As their name reflects, DueDil provides due diligence services ranging from customer-specific risk evaluations and selections to customer onboarding and real-time risk monitoring for leading financial services, high-growth tech and insurance companies. Founded in 2009, the company helps more than 3,000 enterprise users from over 400 clients to not only understand with whom they’re doing business, but to do so with increased efficiency and in compliance with regulatory requirements. 

Due diligence services have evolved in recent years, both because of new regulations and new technologies supplanting legacy systems and processes, many of which relied until recently on pen-and-paper workflows or exhaustive spreadsheet work. DueDil knew this technology transformation represented an opportunity to replace manual processes with automation–but it also recognized a second opportunity: to not merely process data but also activate it by connecting information in disparate IT systems and generating data-driven insights delivered at scale.  

To capitalize on this opportunity, the company built its Business Information Graph, or B.I.G., a platform that maps approximately 300 million connections among companies. B.I.G. ingests billions of data points, and is refreshed multiple times per day, to surface unique insights about business’s relationships, such as fraud risks. The results that B.I.G. drives often speak for themselves: some DueDil customers onboard partners up to 80% faster, perform risk verification up to 18 times faster, and reduce time spent on manual portfolio checks by up to 80%. 

What powers all of this transformation? Application Programming Interfaces (APIs). 

“From a go-to-market standpoint, our product is an API,” said Denis Dorval, DueDil COO, in a recent webcast, explaining that customers can directly tap B.I.G.’s resources for themselves, and build atop them for their own needs, via DueDil’s API. 

Choosing an API management platform to deliver fast, secure, and scalable APIs

To execute on their vision of connecting B2B ecosystems for better insights and efficiency, DueDil looked for a cloud provider that could fulfill several specific criteria. They needed robust management for the APIs with which their internal developers leverage different systems for new use cases and process automations, as well as for the productized API they offer to customers. They needed sophisticated analytics and abundant processing power to crunch through billions of data points. And, they needed enterprise-grade security, scalability, and agility to underpin it all. Last but not least, the company prioritized a smooth transition; DueDil did not want the user experience to suffer as it switched providers.

“The stability of Google Cloud’s Apigee API management platform and the strength of its services stood out”, said DueDil’s Engineering Manager, Robert Cicero. 

“Apigee is a resilient and agile platform, fulfilling our need to build APIs quickly, safely, and at scale,” he remarked, noting that he appreciated that many of Apigee’s API security defense tools and policies work out-of-the-box. For instance, Apigee’s JSON threat detection policies, custom policies, and authentication and authorization processes can be deployed instantly and add minimal latency, meaning DueDil can stop security threats before they enter its network while still avoiding the risk of service lags.

Today, DueDil has five internal services that facilitate business due diligence, all exposed via Apigee. They also use Apigee’s monetization feature to drive API consumption. This said, because DueDil’s go-to-market strategy is fast-paced and client-oriented, they most often use Apigee to rapidly prototype APIs for their clients, so they can understand what a specific API would look like and how it would behave. This allows DueDil, its partners, and its customers to spend more time delivering value from insights rather than getting bogged down in building backend systems. 

Moreover, Apigee made it simpler to also connect to other Google Cloud services, such as BigQuery, Google Data Studio, and Google Cloud Storage. Apigee acts as a central nervous system among systems, giving DueDil not only the ability to connect systems and automate processes but also insight and visibility into how its B.I.G. services are being used by partners and customers. 

Plus, added Cicero, “the migration to Apigee was seamless, with arguably our biggest win being that no one knew that we had switched API management providers to Apigee.”  

Leveraging APIs to provide self-service while enforcing security and governance policies

Moving forward, DueDil plans to leverage Apigee to give staff members and clients more privileges, visibility, and opportunity to create and edit apps in a self-service manner, without needing to rely on an IT department or endure long approvals processes. Harnessing APIs to open up B.I.G. and other capabilities to more teams across the company will also allow DueDil to move faster and include more people in the innovation process. Leveraging Apigee API management capabilities, DueDil also intends to dive deeper and experiment with other Google Cloud products and services, including Cloud Function, Cloud Pub/Sub, and more.

“At the end of the day, every company goes about due diligence a little differently. The only way that we at DueDil are able to provide something that is configurable and dynamic to diverse businesses is if we use platforms that can adapt, too,” said Cicero. “Apigee gives us the agility required to create and deliver for a wide variety of businesses.”

Google Cloud, today, works across banking, capitalmarkets, insurance, and payments worldwide to solve their most challenging problems. Click here to learn more about how Google Cloud Apigee API management can help you design, secure, analyze, and scale APIs anywhere with visibility and control. To try Apigee API management for free, click here.

How many times have you closed an application when you encounter the “spinning wheel of death” (a melodramatic way of saying an application that is taking too long to load)? In today’s digital economy where many organizations rely on applications as a primary source of revenue, that spinning wheel of death (or poor application performance) can mean lost users or revenue. And just about every modern application relies on APIs as the nervous system across distributed systems, third-party services and microservice architectures. While meeting the need for rapid release cycles and frequent API updates, it is also imperative for IT teams to ensure that your APIs are meeting SLOs, performance requirements, and proactively mitigating issues.

Why are synthetic monitoring tools not enough?

When thousands or even millions of users are making multiple requests to your APIs, just relying on synthetic monitoring tools (that rely on sampling or limited API availability information) is not enough for precision diagnoses or useful forensics. At the same time, monitoring every single aspect just increases your overhead and mean time to diagnosis. Apropos, API monitoring has become absolutely critical — and a fusion of art and science — for operations teams to make sure all APIs are running and performing as intended. If you are worried about monitoring blind spots or overheads, let us look at 3 key practices you can follow to stop dreading your sev1 alerts.

#1 Prioritize alerts for critical events requiring immediate investigation

• Ask any engineer on-call for a critical service and they will tell you about the overheads created by incorrectly prioritized alerts. For example let us imagine a distributed application with 20 APIs. Even if you set up basic alert monitors across latency, error, and traffic for these APIs, you end up monitoring and maintaining 60 alert definitions – which is a lot. To balance avoiding monitoring blindspots and alert fatigue at the same time, operations teams must develop a clear understanding of all events and prioritize configuring alerts for events supporting critical traffic.

• Consider quality over quantity while defining new alert conditions where each new condition is urgent, actionable, and actively or imminently user-visible. Every alert condition created should also contain intelligence that requires active engagement from a user as opposed to a mere robotic response. Apigee’s API monitoring allows creation of alert conditions based on metrics or logs while providing actionable information (Ex: status code, rate etc.,) and playbooks for diagnosi
• In today’s multilayered systems, one team’s symptom (“what’s broken?”) is another downstream system’s cause (“Why?”). Even if some events are not suitable for actionable alerts, a failure needs to create an informational broadcast to a downstream system to mitigate the impact of the upstream dependency. In such cases, investing in automating alerts, grouping multiple incidents in notification channels, and incident tracking. For example, Apigee lets you integrate and group your alert notifications in channels like Slack, Pagerduty, webhooks etc.,
• Modern production systems are ever-evolving where an alert that’s currently rare might become frequent and automatable. Analogous to ticket backlog grooming, alert policies need to be reviewed periodically to make sure new conditions are identified and existing alerts are refined with new thresholds, prioritization and correlation. Controls like Advanced API Ops leverage AI and ML to detect anomalous traffic differentiated from random fluctuations to help define accurate alert definitions

Check out some examples of alerts here

#2 Isolate problem areas quickly with dashboards

Google’s Site Reliability Engineering book presents the case for efficient diagnosis by building dashboards that answer basic questions about every service, normally including some form of the four golden signals — latency, traffic, errors and saturation. But at the same time, capturing just these golden metrics at different levels of granularity can quickly add up. Like all software systems, monitoring can become an endless pit of complexity, complicated to change and a burden to maintain. In the same book, the most effective direction to create a well-functioning standalone system is to collect and aggregate basic metrics, paired with alerting and dashboards.

If you are running a large scale API program with a dedicated team to monitor your APIs, you can leverage the out-of-the-box monitoring dashboards in your API Management solution (like Apigee’s API monitoring) to gather real-time insights into your API performance, availability, latency, and errors. In other cases, you can use solutions like Cloud Monitoring that can provide visibility across your full application stack where individual metrics, events, and metadata can be visualized in a rich query language for rapid analysis. Leveraging a single system for your application stack provides observability in context and can reduce your time spent navigating between systems (Apigee customers can use Cloud Monitoring by default or integrate with other systems using Cloud Monitoring API)

Even after you collect and aggregate the metrics, it is important to have impactful data visualizations to quickly understand the issue and identify correlations during diagnosis. In data visualizations as well, focusing on too many dashboards creates a steep learning curve and increases mean time for every diagnosis. For example, Apigee API Monitoring provides the following visualizations as a standard to balance simplicity and efficiency:

• Timeline view to diagnose traffic (in 1 min intervals), error rates (4xx and 5xx across traffic) and latencies (50th, 90th, 95th and 99th percentiles)
• Pivot tables of metrics and attributes for all API traffic, to help compare activity across different metrics
• Treemaps of recent API traffic by proxy to get a snapshot of incidents, error rates and latencies

#3 Incorporate distributed tracing into your end to end observability strategy

Modern application development accelerated the adoption of technologies and practices like cloud, containers, APIs, microservice architectures, DevOps, SRE etc. While this increases release velocity, it also introduces complexity and more points of failure in an application stack. For example, a slow response to a customer request spans across multiple micro services owned (and monitored) by various teams who might not observe any individual performance issues. Without an end-to-end contextual view of a request, it is nearly impossible to isolate the point of high latency.

In such cases, distributed tracing is the best way for DevOps, Operations and SREs to get answers to questions such as service health, root cause of defects, or performance bottlenecks in a distributed system. Organizations should invest in instrumenting their distributed applications using open source standards such as OpenCensus and Zipkin. Using tools like Cloud Trace with a broad platform, language and environment support can help easily ingest data from any source — open instrumentation or proprietary agents.

While distributed tracing helps in narrowing the issue to a given service, in some cases you might need further context to pinpoint the root cause. For example: Even if you have isolated the source of a performance issue to an API proxy, it is still a tedious process to identify the right bottleneck among multiple policies being executed. Tools like Apigee Debug enable you to zoom into an API proxy flow and probe the details of each step to see internal details like policy executions, performance issues, and routing etc.,

As soon as a request starts to span across a handful of microservices, tools like distributed tracing and Debug will become crucial elements of your monitoring strategy. When every service in a distributed system emits a trace, the amount of data can quickly become overwhelming leading to the classic “needle in the haystack” problem. In such scenarios, it becomes vital to ask the right questions and choose between head-based sampling (randomly selecting which traces will be sampled for analysis) and tail-based sampling (observe all trace information and sample the traces with unusual latency or errors) based on application complexity

Implement effective API monitoring in Apigee

Apigee’s API monitoring (based on metrics exposed by the internals of the system) capabilities work with your existing monitoring infrastructure to help reduce mean time to diagnosis and increase application resiliency. Specifically, operations teams can leverage

• Monitoring dashboards to gain in-depth insights into API availability and performance metrics.
• Debug to precisely diagnose with deeper insights into an API proxy flow without toggling multiple tools.
• Alerts and notifications to gather contextual Insights, facilitate customization, and grouping with first class integrations to various tools (like Slack, PagerDuty, email and support for webhooks).
• Best of Google technologies such as Data Flow, Pub/Sub, Stackdriver, Bigtable and BigQuery to handle massive volumes and complex metrics at scale

Using Apigee’s API monitoring will help you maintain high application resiliency with comprehensive controls to reduce mean time to diagnosis and resolution. Get started with Apigee today or explore Apigee’s API monitoring for free here. Check out our documentation for additional information on API monitoring.

While many organizations are driving digital transformation by migrating to the cloud, there are some industries, geographies, and use cases that require a different approach to cloud modernization. Regulated industries such as healthcare, insurance, pharmaceutical, energy, telecommunication, and banking have stringent data residency and sovereignty requirements. Other industries need to meet local data processing requirements, while others require real-time data processing with sub-millisecond latencies, for example to detect defects on manufacturing lines. These use cases demand a combination of edge, on-premises and cloud services for their infrastructure.

With these requirements in mind, Google Cloud launched Google Distributed Cloud powered by Anthos to extend the power of Google Cloud infrastructure and services to the edge (or closer). The underlying infrastructure for this service comes in two variants: a 42U rack filled with compute, storage, and networking devices called Google Distributed Cloud Edge Rack and a 1U appliance called Google Distributed Cloud Edge Appliance.

In this blog post, we discuss the Google Distributed Cloud Edge Appliance and how manufacturing, retail, and automotive industry verticals can use it to address common use cases.

How the appliance works

But first, let’s talk about the Google Distributed Cloud Edge Appliance itself.

Google Distributed Cloud Edge Appliances comprises two components: (1) Distributed Cloud Edge infrastructure and (2) the Distributed Cloud Edge service.

The Google Distributed Cloud Edge service runs on Google Cloud and serves as a control plane for the nodes and clusters running on your appliance. In order to perform remote management of the appliance and to collect metrics, the Distributed Cloud Edge service must be connected to Google Cloud at all times, allowing you to manage your workloads on the edge hardware through the Google Cloud Console. For customers who can’t be connected at all times for data residency or sovereignty reasons, we highly recommend that the appliance be connected to the cloud at least once a month to allow for needed security patches and updates.

Google Distributed Cloud Edge Appliances come with built-in network ports that provide connectivity to the control plane via the internet, Cloud VPN, or Dedicated Interconnect, and to your on-prem network. Each Google Distributed Cloud Edge Appliance is homed to a specific Google Cloud region but it is designed to also use any public Google Cloud endpoint to communicate with the control plane in Google Cloud, allowing you to move these appliances between different geographic locations.

There are two NFS shares on each appliance; one is offline, meaning it does not transfer data to Google Cloud, and the other is online, meaning data saved to that share is synced to Cloud Storage on Google Cloud for further processing. The appliance supports Server Message Block (SMB) and Secure File Transfer Protocols (SFTP) for communication.

Each Google Distributed Cloud Edge Appliance runs Google Distributed Cloud Virtual, enabling you to build a single-node Kubernetes cluster with access to the underlying file system of the appliance. This allows you to build containerized applications on the underlying appliance hardware to address use cases in the following verticals.

Vertical use cases

Now that you understand how Google Cloud Edge Appliance is configured, let’s consider some of the industry use cases where it can provide unique value.

Manufacturing

In the manufacturing industry, quality control and safety is a crucial factor. Businesses need to ensure products are manufactured to the highest standards to remain competitive in their markets, to retain customers, and to keep factory workers safe. To do this, manufacturers need real-time data about the products being manufactured on the production lines, ensuring quality control and gaining a real-time view of where people are on the factory floor.

In manufacturing environments, Google Distributed Cloud Edge Appliance can be used to detect hazards or manufacturing defects in real-time. Figure 1 is a reference architecture for a hazard detection solution running off a Google Distributed Cloud Edge Appliance on a factory floor.

In this architecture, cameras on the factory floor stream live video into the Google Distributed Cloud Edge Appliance. Depending on the number of cameras and appliances, cameras could be split or mapped to different appliances. This architecture makes it possible to initially transfer video data to Google Cloud using an online NFS share. Once in Google Cloud, you can use AutoML to train and build models that can be used as part of the hazard detection solution.

With these trained models, the cameras can stream video data into the appliance using the real-time streaming protocol (RTSP). You can then use AutoML inference to analyze the real-time video streaming data.

For example, in this reference architecture, if an individual comes too close to the fork lift, a function is triggered by the microservices running on the edge appliance that pushes a notification either to a messaging service, or to an enterprise resource planning tool. This alerts factory managers to factory floor hazards in real time so they can take corrective action.

You can also review messages and videos later on for preventive planning purposes, or push streamed videos to Cloud Storage for archive, to use the appliance’s storage space more efficiently.

Data transfers to Google Cloud can be done over Google Cloud Dedicated Interconnect, or VPN between the region and your site. This connectivity also allows you to send the appliance’s control-plane network traffic to the region.

You could also use the reference architecture in figure 2 for a product anomaly detection solution running off a Google Distributed Cloud Edge Appliance on a factory floor or manufacturing line. In this instance, machine learning models are trained to detect anomalies on finished products before final packaging.

Retail

In the retail industry, the Google Distributed Cloud Edge Appliance reference architecture in Figure 2 enables a number of transformative capabilities for retail operations, including:

• contactless checkout
• product scans
• mobile-scan-bag
• cashierless checkout
• unattended retail shops
• visual check-out monitoring

It does all this within a retailer’s facilities with the low latency and high throughput you need to process data locally, so you can obtain actionable insights from your data.

Or, you could use Google Distributed Cloud Edge Appliance at the edge to overhaul store management operations, for example, monitoring store occupancy, queue depth and wait times, detecting slips and falls and out-of-stock items, or monitoring inventory compliance.

Automotive

Advanced Driver Assistance Systems (ADAS) are becoming standard in modern automobiles. To successfully build and roll out continued improvements around ADAS, the automotive industry continues to run extensive tests on ADAS systems that are built into the vehicles they manufacture. Automotive companies can use Google Distributed Cloud Edge Appliance to modernize and transform how they collect data for the ADAS systems they’re developing. For example, test vehicles contain several different sensors that generate data, which can be quickly offloaded to an in-vehicle edge appliance.

Then, within the appliance, you can deploy containerized workloads to transform sensor data, infer videos and images and detect events. This alleviates the need for operators to label all events and allows development teams to quickly gather insights from the tests.

If you want to focus on a subset of information, you can transfer specific data or the entire data payload into Transfer Appliances when vehicles return to the development center. All these systems, i.e., transfer appliances and edge appliances, work in tandem to reduce local system administration and operational costs through a cloud-based control plane.

This approach allows you to deploy, track, monitor and configure services that are running in data centers or at edge locations from the cloud. From the factories, the data can be moved offline or online into Google Cloud where you can use different storage classes and processing capabilities to further process or store the data. You can also deploy newly trained models and business rules back to the edge appliances. In all this, data transfers between the cloud and the appliance are performed using end-to-end encryption, to give you control over your data.

The reference architecture in Figure 3 shows an ADAS implementation where Google Distributed Cloud Edge Appliance is being used to gather, process and transform data at the edge in the automotive industry. It could also be applied to data capture and processing use cases in manned and unmanned vehicles. Notice how the Distributed Edge Appliance extends to the cloud by sending data there, or using other cloud-based services.

We’re just getting started

These are just a few of the use cases where organizations in the manufacturing, retail and automotive industries are using Google Distributed Cloud Edge Appliance with modern and containerized applications that are powered by Google Cloud. If you’re interested in bringing the power of Google Cloud to the edge using Google Distributed Cloud Edge Appliances to transform your business, reach out to us or any of our accredited partners.

Retail organizations constantly face financial pressure to increase sales while maintaining profit margins. Digital commerce creates new opportunities and a more competitive landscape for retailers by allowing them to reach a global customer base online, but it also exposes them to competition from larger online retailers. To be successful in this environment, retailers must not only have a strong online presence to keep up with their competition and gain market share, but also uplift the transactional customer experience to a more experiential one.

In today’s data-driven artificial intelligence-inspired business environment, organizations require complex IT infrastructure to support various functions such as prospecting, product development, marketing, and data analytics. Managing and scaling this infrastructure can be challenging, especially as needs evolve. As a result, many organizations are turning to Google Cloud as a solution for meeting their business goals, rather than simply expanding their in-house IT resources with more equipment and personnel. Specifically, retailers across the globe are betting on Kubernetes on Google Cloud to take advantage of secure, reliable, and scalable infrastructure.

Here are a few examples of worldwide retailers adapting to changing customer expectations using intelligent infrastructure solutions including Google Kubernetes Engine (GKE), the most scalable and automated fully managed Kubernetes from Google Cloud.

Haravan is a Vietnamese ecommerce platform that aims to improve the process of buying and selling products, allowing businesses to focus on creating and selling their products.

By using Google Cloud, Haravan helped small and medium-sized enterprises in Vietnam achieve double-digit growth, consistently met its 99.97% uptime commitment to clients, efficiently managed 5 times the normal amount of ecommerce activity, and facilitated the implementation of artificial intelligence-powered expansion plans.

“We have a guaranteed commitment to enable any volume of sales for clients over all channels, be it social networks, marketplaces, livestream, or website. Only Google Cloud, with the flexible autoscaling of GKE, gives us certainty to meet our guarantees even in the most massive Black Friday surges.” —Hung Le, VP of Software Engineering, Haravan

Loblaw is Canada’s food and pharmacy leader and the nation’s largest retailer. The company operates over 2,500 locations, including corporate, franchised, and associate-owned stores, and employs nearly 200,000 full- and part-time employees.

By using Google Cloud, boosted the performance of the online grocery platform, resulting in higher conversion rates and increased revenue, recovered up to 50% of Site Reliability Engineers’ time for innovation, introduced new, real-time personalization features and shopping conveniences for customers, and enhanced resiliency to protect customers and revenue.

“Moving our online grocery site to Google Cloud gave us a 4x performance increase and the capacity to handle up to three times the traffic; and we can scale up at any time.” —Hesham Fahmy, VP Technology, Loblaw

L.L.Bean is a North American retail company known for its boots and mail-order catalog, which dates back to 1912. The company has a strong online presence, with ecommerce accounting for $1 billion of its annual revenues of $1.6 billion. Like many other retailers, L.L.Bean is adopting an omnichannel sales strategy by interacting with customers through various channels including print, physical stores, its website, app, and social media.

By using Google Cloud, L.L.Bean enhanced customers’ online experience through faster page load times and access to transaction history, allowed for a focus on providing value to customers rather than managing infrastructure, and enabled the rapid release of cross-channel services by reducing development cycles.

“GKE has significantly streamlined the process of upgrading nodes and masters. By comparison, upgrading even minor releases of another container solution that L.L.Bean tested resulted in the need to rebuild that solution’s clusters four times.” —Randy Dyer, Enterprise Architect, L.L.Bean

LPP is a Polish fashion retailer established in 1991 by Lubianiec and Piechocki, whose initials make up the company’s name. LPP currently manages five clothing brands that are popular in 38 countries across Europe, Africa, and Asia, and has over 24,000 employees based in its main offices in Central and Eastern Europe. Growing demand for its ecommerce services led LPP to migrate from an on-premises setup to Google Cloud, harnessing automation to ensure great shopping experiences globally.

By using Google Cloud, LPP promoted a DevOps culture among developers through streamlined deployment of new features, provided 90% more time for engineers to work on innovative solutions instead of managing infrastructure, instantly created and updated new VMs, allowing developers to quickly launch new features, ensured a seamless online shopping experience by automatically adjusting capacity to meet demand.

“GKE enables us to deploy new features for our ecommerce sites very quickly. Previously, it took weeks to launch new instances for each brand. Today, it takes seconds: we simply launch a new machine, deploy the code, and changes are reflected automatically across our environment.” —Marek Maciejewski, Head of IT Service Operations, LPP

Noon.com, based in Riyadh, Saudi Arabia, is a local ecommerce marketplace focused on serving the Middle East. The company aims to become the top online retailer in the region, supporting the growth of a digital economy for both consumers and local businesses.

By using Google Cloud, Noon.com built its ecommerce platform to access self-managed services, allowed developers to establish a fully operational staging environment within two weeks, provided uninterrupted service to nearly four times as many daily users during busy seasons using autoscaling on GKE, used real-time data streaming on BigQuery to inform business decisions and personalize the customer experience, and achieved 99.999% availability with no downtime for planned maintenance or schema changes using a fully managed relational database.

“Google Cloud-managed services are playing a major role in enabling Noon.com customers to get their shopping done whenever they need it, without experiencing any delays or glitches, and without us having to lose sleep at night to ensure our platform is functioning as it should.” —Alex Nadalin, SVP of Engineering, Noon.com

In conclusion, the retail industry is constantly evolving and retailers must stay up-to-date with the latest technology and customer preferences to remain competitive. Digital commerce has changed the landscape of retail, allowing businesses to reach a global customer base but also increasing competition. The use of Kubernetes on Google Cloud can help retailers improve the customer experience, streamline internal processes, and make data-driven and AI-inspired decisions. By embracing these changes, retailers can stay ahead in a constantly evolving industry. Get started today with an exclusive workshop, Unlocking efficiency and innovation with Kubernetes on Google Cloud.

Cloud transformation and the adoption of modern DevOps technology presents both opportunities and challenges for IT compliance functions. With DevOps style application development, the feedback loop for developers and engineers is much tighter than with traditional application development pipelines, enabling speed and agility of application release cycles. While speedy CI/CD is a critical advantage of DevOps, it also shifts compliance left in the development timeline, and therefore puts pressure on the IT risk & compliance organization to modernize their approach to regulatory compliance as well. With the ongoing shift towards cloud technologies and DevOps, modernization of regulatory compliance is no longer optional for an IT compliance function

Compliance modernization is a broad mandate that spans the way the function is governed; the tools, technology, and analytics it uses; the number and nature of its connections to other parts of the business; verifiability and auditability of the controls’ evidence, the expectations assigned to it; and more.

Public cloud technology is becoming a core part of many industries today, and with this comes some potential risks such as cloud misconfigurations exposing intellectual property, loss of physical control of assets, skillset scarcity around cloud based security and compliance. 

Given the constantly changing risk landscape, it is critical that regulations more closely align to address these risks. As regulations and risks evolve, the aim of a modern compliance function is to help an organization stay compliant as it goes through a digital transformation. As organizations go through digital transformation, IT compliance also needs to transform — via upgrading the technology stack, modifying the business processes and most importantly re-skilling people to become cloud aware.

Today we are releasing the new paper by Google Cloud’s Office of the CISO. In the paper we reveal a new approach for modernizing your compliance approach using modern approaches and Google Cloud toolsets. Your team can leverage the paper to add value to enterprises, both by charting a course to the safe use of cloud technology and by reducing risk through the use of the public cloud.

Read the paper “Assuring Compliance in the Cloud.”

Also, review these related resources:

• “Risk Governance of Digital Transformation in the Cloud“ paper
• “Making Compliance Cloud-native” (episode 14) with Zeal Somani
• Google Cloud Compliance resource center
• Our compliance blueprints: PCI DSS on GKE and GCP FedRAMP Blueprint
• Google Cloud security best practices center