Many security leaders head into the cloud armed mostly with tools, practices, skills and ultimately the mental models for how security works that were developed on premise. This leads to cost and efficiency problems that can be solved by mapping their existing mental models to those of the cloud.

When it comes to understanding the differences between on-premises cybersecurity mental models and their cloud cybersecurity counterparts, a helpful place to start is by looking at the kinds of threats each one is attempting to block, detect, or investigate.

Traditional on-premise threats focused on stealing data from databases, file storage, and other corporate resources. The most common defenses of these resources rely on layers of network, endpoint, and sometimes application security controls. The proverbial “crown jewels” of corporate data were not made accessible with an API to the outside world or stored in publicly accessible storage buckets. Other threats aimed to disrupt operations or deploy malware for various purposes, ranging from outright data theft to holding data for ransom.

There are some threats that are specifically aimed at the cloud. Bad actors are always trying to take advantage of the ubiquitous nature of the cloud. One common cloud-centered attack vector that they pursue is constantly scanning IP address space for open storage buckets or internet-exposed compute resources.

As Gartner points out, securing the cloud requires significant changes in strategy from the approach we take to protect on-prem data centers. Processes, tools, and architectures need to be designed using cloud-native approaches to protect critical cloud deployments. And when you are in the early stages of cloud adoption, it’s critical for you to be aware of the division of security responsibilities between your cloud service provider and your organization to make sure you are less vulnerable to attacks targeting cloud resources.

Successful cloud security transformations can help better prepare CISOs for threats today, tomorrow, and beyond, but they require more than just a blueprint and a set of projects. CISOs and cybersecurity team leaders need to envision a new set of mental models for thinking about security, one that will require you to map your current security knowledge to cloud realities.

As a way to set the groundwork for this discussion, the cloud security transformation can start with a meaningful definition of what “cloud native” means. Cloud native is really an architecture that takes full advantage of the distributed, scalable, and flexible nature of the public cloud. (To be fair, the term implies that you need to be born in the cloud to be a native, but we’re not trying to be elitist about it. Perhaps a better term would be “cloud-focused” or doing the security “the cloudy way.”)

However we define it, adopting cloud is a way to maximize your focus on writing code, creating business value, and keeping your customers happy while taking advantage of cloud-native inherent properties—including security. One sure way to import legacy mistakes, some predating cloud by decades, into the future would be to merely lift-and-shift your current security tools and practices into the public cloud environment.

Going cloud-native means abstracting away many layers of infrastructure, whether it’s network servers, security appliances, or operating systems. It’s about using modern tools built for the cloud and built in the cloud. Another way to think about it: You’ll worry less about all these things because you’re going to build code on top of that to help you move more quickly. Abandoning legacy security hardware maintenance requirements is part of the win here. To put another way, security will follow in the steps of IT that has been transformed by the SRE and DevOps revolution.

You can extend this thinking to cloud native security, where some of your familiar tools combine with solutions provided by cloud service providers to take advantage of cloud native architecture to secure what’s built and launched in the cloud. While we talked about the differences between on-prem targeted threats compared to threats targeting cloud infrastructure, here are other vital areas to re-evaluate in terms of a cloud security mental model.

Network security

Some organizations practice network security in the cloud as if it were a rented data center. While many traditional practices that worked reasonably well on-premise for decades, along with many traditional network architectures, are either not applicable in the cloud or not optimal for cloud computing.

However, concepts like a demilitarized zone (DMZ) can be adapted to today’s cloud environments. For example, a more modern approach to DMZ would use microsegmentation and govern access by identity in context. Making sure that the right identity, in the right context, has access to the correct resource gives you strong control. Even if you get it wrong, microsegmentation can limit a breach blast radius.

Becoming cloud native also drives the adoption of new approaches to enterprise network security, such as BeyondProd. It also benefits organizations because it gets them away from traditional network perimeter security to focus on who and what can access your services—rather than where requests for access originated.

Although network security changes driven by cloud adoption can be enormous and transformational, not all areas shift in the same way.

Endpoint security

In the cloud, the concept of a security endpoint changes. Think of it this way: A virtual server is a server. But what about a container? What about microservices and SaaS? With software as a service cloud model, there’s no real endpoint there. All along your cloud security path, users only need to know what happens where.

Here is a helpful mental model translation: An API can be seen as sort of an endpoint. Some of the security thinking developed for endpoints applies to cloud APIs as well. Securing access, permissions, privileged access thinking can be carried over, but the concept of endpoint operating system maintenance does not.

Even with automation of service agents on virtual machines in the cloud, insecure agents may increase risks because they are operating at scale in the cloud. Case in point: This major Microsoft Azure cross-tenant vulnerability highlighted a new type of risk that wasn’t even on the radar of many of its customers.

In light of this, across the spectrum of endpoint security approaches, some disappear (such as patching operating systems for SaaS and PaaS), some survive (such as the need to secure privileged access,) and yet others are transformed.

Detection and response

With a move to the cloud comes changes to the threats you’ll face, and changes to how you detect and respond to them. This means that using on-prem detection technology and approaches as a foundation for future development may not work well. Copying all your on-premises detection tools and their threat detection content won’t reduce risks in the way that most cloud-first organizations will need..

Moving to the cloud provides the opportunity to rethink how you can achieve your security goals of confidentiality, integrity, availability, and reliability with the new opportunities created by cloud process and technology.

Cloud is distributed, often immutable, API-driven, automatically scalable, and centered on the identity layer and often contains ephemeral workloads created for a particular task. All these things combine to affect how you handle threat detection for the cloud environment and necessitate new detection methods and mechanisms.

There are six key domains where threats in the cloud can be best detected: identify, API, managed services, network, compute, and Kubernetes. These provide the coverage needed related to network, identity, compute, and container infrastructure. They also provide specific detection mechanisms for API access logs and network traffic captures.

As with endpoint security, some approaches become less important (such as network IDS on encrypted links), others can grow in importance (such as detecting access anomalies,) while others transform (such as detecting threats from the provider backplane).

Data security

The cloud is changing data security in significant ways, and that includes new ways of looking at data loss prevention, data encryption, data governance, and data access.

Cloud adoption sets you on a path to what we at Google call “autonomic data security.” Autonomic data security means security has been integrated throughout the data lifecycle and is improving over time. At the same time, it makes things easier on users, freeing them from having to define and redefine myriad rules about who can do what, when, and with which data. It lets you keep pace with constantly evolving cyberthreats and business changes, so you can keep your IT assets more secure and make your business decisions faster.

Similar to other categories, some data security approaches wane in importance or disappear (such as manual data classification at cloud scale), some retain their importance from on-prem to cloud unchanged, while others transform (such as pervasive encryption with effective and secure key management).

Identity and access management

The context for identity and access management (IAM) in the cloud is obviously different from your on-premise data center. In the cloud, every person and service has its own identity and you want to be able to control access.

Within the cloud, IAM gives you fine-grained access control and visibility for centrally managing cloud resources. Your administrators authorize who can act on specific resources, giving you full control and visibility to manage cloud resources centrally. What’s more, if you have complex organizational structures, hundreds of workgroups, and a multitude of projects, IAM gives you a unified view into security policy across your entire organization.

With identity and access management tools, you’re able to grant access to cloud resources at fine-grained levels, well beyond project-level access. You can create more granular access control policies to resources based on attributes like device security status, IP address, resource type, and date and time. These policies help ensure that the appropriate security controls are in place when granting access to cloud resources.

The concept of Zero Trust is strongly in play here. It’s the idea that implicit trust in any single component of a complex, interconnected system can create significant security risks. Instead, trust needs to be established via multiple mechanisms and continuously verified. To protect a cloud-native environment, a zero trust security framework requires all users to be authenticated, authorized, and validated for security configuration and posture before being granted or keeping access to cloud-based applications and data.

This means that IAM mental models from on premise security mostly survive, but a lot of underlying technology changes dramatically, and the importance of IAM in security grows significantly as well.

Shared fate for greater trust in cloud security

Clearly, cloud is much more than “someone else’s computer.” That’s why trust is such a critical component of your relationship with your chosen cloud service providers. Many cloud service providers acknowledge shared responsibility, meaning that they supply the underlying infrastructure but leave you responsible for many seemingly inscrutable security tasks.

With Google Cloud, we operate in a shared fate model for risk management in conjunction with our customers. We believe that it’s our responsibility to be active partners as our customers deploy securely on our platform, not delineators of where our responsibility ends. We stand with you from day one, helping you implement best practices for safely migrating to and operating in a trusted cloud.

Get ready to go cloud native

We offer you several great resources to help you prepare for cloud migration, and guide you as you review your current security approaches for signs of on-prem thinking.

Listen to our podcast series where Phil Venables, Vice President, CISO at Google Cloud, and

Nick Godfrey, Director, Financial Services Security & Compliance and member of Office of the CISO at Google Cloud, join me in a discussion on preparing for cloud migration (Podcast 1, Podcast 2). You can deepen your cloud native skills by earning a Professional Cloud Security Engineer certification from Google.

June is the month that holds the summer solstice, and some of us in the northern hemisphere get to enjoy the longest days of sunshine out of the entire year. We used all the hours we could in June to deliver a flurry of new features across BigQuery, Dataflow, Data Fusion, and more.  Let’s take a look!

Simple, Sophisticated, and Secure

Usability is a key tenant of our data analytics development efforts. Our new user-friendly BigQuery improvements this month include:

• Flexible data type casting
• Formatting to change column descriptions 
• GRANT/REVOKE access control commands using SQL

We hope this will delight data analysts, data scientists, DBAs, and SQL-enthusiasts who can find out more details in our blog here.

Beyond simplifying commands, we also recognize that it’s equally important to have more sophistication when dealing with transactions. That’s why we introduced multi-statement transactions in BigQuery.

As you probably know, BigQuery has long supported single-statement transactions through DML statements, such as INSERT, UPDATE, DELETE, MERGE and TRUNCATE, applied to one table per transaction. With multi-statement transactions, you can now use multiple SQL statements, including DML, spanning multiple tables in a single transaction. 

This means that any data changes across multiple tables associated with all statements in a given transaction are committed atomically (all at once) if successful—or all rolled back atomically in the event of a failure. 

We also know that organizations need to control access to data, down to the granular level and that, with the complexity of data platforms increasing day by day, it’s become even more critical to identify and monitor who has access to sensitive data. 

To help address these needs,  we announced the general availability of BigQuery row-level security. This capability gives customers a way to control access to subsets of data in the same table for different groups of users. Row-level security in BigQuery enables different user personas access to subsets of data in the same table and can easily be created, updated, and dropped using DDL statements. To learn more, check out the documentation and best practices.

Simple, Safe, and Smart

Beyond building a simpler, more sophisticated and more secure data platform for customers, our team has been focused on providing solutions powered by built-in intelligence. One of our core beliefs is that for machine learning to be adopted and useful at scale, it must be easy to use and deploy.  

BigQuery ML, our embedded machine learning capabilities, have been adopted by 80% of our top customers around the globe and it has become a cornerstone of their data to value journey.  

As part of our efforts, we announced the general availability of AutoML tables in BigQuery ML.  This no-code solution lets customers automatically build and deploy state-of-the-art machine learning models on structured data. With easy integration with Vertex AI, AutoML in BQML makes it simple to achieve machine learning magic in the background. From preprocessing data to feature engineering and model tuning all the way to cross validation, AutoML will “automagically” select and ensemble models so everyone—even non-data scientists—can use it.   

Want to take this feature for a test drive? Try it today on BigQuery’s NYC Taxi public dataset following the instructions in this blog! 

Speaking of public datasets, we also introduced the availability of Google Trends data in BigQuery to enable customers to measure interest in a topic or search term across Google Search.  This new dataset will soon be available in Analytics Hub and will be anonymized, indexed, normalized, and aggregated prior to publication. 

Want to ensure your end-cap displays are relevant to your local audience?  You can take signals from what people are looking for in your market area to inform what items to place. Want to understand what new features could be incorporated into an existing product based on what people are searching for?  Terms that appear in these datasets could be an indicator of what you should be paying attention to.

All this data and technology can be put to use to deploy critical solutions to grow and protect your business. For example,  it can be difficult to know how to define anomalies during detection. If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML. 

But what if you don’t know what kind of anomaly to expect, and you don’t have labeled data? Unlike typical predictive techniques that leverage supervised learning, organizations may need to be able to detect anomalies in the absence of labeled data. 

That’s why, we were particularly excited to announce the public preview of new anomaly detection capabilities in BigQuery ML that leverage unsupervised machine learning to help you detect anomalies without needing labeled data.  

Our team has been working with a large number of enterprises who leverage machine learning for better anomaly detection. In financial services for example, customers have used our technology to detect machine-learned anomalies in real-time foreign exchange data.  

To make it easier for you to take advantage of their best practices, we teamed up with Kasna to develop sample code, architecture guidance, and a data synthesizer that generates data so you can test these innovations right away. 

Simple, Scalable, and Speedy

Capturing, processing and analyzing data in motion has become an important component of our customer architecture choices. Along with batch processing, many of you need the flexibility to stream records into BigQuery so they can become available for query as they are written.  

Our new BigQuery Storage Write API combines the functionality of streaming ingestion and batch loading into a single API. You can use it to stream records into BigQuery or even batch process an arbitrarily large number of records and commit them in a single atomic operation.

Flexible systems that can do batch and real-time in the same environment is in our DNA: Dataflow, our serverless, data processing service for streaming and batch data was built with flexibility in mind.  

This principle applies not just to what Dataflow does but also how you can leverage it—whether you prefer using Dataflow SQL right from the BigQuery web UI, Vertex AI notebooks from the Dataflow interface, or the vast collection of pre-built templates to develop streaming pipelines.

Dataflow has been in the news quite a bit recently. You might have noted the recent introduction of Dataflow Prime, a new no-ops, auto-tuning functionality that optimizes resource utilization and further simplifies big data processing. You might have also read that Google Dataflow is a Leader in The 2021 Forrester Wave™: Streaming Analytics, giving Dataflow a score of 5 out of 5 across 12 different criteria.  

We couldn’t be more excited about the support the community has provided to this platform. The scalability of Dataflow is unparalleled and as you set your company up for more scale, more speed, and “streaming that screams”, we suggest you take a look at what leaders at Sky, RVU or Palo Alto Networks have already accomplished.

If you’re new to Dataflow, you’re in for a treat: this past month, Priyanka Vergadia (AKA CloudGirl) released a great set of resources to get you started. Read her blog here and watch her introduction video below!

https://youtube.com/watch?v=WRspZRG9e90%3Fenablejsapi%3D1%26

Simple structure that sticks together

We thrive to be the partner of choice for your transformation journey, regardless where your data comes from and how you choose to unify your data stack.  

Our partners at Tata Consultancy Services (TCS) recently released research that highlights the importance of a unifying digital fabric and how data integration services like Google Cloud Data Fusion can enable their clients to achieve this vision.

We also  announced SAP Integration with Cloud Data Fusion, Google Cloud’s native data integration platform, to seamlessly move data out of SAP Business Suite, SAP ERP and S4/HANA. To date, we provide more than 50 pipelines in Cloud Data Fusion to rapidly onboard SAP data.  

This past month, we introduced our SAP Accelerator for Order to Cash.  This accelerator is a sample implementation of the SAP Table Batch Source feature in Cloud Data Fusion and will help you get started with your end-to-end order to cash process and analytics. 

It includes sample Cloud Data Fusion pipelines that you can configure to connect to your SAP data source, perform transformations, store data in BigQuery, and set up analytics in Looker. It also comes with LookML dashboards which you can access on Github.

Countless great organizations have chosen to work with Google for their SAP data. In June, we wrote about ATB Financial’s journey and how the company uses data to better serve over 800,000 customers, save over CA$2.24 million in productivity, and realize more than CA$4 million in operating revenue through “D.E.E.P”, a data exposure enablement platform built around BigQuery.

Finally, if you are an application developer looking for a unified platform that brings together data from Firebase Crashlytics, Google Analytics, Cloud Firestore, and third party datasets, we have good news!  

This past month, we released a unified analytics platform that combines Firebase, BigQuery, Google Looker and FiveTran to easily integrate disparate data sources,  and infuse data into operational workflows for greater product development insights and increased customer experience. This resource comes with sample code, a reference guide and a great blog!  We hope you enjoy it. See you all next month!

https://youtube.com/watch?v=L25Vfzr2Ciw%3Fenablejsapi%3D1%26

Last July, Google Cloud launched our ambassador partnership with the Health Information Sharing and Analysis Center (Health-ISAC) and committed to working with industry leaders to better protect our healthcare ecosystem. Securing healthcare technology and data is a global challenge, and to meet it security professionals need to have better channels for sharing information and effective security practices. 

To that end, we’re pleased to announce that our relationship with Health-ISAC is now expanding to include CISOs and security leaders in Europe, the Middle East, and Africa (EMEA).  

On May 23, 2023, we’ll join the Health-ISAC on a 17-city tour across the region, starting in Zurich, Switzerland, as part of its European Healthcare Threat Landscape Tour. In each session, we will bring together experts from across the region with cybersecurity practitioners from the Health-ISAC, Google Cloud, law enforcement, and regulatory bodies.  

Our goals is to focus the discussions on several topics:

• How the threat landscape is unique to each country we visit;
• How we can better share threat intelligence; 
• What are the emerging European laws and regulations  that can affect healthcare and cybersecurity;
• How ransomware continues to evolve and the threat that poses to healthcare; organizations;
• What we can do to improve cyber incident response;
• What impact digital sovereignty can have on European healthcare;
• And which initiatives Health-ISAC and Google Cloud are launching to better protect Europe’s healthcare system.

The timing for the tour could not come at a more critical time. Ransomware and digital extortion threatens healthcare and its subsectors, with national health systems in Ireland, UK, Germany, and Spain all recently experiencing extended outages because of ransomware. Threat actors continue to target proprietary data at biotechnology and pharmaceutical firms. Meanwhile, existing regulations such as the Digital Markets Act and GDPR, and new regulations such as the NIS2 Directive (Directive (EU) 2022/2555), which seek to protect EU citizens and businesses from risks posed by the digital world, are raising the bar that CISOs and security leaders must surpass to ensure their businesses can stay compliant and able compete in the global digital economy.

“The European Healthcare Threat Landscape Tour is one of many efforts we have undertaken to build a closer community of trust around threats and cybersecurity practices that keep health organization’s safe,” said Denise Anderson, executive director, Health-ISAC.

“We all rely on these organizations to take care of us and produce therapies that keep us healthy. Delivering these engagements alongside Google Cloud helps us achieve global scale and shows off both organizations’ commitments to building a sustainable, resilient healthcare ecosystem for entire societies.” 

Each stop in the tour will provide opportunities to dive deep into these topics, grow an understanding of these challenges, create a space for collaboration, and build closer, trusted partnerships. The meetings are open to member and non-member organizations working in healthcare. Interested participants can contact the Health-ISAC through their contact form or register on their website for the nearest event.

“The Google Cybersecurity Action Team’s mission is to secure customers, secure the cloud, and secure the planet (and beyond). Working with industry groups like Health-ISAC helps us achieve this mission while respecting each other and the unique challenges European health systems face. Myself and our EMEA-based Google Cloud and Mandiant security teams are looking forward to another great collaboration,” said Phil Venables, chief information security officer, Google Cloud.

At Google Cloud, we’re committed to helping build a secure and resilient healthcare ecosystem for everyone. As we hit the road with the Health-ISAC this summer, we’ll be sure to send updates on what we learn through this collaboration and how we can all work together to secure smarter in 2023 and beyond.