloud adoption.

“Enterprise customers are increasingly adopting Google Cloud to run their core business-critical and customer-facing applications,” says June Yang, VP and GM, compute, Google Cloud. “The availability of Ubuntu Pro on Google Cloud will offer our enterprise customers the additional security and compliance services needed for their mission-critical workloads.”

Ubuntu Pro on Google Cloud is a premium version of Ubuntu focusing on enterprise and production use. It provides developers/administrators with a secured devops environment, addressing Security, one of the fundamental pillars for all IT systems. It is based on standard Ubuntu components, but comes with a set of additional services activated out of the box, including:

Patching of high and critical CVEs for Ubuntu’s universe repository, which covers over 30,000 packages, including Node.js, MongoDB, Redis and Apache Kafka, to name a few.
A 10 year maintenance commitment (For 18.04 LTS onwards. The maintenance period for Ubuntu Pro 16.04 LTS is 8 years.)
Live kernel patching, which offers VM instances increased security and higher uptimes.
Officially certified components to enable operating environments under compliance regimes such as FedRAMP, HIPAA, PCI, GDPR, and ISO.
Features to be available in H2 2021: Certified FIPS 140-2 components; security dashboard for Security Command Center, Managed Apps and more.
All the standard optimisations and security updates included in Ubuntu.
Ubuntu Pro for Google Cloud at work
Gojek has evolved from a ride-hailing company to a technology company offering a suite of more than 20 services across payment, e-commerce, and transportation. Through their applications, they’re now serving millions of users across Southeast Asia.

“We needed more time to comprehensively test and migrate our Ubuntu 16.04 LTS workloads to Ubuntu 20.04 LTS, which would mean stretching beyond the standard maintenance timelines for Ubuntu 16.04 LTS. With Ubuntu Pro on Google Cloud, we now can postpone this, and in moving our 16.04 workloads to Ubuntu Pro, we benefit from its live kernel patching and improved security coverage for our key open source components,” according to Kartik Gupta, engineering manager for CI/CD & FinOps at Gojek.

To help customers get better visibility of cost and money savings, Ubuntu Pro for Google Cloud embeds a transparent and innovative approach to pricing. For instance, Ubuntu Pro will be 3-4.5% of your average computing cost, meaning the more computing resources you consume, the smaller percentage you pay for Ubuntu Pro. Customers can purchase Ubuntu Pro directly through GCP Console or Google Cloud Marketplace for a streamlined procurement process, enabling quicker access to these commercial features offered by Canonical.

On May 12, 2021 President Biden issued Executive Order 14028, also known as the Executive Order on Improving the Nation’s Cybersecurity. This EO covers a lot of ground, and like all executive orders, it instructs agencies of the U.S. Federal Government to perform specific actions. What it doesn’t do is appropriate funding or create industry mandates. That’ll come later, once some of the plenary stages of the EO are complete.

Despite this, I’m confident that EO 14028 could be as impactful to cybersecurity as GDPR was to data privacy. I’m also confident that the EO has the attention of business leaders who are trying to figure out what it means to them and their teams—DevOps professionals. While the EO has no underlying mandate to private businesses or those located outside the U.S., it does outline a reasonable set of cybersecurity best practices that can be reasonably interpreted as a call to action for greater transparency.

In this article I’m going to use the terms supplier and buyer, but I don’t mean to imply that money needs to change hands; only that there are producers of software (suppliers) and consumers of software (buyers). If there is one supplier and one buyer, the software supply chain has one link. If that supplier is also a buyer, the chain has two links. For most software, there are many suppliers; in these cases, the supply chain isn’t so much a length of links but more of a mesh of interconnected links.

The 2021 Synopsys OSSRA report showed that the average audited application had over 500 dependencies, so that mesh is quite complex—to the point where the security practices used anywhere in the mesh are often obscure, at best. It’s this obscurity that attackers can exploit, which is why one of the key tenants of the EO is transparency.

As anyone charged with operating a piece of software knows, the security testing performed by a supplier isn’t really obvious, but, as a buyer, you assume the supplier has done the right things when it comes to security. But how do you know? Do you know where the code originated from? Do you know what security targets were used by the supplier? If an attacker is actively compromising the application, how would you know?

These are all very good questions that are reflected in the EO, to varying degrees. Those questions also belie the reality that suppliers test software. Suppliers do, of course, test their software, but what testing techniques did they use and how effective are/were their tools? And, more importantly, how can you confirm that the mitigations and fixes applied to address identified defects were effective for your deployment scenario? After all, it’s not like every supplier is going to provide you with full access to their source code.

This is where things become quite actionable for DevOps teams that have embraced the principles of operational feedback mechanisms. For those needing a bit of a refresher, that’s the part of the DevOps mobius where lessons learned operating software are fed back into the next iteration of the development flow. Let’s look at two pieces of operational feedback that are mentioned in the EO: least privilege and log management.

Log management features prominently in the EO in part because logs enable an understanding of how an application is operating. If a supplier provides detailed information about the log structure and flows, it then becomes possible to build alerts in log aggregation services and SIEMs as indications of unexpected operation. That’s the proactive model the EO embraces—as opposed to forensic postmortem log analysis following a breach—but this requires the supplier to provide insights into the log data indicating that the system became compromised. Of course, to provide that type of data, the supplier must have tested their system software in a manner that allows such a log template to be created.

We’ve heard that software should be developed using principles of “least privilege” for years, but if you’ve ever been asked to run an application as root or had a supplier not detail the precise network ports required by the application, you know there is still work to be done there. The EO specifically calls out cybersecurity techniques like multifactor authentication and zero-trust network access as ways to achieve granular control over how applications operate.

It is, however, the coverage of Software Bill of Materials (SBOM) that could have the greatest impact for DevOps teams. In effect, the EO stated that suppliers need to tell buyers the origin of all code within an application. While the EO doesn’t specify the elements of an appropriate SBOM, it does instruct NIST to publish the minimum elements of an acceptable SBOM within 45 days of the EO. When combined with the sections on how build environments are created and managed and the security transparency elements elsewhere in the EO, it’s clear that the EO intends to raise greater awareness of how software is created, patched and maintained.

In the end, Executive Order 14028 sets a high bar for cybersecurity in general—one where everyone involved in the creation and operation of software will need to up their game. I’ve said for years that it’s impossible to patch something you don’t know you’re running, and that remains true. What must be added is that you also can’t reliably secure something when you don’t know how well each component was tested. The EO is prompting us all to take a step toward solving that problem.

A recent research study on the global GDPR Services market presented by Zeal Insider offers a detailed analysis of key market players, market revenue, market segments, share, and geographic regions. It also offers several industry trends and predictions for upcoming Eight years. The report also puts light on the current COVID-19 pandemic situations on the Global GDPR Services Market enabling the user to propose strategic growth plans and tactical business judgments. The size of global GDPR Services market is estimated to grow during the forecast period of 2020 to 2028 with a CAGR of xx% and is estimated to reach.US$ xx million by 2028, from US$ xx million in 2020.

COVID-19 Impact

The report explains the degree of COVID-19 impact on every segment under the scope of the report with its trend over the forecast period. COVID-19 pandemic situations have led to a significant slowing down in the production and manufacturing of various sectors. Strict lockdowns imposed by several governments have led to a temporary shutdown of small as well as major market players. The report study offers a useful insight that explains the impact of government policies, import & export control, disruption of the supply chain, distribution network, and halts on production activities on overall revenue generation. Also, it helps clients to make strategic decisions to overcome these pandemic conditions.

Manufacturers Information

The GDPR Services report focuses on the major players operating in the global GDPR Services market to study their market share, net sale, business strategies, top competitors, key business segments, and regional presence. The competitive landscape presented in the report will help clients to focus on the key parameters that will help them to achieve desired targets in the global GDPR Services market.

Leading companies reviewed in the GDPR Services Market‎ report are:

IBM
Veritas
AWS
Microsoft
Micro Focus
Oracle
SAP
Capgemini
Absolute Software
Proofpoint
Mimecast
Varonis
SAS Institute
Symantec
Trustwave
Trustarc
Protegrity
Talend
Informatica
Onetrust
DXC Technology
Segmentation

A research study offers a systematic estimation of the global market, comprising key growth trends and significant opportunities based on its segments which provides insights to draw effective strategies for business growth. The report bifurcates the global GDPR Services market by studying various factors into respective segments and their sub-segments. Further, the report comprises various existing, previous, and future growth trends for all segments and sub-segments for the forecast period 2020-2028. The study of segmentation outlined accurate calculations and predictions in terms of revenue and volume.

By the product type, the market is primarily split into:

Data Discovery and Mapping
Data Governance
API Management
By the end-users/application, this report covers the following segments:

GDPR Readiness Assessment
Risk Assessment and DPIA
DPO-as-a-Service
Regional Information

The regional analysis offered in the report will help clients to categorize significant opportunities of the global GDPR Services market presented in different regions and countries. The GDPR Services market is segmented based on various regions such as:

North America
Europe
Asia Pacific
Latin America
Middle East and Africa
Who will get benefit of this report?

Investors and Private Equity Firms
GDPR Services Market Providers
Suppliers and Distributors
Government and Regulatory Agencies
End users
Why should buy this report?

Provides in depth research analysis of the overall GDPR Services market. which can help save time for start-up businesses related to the GDPR Services Market.
The GDPR Services markets latest news, forecast analysis as well as the key competitors of the market are easily available with all the necessary information.
The GDPR Services report comprises of graphs, pie charts and other representations that can help the reader understand the information at a glance.
Through the GDPR Services report the manufacturers can understand the consumer behaviour, business segments as well as sell products-based information provided.
COVID-19 impact on the market and industry as well as the recovery analysis.

Report Methodology

The GDPR Services report has designed by taking in to consideration of latest trends and opportunities, potential threats, and key driving factors, as well as key revenue pockets. The study has been concluded based on thorough secondary research after studying several industry publications, government reports, company news and press releases. All these studies are backed with detailed primary studies which combinedly draw a revenue outlook for the GDPR Services market.

We at Zeal Insider aim to be global leaders in qualitative and predictive analysis as we put ourselves in the front seat for identifying worldwide industrial trends and opportunities and mapping them out for you on a silver platter. We specialize in identifying the calibers of the market’s robust activities and constantly pushing out the areas which allow our clientele base in making the most innovative, optimized, integrated and strategic business decisions in order to put them ahead of their competition by leaps and bounds.

Ojai, CA, March 02, 2021 –(PR.com)– Delphix, the pioneer in programmable data infrastructure, and GenRocket, the Test Data Automation (TDA) technology leader, have announced a technology alliance designed to fulfill the needs of enterprise customers who desire a comprehensive test data solution that improves software quality.
Uncovering software issues early in the development cycle keeps poor-quality code from moving down the pipeline where it becomes increasingly difficult to discover and expensive to fix, or worse, escapes into production. In “The Cost of Poor Software Quality in the US: A 2020 Report,” CISQ estimates a staggering $2.08 trillion was lost last year alone because of software defects, fragile legacy systems, cybersecurity failures and failed IT projects.

Enterprise applications are vast and complex and finding defects requires sophisticated test plans that cover a staggering array of use cases, transactions and functionality. To create rapid feedback loops on the quality of an application as it passes through testing stages, Agile development teams have shifted left by testing early and often. Maintaining a continuous flow of quality data to test environments is essential to ensure complete test coverage.

Delphix fills a critical gap in the DevOps toolchain with automated provisioning of production-quality test data, delivering complete, masked virtual copies of production databases to test environments, in minutes. GenRocket’s complementary capabilities provide data subsets and a patented synthetic data solution for application development and test environments.

The end-to-end test data solution ensures development environments can thoroughly exercise all data-related permutations and branches of an enterprise application, edge cases that are known to exist, and net-new database schema or functionality that have no production data yet.

“At Delphix, we believe every company has the opportunity to dramatically accelerate software release cycles and improve software quality by automating the process of delivering ephemeral, compliant, data-ready environments across the multi-cloud,” said Dan Graves, CTO at Delphix. “The combination of full data sets from Delphix with synthetic data generation capabilities from GenRocket gives our joint customers the best of both worlds when it comes to testing with data.”

“At the heart of this partnership is absolute alignment of vision for the future of test data management: A fusion of data virtualization with synthetic data generation to deliver unprecedented test coverage, quality and speed,” noted Garth Rose CEO and Co-founder of GenRocket. “We are excited to partner with Delphix to deliver best-in-class solutions for realizing that vision.”

GenRocket

GenRocket is the leader in real-time synthetic Test Data Automation (TDA), high-performance technology for provisioning test data for Agile and DevOps environments. GenRocket’s patented, groundbreaking platform accelerates test data provisioning by more than 1,000% as it improves data quality and test coverage while reducing cost and ensuring data privacy. Headquartered in Ojai, California, GenRocket operates in global markets through a network of systems integration partners and has customers in more than 10 vertical markets including financial services, insurance and healthcare.

Delphix is the pioneer in programmable data infrastructure. Delphix automates the biggest constraint in digital transformation programs – the data. Cloud, CI/CD, and AI/ML all have a voracious appetite for data and development environments. With our multi-cloud data platform, enterprises can adopt cloud 30% faster, release software 50% faster, and access 90% more data for AI/ML, while protecting personal data privacy and maintaining compliance with GDPR, CCPA, HIPAA, etc.

Cloud computing has been a sole bastion of private enterprises for the last decade or so. Not anymore. Government organisations in India and world-over have now started to venture into cloud computing in a big way, as well. Be it hosting, computing, storage, innovative e-Services or PaaS solutions, governments worldwide have started to involve and adapt themselves to cloud related technologies as part of their digital transformation. The reasons – infinite computing power, storage, security, scalability, speed and resilience.

Gartner predicts governments’ cloud spending worldwide to grow at a CAGR of nearly 17.7 per cent through 2021 and sees a two-fold rise in the public cloud adoption of governments in this period.

So, why is there a sudden push to cloud by government organisations and agencies in India and world-over?
The traditional reason everyone can think of is cost. ‘Pay-as-you-go’ model propagated by public cloud providers could have been the clincher, we all think. May be not. Let us analyse this. If the business objectives of any government could not have been thoroughly addressed by deploying their systems to the cloud, they would not have ventured into cloud in the first place. After-all governments are in the business of satisfying citizens’ needs and if that does not get accomplished securely and successfully, governments would not have turned to cloud in such vast numbers this year, regardless of the cost savings they can achieve.

So, what are the security, compliance and performance barriers that are being taken out in cloud environments, for the governments to feel safe to move to the cloud now than ever-before?

Cloud security and compliance:
Cloud security and data security in particular was a primary barrier. The major confidence-booster for governments in this regard, is the security measures that have improved tremendously in the public cloud arena. Matured techno-concepts such as private subnets, network access control lists, Web-application firewalls, reverse proxies, data encryption at rest and motion, have helped to secure the computing and storage resources of governments. Compliance standards such as FedRAMP, HIPAA, GDPR, GxP, HITRUST, ISO, SOX, PCI that are facilitated and adopted by cloud providers and cloud system integrators have helped the governments to assess the security readiness of their cloud systems in order to thwart attacks from hackers.

Local data storage:
Local data storage and related data security was another key barrier. Governments world-over wanted their citizens’ data to be stored in public clouds within their own borders. That means all the public cloud providers needed to have at least one data center in each country. They are working towards this, but this will take some time. So, for now, at least governments are looking at storing data within their regions (like APAC AWS/Azure Regions for India) and nearby availability zones, so that data does not go far from their home.

Scaling and availability:
The third major barrier was the scaling and availability of the compute and storage servers. When government systems and applications are processing millions of citizen records, the ability for the systems to scale and accommodate these high-volume requests become a critical criterion for success. With the advent of “autoscaling” feature almost all the public cloud providers are able to address the high-volume problem successfully nowadays.

Cloud compute and storage systems, aided by a technology called containerisation could now dynamically deploy additional “live” parallel compute/storage systems within seconds of anticipating such loads, in order to cater to the additional citizen demands instantly. These systems can also shut themselves off automatically, when the sudden surge in demand from citizens reduces. The “Pay-as-you-go” model also helps here as the governments are charged only for the usage of these additional systems from the time, they come alive until the time they shutdown automatically as part of the autoscaling.

In summary, we see that all the potential issues the government faced in hosting systems, applications, computing and storing data in cloud have been addressed to a point that governments world-over are comfortable moving to cloud. The additional comfort of decoupling all government systems from each other and also from other private enterprises, within the same cloud deployment, by way of the security technologies mentioned above helps from a risk mitigation standpoint for all the stakeholders. Hence it is not a surprise to see even the local and state governments moving their latest technology systems involving IoT, prediction analysis, AI/ML based data engineering, Robotic Process Automation, and blockchain to the public cloud, along with their national governments.

Overview

Ludo Partchis is a modern mobile adaptation of the classic Pachisi board game — a strategy dice game with roots in ancient India, traditionally played by royalty. This Android implementation brings the familiar gameplay of moving tokens from start to the center of the board, competing against up to three other players or AI-controlled opponents.

The app is distributed as a ready-to-compile Android Studio project and includes AdMob monetization and GDPR consent support out of the box, making it a practical starting point for developers looking to publish a monetized casual game on the Google Play Store.

Gameplay

Players roll a virtual dice and move their chips (tokens) around the board according to the traditional Pachisi/Ludo rules. The objective is to navigate all your tokens to the center finish area before your opponents. The game preserves classic rules, including the ability to send an opponent’s token back to the starting position when you land on it.

Ludo Partchis supports both single-player mode (against the computer) and local multiplayer for 2 to 4 players on the same device, reflecting how the game has been played — socially and competitively — across generations.

Key Features

• Offline play: No internet connection is required to play — the core game logic runs entirely on-device.
• Game modes: Single-player (vs. computer AI) or local multiplayer with 2 to 4 human players.
• AI opponent: A built-in computer AI handles single-player mode, described as providing a meaningful challenge.
• Save and resume: Game progress is saved automatically, allowing players to resume interrupted sessions.
• Graphics: Layered PNG assets are used for the board and token visuals, giving the game a polished traditional aesthetic.
• AdMob integration: Both Banner and Interstitial ad formats are pre-integrated using the Google AdMob SDK.
• GDPR compliance: The project includes GDPR consent handling, required for serving personalised ads to users in the European Economic Area.

Technical Details

The project is built with Android Studio and targets the Android platform. Key integration points for developers include:

AdMob Integration

AdMob Banner and Interstitial ad units are wired into the app. To publish your own version, you will need to replace the placeholder AdMob App ID and ad unit IDs in the project with your own credentials from the Google AdMob console. These values are typically defined in AndroidManifest.xml and in the relevant Activity classes.

GDPR Consent

The project includes a GDPR consent flow, which presents users in the EEA with a choice about personalised advertising. This is implemented using a consent dialog or library that stores the user’s preference and passes it to the AdMob SDK before ads are loaded. Developers should verify that the consent implementation meets current IAB and Google requirements before submitting to the Play Store, as regulations and SDK APIs in this area are updated periodically.

Save State

Game state persistence is handled within the app, allowing interrupted games to be resumed. The exact storage mechanism (SharedPreferences, SQLite, or file-based) is not specified in the source listing and should be confirmed by reviewing the project code.

Who This Is For

This project suits Android developers who want to:

• Publish a complete, polished casual game to the Google Play Store with minimal development overhead.
• Learn how AdMob Banner and Interstitial ads are integrated into a real Android project.
• Understand GDPR consent flows in a practical, working app context.
• Use the codebase as a foundation and customise branding, rules, or visuals.

The full source code and asset files are available on CodeCanyon (Envato Market). Licensing terms and the latest version details can be found on the product listing page.

Why Data Masking Performance Matters for DevOps

As organizations capture more customer data, the volume that needs to be masked before it reaches non-production environments grows proportionally. At the same time, cloud adoption, database migration projects, big-data platforms, and shorter release cycles all increase the frequency with which masked copies must be generated. A masking job that takes 18 hours may have been acceptable when releases shipped quarterly; it becomes a blocker when teams deploy daily.

The goal is to provision masked datasets that are:

• Contextually accurate — data types, referential integrity, and value ranges must remain realistic so that tests are meaningful.
• Provably de-identified — original sensitive values must not be recoverable, satisfying compliance requirements.
• Available quickly — masked copies must be ready within whatever SLA window the development pipeline requires.

How Data Masking Performance Is Measured

The most common unit cited by practitioners is rows per second. It is intuitive, but it can be misleading because row width varies significantly across tables. A table with four narrow columns processes very differently from one with 60 wide columns, even if both have the same row count. When comparing solutions or planning capacity, always qualify throughput figures with the average row size and column count of the tables being measured.

Because data masking is a batch process that reads, transforms, and writes large volumes of data, total elapsed time—not just rows per second—is the metric that determines whether a masking run fits inside an overnight or weekend maintenance window. Both metrics should be tracked.

Three Variables That Drive Masking Runtime

Regardless of the tool chosen, three categories of variables dominate masking performance: database characteristics, hardware configuration, and masking configuration. Optimizing across all three is more effective than tuning any single one in isolation.

1. Database Characteristics

The shape of the database tables being masked has the most direct impact on runtime:

• Tall tables (high row counts) — more data elements to process; runtime scales roughly linearly with row count when other variables are held constant.
• Wide tables (many columns) — even if only a small number of columns require masking, wide rows introduce I/O overhead because the full row must be read and rewritten during transformation.
• Referential integrity and dependencies — foreign-key relationships require masking to be applied in a specific order or with coordinated lookups, which can serialise otherwise parallel operations.
• Data types — large object (LOB) columns, JSON blobs, and encrypted fields each impose their own I/O and CPU costs.

Involving a subject-matter expert who understands the production data model early in the masking project helps scope realistic performance expectations and identify problem tables before the first full run.

2. Hardware Configuration

Data masking is an I/O-heavy batch workload. Most production database servers are tuned for online transactional processing (OLTP)—many small, random reads and writes. Masking is the opposite: large sequential reads followed by large sequential writes. This mismatch means that running masking directly against a production-tuned server without adjustment often yields poor throughput.

Key hardware considerations for a dedicated masking or staging server:

• Storage speed and layout — faster storage (NVMe or SSD RAID arrays with reads/writes spread across multiple disks) delivers measurably better throughput than spinning disks. Co-locating the staging server in the same data centre as the source database eliminates network latency from the critical path.
• RAM — sufficient memory allows the masking engine to buffer data in-process and reduces disk I/O round-trips. Allocating RAM to the masking engine rather than relying on OS-level disk caching gives the tool more predictable performance.
• CPU cores — parallelisation strategies (see below) are only useful if enough cores are available to run multiple threads simultaneously.

When SLA windows are tight, provisioning appropriately sized hardware for the staging tier is the highest-leverage, lowest-complexity change available. Most organisations already have access to the infrastructure needed; the key is to size it for a batch workload rather than an OLTP workload.

3. Masking Configuration

The scope and complexity of the masking rules applied directly affect runtime:

• Volume of masked columns — in a typical sensitive-data profile, masking rules apply to roughly 15–20% of tables. Stricter compliance requirements or broader data classification can push this toward 30–40% of tables, proportionally increasing runtime.
• Transformer type — different transformation algorithms have different CPU and I/O profiles:
  • Data Generators synthesise entirely new fictional values (e.g., valid-format credit card numbers, phone numbers). These are CPU-bound but require no external lookup.
  • Data Loaders / Shufflers substitute values from a predefined reference set (e.g., replacing real names with names drawn from a lookup table). These introduce additional I/O for the reference data reads.
• Transformer selection — where the business requirement can be satisfied by more than one transformer type, selecting the lighter-weight option can reduce runtime without compromising the compliance outcome.

Techniques for Improving Masking Throughput

Once the three core variables are understood and appropriately configured, there are additional architectural and tool-level techniques that can push throughput further:

Multi-Threading and Parallelisation

Running masking operations on multiple database columns or tables concurrently is the most direct way to scale throughput. Modern masking tools support configurable thread counts so that independent tables are processed in parallel, subject to referential-integrity ordering constraints. On a well-provisioned staging server, increasing thread count can reduce total elapsed time significantly, though the gains flatten once CPU or I/O is saturated.

Bulk SQL Operations

Row-by-row UPDATE statements generate substantial transaction-log overhead. Replacing individual DML statements with bulk SQL operations—set-based UPDATEs, bulk inserts, or direct-path writes depending on the database engine—reduces logging overhead and improves throughput. Some masking tools apply bulk SQL automatically based on database type and operation; others require explicit configuration.

Executing Transformations on the Database Tier

Where the masking engine can push transformation logic directly to the database server rather than pulling data across the network, transforming it on the application server, and writing it back, network round-trips are eliminated. Keeping data movement local to the database tier can be a substantial performance gain for large tables, especially over slow or high-latency network links.

Storage Architecture Alignment

As noted above, masking is a batch workload. If the staging database is configured with OLTP storage settings (small block sizes, heavy logging, synchronous commits), reconfiguring it for bulk-friendly I/O patterns—larger block sizes, minimised logging where the database engine permits—can improve throughput. This is a database-administrator-level change that requires careful review to avoid affecting production systems, but it is low-risk on a dedicated staging instance.

Slowing Down to Speed Up: The Implementation Discipline

The counterintuitive lesson practitioners learn repeatedly is that rushing the planning phase leads to slow masking jobs that are difficult to tune retroactively. Investing time upfront in the following activities pays dividends in predictable, faster runtimes:

1. Profile the data model — identify the tallest and widest tables, locate LOB columns, and map referential-integrity chains before writing a single masking rule.
2. Define the compliance scope precisely — work with legal, compliance, and data-owner stakeholders to agree on exactly which columns require masking. Avoid masking everything out of caution; every additional column has a runtime cost.
3. Right-size the staging server — specify hardware based on the actual data volume and target SLA window, not on a generic template. Document the specification so it can be reproduced or scaled.
4. Select transformers intentionally — for each column, choose the transformer that meets the compliance requirement with the lowest performance overhead. Document the rationale so future changes are deliberate.
5. Benchmark iteratively — run masking jobs on a representative subset, measure throughput, identify bottlenecks, tune one variable at a time, and re-measure. This produces a calibrated model that can be used to predict runtime for full production volumes.

Key Takeaways

• Masking throughput is affected by database shape, hardware configuration, and masking rule complexity—optimise all three.
• Right-size staging server hardware for a batch I/O workload, not an OLTP workload. Co-locate it with the source database.
• Use multi-threading, bulk SQL, and on-database-tier execution to push throughput higher once the fundamentals are in place.
• Faster storage—spread across multiple disks, ideally SSD or NVMe—has a disproportionate positive impact on masking runtime.
• Scope masking rules precisely; every additional column or table has a measurable runtime cost.
• Benchmark on representative data subsets early, tune iteratively, and document the configuration so results are reproducible.

This article is based on a post originally published on Security Boulevard (2018). Product-specific details relate to Imperva Camouflage as described in the original source; verify current feature availability with the vendor.

The GDPR Challenge for Most Enterprises

When the EU’s General Data Protection Regulation (GDPR) approached its enforcement date in May 2018, a significant portion of enterprises were still scrambling to understand their obligations, let alone meet them. The regulation introduced strict requirements around how personal data belonging to EU residents is collected, stored, processed, and deleted—along with substantial fines for non-compliance.

Against this backdrop, Cybric—a security platform provider with fewer than 20 full-time employees at the time—stood out by claiming to have been GDPR-ready well before the deadline. Their chief innovation officer, Mike D. Kail, attributed that readiness directly to the DevOps culture and practices the company had already embedded into its operations.

Why DevOps and GDPR Are a Natural Fit

At its core, GDPR compliance is a data-governance problem: organizations must know what personal data they hold, where it came from, where it flows, and how long it is retained. These are exactly the kinds of concerns that well-implemented DevOps practices already address.

Kail summarized the connection clearly:

“One core part of the culture of DevOps is about collaboration, sharing and communications and making sure you have automated processes in place. That is, in part, how we were able to do what had to be done for GDPR.”

Mike D. Kail, Chief Innovation Officer, Cybric

The three DevOps principles that most directly enabled Cybric’s compliance were:

• Automation: Replacing error-prone manual data-handling steps with repeatable, auditable automated workflows.
• Transparency: Maintaining clear, up-to-date records of data sources, processing steps, and the geographic location of data subjects.
• Cross-functional collaboration: Ensuring that infrastructure, development, and business teams shared ownership of data-flow decisions rather than siloing responsibility in a single compliance team.

Standard Procedure: Building Compliance Into Daily Operations

Cybric’s advantage was that many GDPR-relevant practices were already in place before the regulation was even announced. The company kept transparent records of its data sources and the corresponding geographic locations of individuals in its databases, and was continuously evaluating how to automate data-flow handling.

According to Kail, this proactive stance meant the company was analyzing how it processed data and tracking where it was going long before GDPR compliance became an external requirement. When GDPR appeared on Cybric’s radar more than a year before enforcement began, the compliance work was largely a matter of formalizing and documenting processes that were already in motion.

Kail contrasted this with the alternative approach many organizations take:

“The wrong way consists of manual, out-of-bound efforts for processes and tasks that people ultimately screw up or forget to do on a regular basis. And when that happens, you now have potential compliance issues.”

Mike D. Kail, Chief Innovation Officer, Cybric

The lesson here is straightforward: automation reduces compliance risk not just by increasing speed, but by eliminating the human-error surface area that manual processes inevitably create.

Shared Ownership Across All Teams

A recurring theme in Cybric’s approach was that GDPR compliance was treated as everyone’s responsibility, not a task delegated exclusively to a legal or security team. Infrastructure engineers, developers, and business stakeholders each understood how their work intersected with data flows and, by extension, with regulatory obligations.

Kail explained the cultural dynamic:

“Each team figures out how they can help, which goes back to the collaborative culture of DevOps. It’s everybody’s responsibility and not just one team’s concern in the company. In the case of GDPR, the fines and levies can be significant, so everybody should take this seriously and not put it off.”

Mike D. Kail, Chief Innovation Officer, Cybric

For small and medium-sized enterprises (SMEs), this model is especially practical. With a compact team, cross-functional alignment is easier to achieve than in a large enterprise where organizational silos and competing priorities can delay compliance efforts for months.

GDPR’s Long Arm: Don’t Assume You’re Out of Scope

One of the most common misconceptions about GDPR at the time was that it only applied to companies that directly store large volumes of EU customer data. In reality, GDPR’s scope is considerably broader. Any organization that processes the personal data of EU residents—regardless of whether that organization is based inside the EU—falls under the regulation.

This means GDPR applies to data collected from:

• Website visitors from EU member states who submit contact forms or download gated marketing content.
• EU-based sales prospects in a CRM system.
• EU-based vendor or partner contacts in any business system.
• EU employees or contractors in HR systems.

Cybric itself is an illustrative example. Even though the company outsourced its customers’ personal data processing, it was still required to comply with GDPR because EU residents could visit its website and submit personal information to download marketing materials.

Kail specifically flagged marketing as an area where many organizations underestimate their exposure:

“I think marketing is an area that a lot of people don’t think about too much. They are like, ‘I don’t have any personal data directly,’ but indirectly, they do have personal data and must be GDPR-compliant.”

Mike D. Kail, Chief Innovation Officer, Cybric

Lessons for SMEs: Size Is an Advantage, Not a Barrier

A common anxiety among smaller organizations is that GDPR compliance requires the same level of investment in legal counsel, tooling, and dedicated personnel as a large enterprise. Cybric’s experience pushes back against that assumption.

With fewer than 20 full-time employees, Cybric found that its small size actually made compliance easier. Fewer people means fewer silos, shorter communication loops, and faster iteration when processes need to change. The collaborative practices already embedded in DevOps workflows translated directly into an organization that could identify, discuss, and resolve compliance gaps quickly.

Kail’s advice for smaller organizations:

“As a small company, putting together a collaborative process with all of the different departments made it much easier for us. That is also the approach people have to take.”

Mike D. Kail, Chief Innovation Officer, Cybric

Key Takeaways for DevOps Teams

Cybric’s story offers practical principles that any engineering-led organization can apply to compliance challenges—GDPR or otherwise:

1. Map your data flows early and continuously. Know what personal data you collect, where it goes, and who can access it. Treat this as a living artifact, not a one-time audit deliverable.
2. Automate data-handling processes. Manual steps are compliance liabilities. Wherever possible, encode data retention, access controls, and deletion workflows into automated pipelines.
3. Make compliance a cross-functional concern. Engineering, marketing, sales, and legal all touch personal data in different ways. Compliance cannot be owned by a single team in isolation.
4. Don’t assume you are out of scope. If your product or website is accessible to EU residents, audit every data-capture touchpoint—forms, analytics, marketing automation, and third-party integrations.
5. Start now, even if you don’t have full clarity. Waiting for perfect understanding of the regulation before acting is a common and costly mistake. Incremental progress toward compliance is better than inaction.

While GDPR is now several years into enforcement and most organizations have addressed the initial compliance wave, the underlying challenge—knowing where personal data lives and ensuring it is handled responsibly—remains ongoing. For teams that have embedded DevOps principles into their engineering culture, that challenge is considerably more manageable.

The Compliance Gap: What the Surveys Found

When the EU’s General Data Protection Regulation (GDPR) took effect on 25 May 2018, most organizations were not fully compliant. Multiple independent surveys, conducted in the months before the deadline, painted a consistent picture: awareness was high, but concrete readiness was not.

The Cloud Security Alliance (CSA) and Netskope released survey results drawn from more than 1,000 respondents. Key findings included:

• 83 percent of respondents said their organization was not very prepared for GDPR.
• 85 percent had taken some steps toward compliance, but only 31 percent had a well-defined plan for meeting GDPR provisions.

A separate survey of 700 network security and IT professionals by Enterprise Strategy Group (ESG) found:

• Only 11 percent of firms were completely ready ahead of the May 25 deadline.
• 33 percent said they were prepared to disclose data breaches within the GDPR-mandated 72-hour window.
• An additional 33 percent were mostly prepared, and 44 percent were somewhat prepared for breach disclosure requirements.
• While only 10 percent had made or planned major GDPR investments, 63 percent committed to incremental investments.

Analyst firm Varonis surveyed 500 IT security professionals across the United States, UK, France, and Germany in October 2017. Their findings showed:

• 60 percent of EU respondents and 50 percent of US respondents faced serious challenges before they could achieve GDPR compliance.
• Despite that, 70 percent of represented firms believed GDPR compliance would give them a competitive advantage.

Where Is the Data? The Fundamental Visibility Problem

A core reason organizations struggled with GDPR was a basic failure to understand where their data actually lives. Compliance with regulations like the right to erasure or data portability is impossible if you cannot locate and inventory personal data in the first place.

Varonis data highlighted the scale of this internal data governance problem:

• 21 percent of organizations’ electronic folders were open to all employees — with no access controls in place.
• 54 percent of surveyed organizations’ data was classified as stale — not actively used but still stored and potentially subject to GDPR obligations.

The shift to hybrid infrastructure — where data is spread across on-premises servers, private clouds, and public cloud providers — compounded the problem. Tracing the origin of customer data and determining whether it is subject to GDPR protection becomes significantly harder in a distributed environment.

“We live in a hybrid world in which data isn’t stored entirely on-premises or completely in the cloud. The hybrid model creates additional challenges and potential risk from a security and data governance standpoint. You can’t assume your data is safe in the cloud. If your cloud environment isn’t secure, your data won’t just be in danger of being exposed to your entire organization — it could be accessible to the world.”

— Ken Spinner, Vice President of Global Field Engineering, Varonis

Uncertainty About GDPR Scope Slowed Progress

Beyond data visibility, legal and regulatory uncertainty was a significant blocker. Key questions that remained unresolved ahead of enforcement included:

• Whether GDPR protections apply to EU citizens only, EU residents, or any individual physically present in EU territory at the time of data collection.
• How the EU would practically enforce fines against non-EU organisations that process EU personal data.
• Which specific technical and organisational measures would satisfy requirements like “data protection by design and by default.”

According to ESG data, fewer than one-third of surveyed organisations fully understood all of GDPR’s requirements. Legal departments were waiting for further regulatory guidance before greenlighting compliance projects — creating a bottleneck that delayed DevOps and engineering teams from doing the work they were already positioned to do.

The CSA/Netskope survey identified the three biggest compliance stumbling blocks:

• Right to erasure — cited by 53 percent of respondents as a major challenge.
• Data protection by design and by default — cited by 42 percent.
• Records of processing activities — cited by 39 percent.

The Stakes: GDPR Penalties

Non-compliance with GDPR carries significant financial penalties. Under the regulation, supervisory authorities can issue fines of up to 4 percent of an organisation’s annual global revenue or €20 million, whichever is higher, for the most serious violations. Lower-tier violations carry fines of up to 2 percent of annual global revenue or €10 million. The scale of potential fines made GDPR a board-level concern, not just an IT one.

Where DevOps Fits In the Compliance Picture

DevOps practices offer genuine capabilities for building and sustaining GDPR compliance — but only after legal and business stakeholders define what compliance actually requires for their specific context. The bottleneck in 2018 was not technical; it was organisational.

Once requirements are defined, DevOps teams can contribute in concrete ways:

• Shifting compliance left: Embedding data protection requirements into the design phase of new applications, rather than retrofitting them after deployment.
• Automated data discovery and classification: Using tooling to continuously scan and classify personal data across hybrid environments, reducing manual audit effort.
• Pipeline controls: Adding automated checks to CI/CD pipelines that flag code or infrastructure changes that could violate data protection policies before they reach production.
• Audit logging and monitoring: Implementing automated audit trails to support both internal governance and regulatory reporting requirements.
• Handling data subject requests at scale: Automating responses to rights requests (access, erasure, portability) — critical for large organisations with millions of data records.

“DevOps can assist companies in complying with regulations like GDPR by ensuring that key regulatory requirements are part of the initial design criteria, and taken into account when applications are updated. GDPR manual methods of enforcing data policy aren’t realistic solutions because large organisations could have millions of files. DevOps can support GDPR requests with processes and solutions that ensure personal data that falls under the new regulations is protected and used appropriately.”

— Ken Spinner, Vice President of Global Field Engineering, Varonis

Key Takeaways for DevOps and Engineering Teams

The 2018 GDPR experience highlighted a recurring pattern in compliance initiatives: technical teams are often ready and able to execute, but they are blocked by organisational, legal, or budget decisions made upstream. For DevOps engineers, the practical lessons are:

• You cannot protect data you cannot find. Data inventory and classification are prerequisites to any compliance programme, and automation is essential at scale.
• Hybrid environments require deliberate governance. Assuming cloud providers handle data security on your behalf is a compliance risk.
• Compliance is not a one-time project. GDPR requires ongoing processes — continuous monitoring, regular audits, and repeatable workflows for handling data subject requests.
• Cross-functional alignment is non-negotiable. Legal, security, product, and engineering teams must define requirements together. DevOps cannot automate what has not been defined.

GDPR enforcement has continued to evolve since 2018, with regulators issuing fines and guidance that have clarified many of the early ambiguities. Organisations building compliance programmes today have more precedent to draw on — but the fundamental challenge of data visibility and cross-team coordination remains.