When technology just works, it’s easy to trust. But too often, we place our trust in technology that doesn’t deserve it. When we do this with technology to provide healthcare, we put the safety of patients and the security and reliability of our global healthcare system at risk.

The institutions that make up our global healthcare system also place their trust in cybersecurity measures and technology to keep their systems running and repelling the unceasing wave of attacks they face. We often hear about the institutions that succumb to cyberattacks, but we don’t read much about the institutions that have been successful at defending their digital perimeter, and ultimately protected their ability to treat and protect patients.

Why were some institutions successful and others not as much? What cybersecurity capabilities made the healthcare institution resilient from these attacks? As we covered in our previous post on how healthcare can strengthen its cybersecurity resilience, establishing visibility and antifragility practices are necessary to repel attackers and build trust in the security and reliability of the technology our global healthcare system relies on.

As we continue our series on healthcare and cybersecurity, we’ll focus next on establishing sustainable visibility mechanisms so cybersecurity teams working in our global healthcare system can secure and preserve patient care and safety.

Visibility is akin to a patient taking 20 different medications but their doctor only knows about 10 of them. That’s the current state of many healthcare organizations’ relationship to the technology used on their networks and systems: They aren’t always aware of what technology is in use, whether it has vulnerabilities, how serious those vulnerabilities are, or how one weakness could lead to another or cascade out of control.

Visibility maps those technology assets (including the software, hardware, configurations, protections, who built it, and where the component parts come from) and help prioritize the importance of those assets in keeping systems and networks secure, no matter how complex the healthcare organization’s use of technology might be.

Building effective visibility

Building visibility into technology assets requires a structured approach. As mentioned in the first blog in this series, using a guide like the NIST Cybersecurity Framework (CSF) provides an important mental model we can use to get a clear picture of the strengths and weaknesses of an organization’s cybersecurity risk profile, and how it applies to the assets the organization seeks to protect.

Cybersecurity author and leader Sounil Yu’s book Cyber Defense Matrix provides an important interpretation of the NIST CSF framework that can help us better understand what visibility we have into technology assets and how they are protected. Visibility is about more than just seeing all assets we have all at once. It’s also about understanding which assets are mission-critical and must be protected at all costs versus which ones are perhaps important but less crucial overall. Without the right balance, we end up trying to protect everything while not protecting what matters enough. This is where structural awareness comes in.

Structural awareness, or the conscious understanding and state of an organization’s assets, is established as one implements controls captured in the NIST CSF functions Identify and Protect. These controls focus on identifying assets, their bill of materials, their creators, their dependencies on other assets, the protections and vulnerabilities they have, and the threats they face. While visibility helps create an accurate map of technology assets, structural awareness builds on that visibility and answers questions such as, “What are the ways the threat actor could compromise that asset? What could be lost? How would it most likely happen? Would I know?”

Yu says that structural awareness is efficiently achieved with the help of automation, and is not as people-intensive as many practitioners, decision-makers, and executives might think. If a cyberattack is like an explosion, cybersecurity teams’ structural awareness helps them understand how the explosion could happen before (or ‘left of’) it goes ‘boom’.

It’s also important to remember that while structural awareness is related to situational awareness in some ways, they are different concepts. Situational awareness refers to mechanisms that are used to detect and respond to an event. Structural awareness mechanisms are ‘left of boom,’ or mechanisms that protect your assets so that events don’t happen. The goal is to stay ‘left of boom’ and avoid being ‘right of boom’. Being proactive in your protections and being ready to respond in either case is really important, nonetheless.

Thinking about assets in terms of users, devices, networks, applications and workloads and data (asset classes), and adopting mechanisms to discover assets of each type through visibility controls suggested by the NIST CSF, will lead to developing structural awareness. Structural awareness can help avoid the boom, but if the boom does happen, it can also help shape the situational awareness needed to react. The Cyber Defense Matrix is an ongoing project, and you can read more about it (and contribute to it) here.

Framing asset discovery for resilience

Cybersecurity teams must map out their organizations’ most critical healthcare services and systems that support them, but that’s easier said than done. Asset discovery can feel overwhelming. Trying to find, count, and audit the hardware, software, users, and data down to the component across even a small part of one’s technology footprint can feel like pushing a dead car up a steep hill. The way to make this easier is to prioritize this effort in the parts of the business where the impact of a quality or safety issue has the potential to create the most harm to the organization and those who depend on it.

A smart place to begin the process to find and evaluate the measurements that an institution uses to monitor those processes.

Healthcare providers: Start with the quality and safety measurements which must be submitted to various regulatory agencies in order to maintain licenses to operate.

• Health insurers: Look at the performance improvement metrics used to ensure subscriber benefits, quality requirements, and legal mandates are being met.
• Health IT services: Use service level agreements for measuring contract compliance with things like uptime, recovery time and point objectives, and response turnarounds.
• Life sciences organizations: Begin with the relevant Good Practice definitions and the metrics used to monitor quality and safety levels of products and services.

When analyzing the types of technology unique to healthcare – like network-connected medical devices such as infusion pumps, implantable pacemakers, ventilators, EKG equipment, and MRI machines – it’s important to ask what are the immediate safety hazards if there was a cyberattack. Not sure how to answer that question? The International Medical Device Regulators Forum produced a risk categorization model that could be helpful in framing that response. Operational technology, like blood bank and sample refrigerators, climate control, air handling, infection control and pneumatic tube systems, may also be used to identify and prioritize inventory efforts.

Once priorities have been set, teams can begin gathering data on critical services, the owners of those services, the systems those owners rely on, and the technology itself.

Inventory tactics on Google Cloud

Building service-technology mapping is not a one-time exercise. Organizations should take the time to automate inventory creation and maintenance, so they can maintain an up-to-date view of all the items in their environment at any given time. This is especially for organizations that have highly dynamic environments.

Google Cloud provides best practices on discovering and cataloging assets in its Cloud Architecture Center. Implement controls found in the Identity and Protect categories using the NIST Framework & Google Cloud technical paper. Automation is your friend when building and maintaining a complete and accurate inventory. Google Cloud Asset Inventory and Security Command Center can be used to inventory a variety of resources running in Google Cloud. Cloud Build, Google Cloud’s CI/CD platform, implements SLSA 1 and provides a trustworthy audit of software artifacts deployed through a managed pipeline. Cloud DLP inventories and labels data stored on Google Cloud Storage and services like BigQuery.

Meanwhile, a number of Google Cloud Marketplace partners have solutions that can help build a comprehensive inventory, and innovation continues to improve our technological options. One emerging field of security technology, called attack surface management, helps discover previously-unknown assets. Finally, our next blog on resilience discusses how to use a software bill of materials (also known by the breezy acronym SBOM) to gain visibility and structural awareness into applications.

It’s important to remember that when starting down this path, the goal is not to gain 100% visibility into every single component on every single device attached to every network supporting every service running. Success or failure is not achieved when the inventory process has reached an arbitrarily-determined ‘percentage complete.’ We want to prioritize expanding visibility and developing structural awareness on assets where safety and quality are at risk, so we can improve their resilience.

And finally, we feel it’s important to emphasize that in the past, healthcare industries have focused mainly on protecting the confidentiality of data. While that’s important, we must evolve security programs beyond protecting confidentiality as its primary (and some cases, only) focus.

To be resilient, we must design and build cybersecurity capabilities that deliver safety, integrity, and availability of the technology that cares for patients directly. As we gain visibility into the technology we depend on to keep us healthy, we improve our understanding about which parts of it we can trust, and which parts we can’t. Improving visibility is an important early step on our path to resilience.

The future of AI is better AI—designed with ethics and responsibility built in from the start. This means putting the brakes on AI-driven transformation until you have a well-functioning strategy and process in place to ensure your models deliver fair outcomes. Failing to recognize this imperative is a threat to your bottom line. The following post provides a simple framework to follow to keep your business on the right track as you place more trust in algorithms. 

AI is inherently sociotechnical. AI systems represent the interconnectedness of humans and technology. They are designed to be used by and to inform humans within specific contexts, and the speed and scale of AI means that any lack of responsibility—such as bias, safety, privacy, scientific excellence etc—will also replicate at that same speed and scale. Without ethics and responsibility built in by design, AI systems lack the critical “inputs” or societal context that enable long term success. 

Lawsuits stemming from AI systems that are biased towards certain groups are stacking up. In August 2020, IBM was forced to settle a lawsuit with the city of Los Angeles for misappropriating data it collected for its weather channel app. Health services company, Optum, is being investigated by regulators for creating an algorithm that allegedly recommended that doctors and nurses pay more attention to white patients than to sicker black patients. And Facebook, which granted Cambridge Analytica, a political firm, access to the personal data of more than 50 million people, is buried in legal work.  Google has also run into its share of issues with algorithms making egregious mistakes. 

While lawsuits are real, the foundational reason ethical AI is critical to your bottom line is trust. Without it, increasingly, consumers will ignore you and choose a brand they do trust. Research from Kantar, which runs one of the largest global brand equity studies (4 million consumers, 18,000 brands, across 50 markets), revealed that almost 9% of a brand’s equity is driven by corporate reputation, of which responsibility is a key attribute. Over the last decade, the importance of responsibility to consumers in relation to making brand choices has tripled. 

The study stated brands perceived to be among the world’s most trusted and responsible shared three crucial factors that proved particularly important for building consumer trust and confidence, even when a brand might be new to a market. These are:

• Honesty and openness
• Respect and inclusion
• Identifying with and caring for customers

Brands that develop these associations more strongly tend to outperform their competitors in defending and growing their brand value.

Technology and business leaders need to focus on four areas to accomplish a well-functioning ethical AI strategy. Lopez Research refers to this group of tasks as SEED, which stands for security, ethics, explainability, and data (SEED). Each of these topics could be an article in itself, but this post will define several essential components. 

SECURITY (S)  

It might not seem obvious, but a robust AI strategy requires an embedded security strategy. Companies should look for hardware-level security in components such as GPUs and CPUs. IT leaders should build software security into models to minimize attacks such as poisoning, evasion, deepfakes, backdoors, and model extraction. The threat of adversarial data poisoning attacks machine learning models by maliciously introducing inaccurate data designed to corrupt the model’s ability to be accurate. Another security threat is model extraction, also known as model cloning, where a hacker finds a way to either reconstruct a black-box machine learning model or extract the training data. The first line of defense against all security attacks is to design security at the outset, but the next best step is to frequently test models to ensure they are operating as planned. Business leaders, data science experts, and IT leaders must work together to regularly review the outcomes of AI models.

ETHICS (E)

Today, organizations must understand that ethics should be designed into the solution at its outset. The ethics process starts with defining the potential positive and negative outcomes of the model that your business is creating. Once the team has evaluated potential harmful effects, which means unpacking the systems, beliefs, power hierarchies and dynamics that interconnect with the technology, it’s your responsibility to eliminate or minimize the impact of these outcomes. It’s also critically important to review the impact of models in production and shut down models demonstrating issues. An example of this was the public beta release of the Tay chatbot that Microsoft deployed and rapidly shut down because it propagated negative biases. 

Yet, many organizations aren’t taking this action. The FICO study revealed that 93% of companies said responsible AI was critical for success but only 33% of these companies were measuring AI model outputs to ensure these models were operating as expected (measuring for model drift). Another survey by Pew Research revealed that 68% believe that ethical principles focused primarily on the public good will not be employed in most AI systems by 2030. 

Regulations may turn this tide, regardless of whether organizations plan to adopt an ethical AI framework. Laws governing the ethical use of data in AI are expected to be finalized as soon as 2022, such as the European Commission’s proposed legal framework for AI. Organizations that start with ethical use of AI in mind will be better positioned to deal with customer privacy concerns and regulatory compliance.

EXPLAINABILITY(E)

As models have become more sophisticated, it’s also become increasingly difficult to explain why a model created a specific outcome. In the FICO Responsible AI  report, 65% of respondents could not explain how specific AI model decisions or predictions are made, and only 35% said their organization made an effort to use AI in a way that was transparent and accountable.  However, it’s never been more important to clarify how AI models came to conclusions such as why a loan was denied, why a particular strategy should be implemented, and how AI selected a set of resumes to review for a position. The goal is to create an explainable AI model from the outset but many of today’s models lack this capability. Every business should review its existing models and use open-source toolkits that can be found on Github.com that support the interpretability and explainability of machine learning models. 

Keep in mind that explainability isn’t one-size-fits-all. Different stakeholders need different types of information. Much of explainability to date has focused on “opening the black box” which gets equated to information that is only useful for other data scientists. That’s important, but it doesn’t help the line of business users whose workflows AI is integrated into, or end users who deserve information about how decisions are made; or policymakers who don’t have data science backgrounds, and so on. 

DATA (D) 

An equally important item in ethics is data. Ethics starts with ensuring you have the correct data to create and update models. Three main issues include representative data, inherent biases within existing data, and inaccurate data. A critical issue that most companies miss in creating models is that current data sets frequently lack full market representation. A recent Capgemini Research Institute report revealed that 65% of executives “were aware of the issue of discriminatory bias” with these systems.

Awareness is the first step, but organizations must take action to remedy this issue. Historical data may no longer serve a company’s current needs for model creation. Historical records may contain biases against certain groups. For example, historical criminal data records show an imbalance in ethnic groups’ incarceration, which would lead to model biases. Additionally, laws and societal norms also change. Certain groups were prosecuted for sexual preference in the past, but today this information would create an inaccurate model. 

Companies have also discovered that using demographic data, a common practice in marketing, can also lead to model bias. For example, individuals that primarily used cash for transactions and others that lived in specific zip codes were at a disadvantage in banking models to determine creditworthiness. To minimize these issues, a company needs to augment its data with full representation in areas such as ethnicity, gender, age, behavioral and economic profiles.

Design AI models with a continuous feedback loop

Another, more prominent, yet tricky issue is data accuracy. As the adage says, garbage in, equals garbage out. The least appreciated but arguably the most essential component of the AI model lifecycle is ensuring the model has accurate data at all times. Inaccurate data from either poor data hygiene or data that was tampered with for security purposes can cause model failures. Organizations need to invest the time and resources to ensure they have the correct data. Data privacy is another key element that businesses must address, but the concepts of data privacy, sovereignty, and security are significant enough that we will come back to this in a separate article. 

Overall, it’s clear that while we may have an abundance of data, it most likely doesn’t represent what we want to model for the future. A successful AI strategy is an ethical AI strategy that requires the organization to be thoughtful in its model creation by ensuring it has a broad representation of accurate data and testing the outcomes to ensure the models are secure and operating as expected. 

Organizations that define an AI model lifecycle with a continuous feedback loop will reap the benefits of better intelligence. This will increasingly mean stronger, longer lasting trust with customers and staying on the right side of new laws and regulations.

Access to the cloud has advanced the distribution and consumption of financial information on a global scale. In parallel, the global financial data landscape has been transformed by an influx of alternative data sources, including social media, meteorological data, satellite imagery, and other data. Exchanges and market data providers now find they need to include these new datasets to enrich their products and compete, which has meant they now must consider cloud-based models to keep up with the demands of their customers who expect easy, quick, flexible and cost-efficient ways to consume market data.

To address these needs, today we’re announcing the general availability of Datashare for financial services, a new Google Cloud solution that brings together the entire capital markets ecosystem—data publishers, and data consumers—to exchange market data securely and easily.

Datashare helps organize third-party financial information, making it accessible and useful to market data publishers and data consumers. We open-sourced the entire Datashare solution so market data publishers can now onboard their licensed datasets to Google Cloud securely, quickly and easily, while data consumers can consume that data as a service in tools of their preference, such as BigQuery.

Three ways to distribute and consume your data

Batch data delivery

Datashare provides a batch data delivery mechanism for data publishers to deliver their reference data, historical tick data, alternative market data sources and more via BigQuery, reducing the administrative burden on data consumers to extract insights from data. 

Real-time data streaming delivery

By using this event-based data delivery channel for rapidly changing instrument prices, tick data, orders, news and others via Pub/Sub, data consumers can reliably process individual messages or rewind to a point in time to replay a prior market scenario and test model changes.

Monetizing licensed datasets

Market data publishers can onboard their licensed datasets to Google Cloud and make them available via a one-stop-shop on Google Cloud Marketplace, enabling a new sales channel to expand market reach.

Reference architecture

Check out the diagram below to see how you can share your batch and real-time data directly to your Google Cloud customers with BigQuery and Pub/Sub.

As you can see in the above reference architecture, both publishers and consumers can derive several benefits from the solution:

Benefits for data publishers

• You no longer have to maintain your own delivery and licensing infrastructure.
• You can easily package and deliver granular data products and experiments with SQL.
• You can have a solution that scales with your business as data volumes and number of customers grow.

Benefits for data consumers

• Your data is ready for analysis and machine learning (ML)— you no longer have to maintain extract, transform, and load (ETL) pipelines to load files and transform data.
• You can avoid the expense and burden of maintaining multiple copies of large data files.
• You can be more targeted with consumption of data using BigQuery queries, improving performance, and compliance, and reducing cost.

Accessing the datasets

Google Cloud has been working with multiple industry firms on innovating in the market data space. By using Datashare for publishing, data publishers can make their entire datasets available on Google Cloud. Early adopters of Datashare include firms such as OneTick and Accern. OneTick’s datasets include reference and historical futures data (that can be accessed in our console with your login). Accern’s datasets include alternative data such as market sentiment and credit analysis data (that can be accessed in our console with your login).

To make it more helpful, we partnered with Accern to create a hypothetical scenario to describe the data acquisition and analytics process step-by-step.

Accern use case 

As a sustainability analyst, you require an economic, social and governance (ESG) dataset to determine which sector is the most widely covered ESG sector by analysts, and to also identify the sector with the lowest ESG sentiment score. Now, you can discover and acquire an ESG dataset in Google Cloud.

Step 1. Navigate to the Financial Services solutions page in the Google Cloud console:

Step 2. Click a dataset, for example Accern AI-Generated ESG Insights, then review the overview details, plans and pricing, documentation and support information. To view the available pricing tiers, click ‘View All Plans’. Once you’ve decided on a tier that you would like to subscribe to, click ‘Select’, choose a billing account and review and accept the terms of service to complete the subscription. Once the steps are complete, click ‘Subscribe’ at the bottom. An overlay window will appear, click ‘Register with Accern’ to activate and complete the subscription.

Step 3. Once activation is complete, you’ll be directed to the Datashare ‘My Products’ screen. Voila! You are now subscribed to Accern’s ESG Scores dataset and can access it in your Google Cloud instance using BigQuery. To access the data, click the hour glass icon on the corresponding ‘My Products’ record that you just purchased. An overlay will present you with the details on the dataset and/or table. Click the ‘Navigate to Table’ button to navigate through to the BigQuery console.

Step 4. Now that you have access and are in the BigQuery console, it’s time to generate data insights.

^{For this example, we’ve eliminated the company identifying information that is included as part of the subscription and aggregated company ESG in a view where each row represents a day, an industry sector, a specific identified ‘ESG Issues’ (event_group and event) and the respective ‘ESG Sentiment’ per issue.}

For example, row 1 indicates that within the ‘Healthcare’ sector, there was a ‘Social – Civil Society’ issue identified and it had a negative ESG sentiment score of -15.35.

Step 5. Generate a report by exporting it to Data Studio to build visualizations and conduct additional analysis on the ESG data.

Select ‘Export’ and ‘Explore with Data Studio’.

Step 6. Build a simple/basic report.

Now that the ESG data appears in Data Studio, you can start by building a simple chart to help you understand which industry sectors have the highest volume of discussions around ESG and the overall ESG Sentiment per industry sector.

To build the chart:

• Select the chart type ‘Table’.

• Include Entity_Sector as your dimension to aggregate results by ‘Industry Sector.’
• Include Signal_ID as a measure to count the number of ESG passages identified per ‘Industry Sector.’
• Include AVG(Event_Sentiment) as a measure to display the overall ESG Sentiment per ‘Industry Sector’ across ESG Issues.

You can see sectors that are  most discussed when it comes to ESG related topics and their corresponding ‘ESG Sentiment’ scores.

Step 7. Build your final report in Data Studio.

As a next step you can further drill into the data to understand ESG data specific to each ‘Industry Sector’ and identify positive and negative ESG practices. 

Accern has built a more complex sample dashboard and made it available publicly here. You can interact with this report and play around with the data. The dashboard can help to identify material ESG insights for each sector to inform your investment and risk processes. If you have additional questions, you can reach out to Accern directly.

Discovering, accessing and analyzing licensed datasets is quick and easy. Stay tuned for more updates on new licensed datasets.

Publishing your data via Datashare

If you are a publisher of market data, alternative, or exotic data, you can use Datashare to get it published on Google Cloud Marketplace.

Start by joining the Partner Advantage program by registering for the Partner Advantage Portal and applying for the Partner Advantage Build Model engagement. Visit our getting started guide for information to get started on publishing licensed datasets in the Marketplace. Stay tuned for a future blog post about using Datashare to publish datasets in the Marketplace.

More solutions for capital markets

Check out other Google Cloud solutions for capital markets.

Google Cloud’s industry-leading controls, contractual commitments, and accountability tools have helped organizations across Europe meet stringent data protection regulatory requirements for years. This commitment to supporting the  compliance efforts of European companies has earned us the trust of businesses like retailers, manufacturers and financial services providers.

As part of our continued efforts to uphold that trust, Google Cloud was one of the first cloud providers to support and adopt the EU GDPR Cloud Code of Conduct (CoC). The CoC is a mechanism for cloud providers to demonstrate how they offer sufficient guarantees to implement appropriate technical and organizational measures as data processors under the GDPR.  

Today the Belgian Data Protection Authority, based on a positive opinion by the European Data Protection Board (EDPB), approved the CoC, a product of years of constructive collaboration between the cloud computing community, the European Commission, and European data protection authorities. We are proud to say that Google Cloud Platform and  Google Workspace already adhere to these provisions. This is the first European code approved under the GDPR; it is excellent news for the industry to have a new transparency and accountability tool that helps promote trust in the cloud. 

In addition to the CoC, Google Cloud has already been certified against internationally-recognized privacy standards such as ISO/IEC 27001, ISO/IEC 27017, ISO/IEC 27018 and ISO/IEC 27701. These certifications provide independent validation of our ongoing dedication to world-class security and privacy.

This initiative reaffirms Google Cloud’s commitment to help our customers navigate their compliance journey when using our services. To learn more about how Google Cloud can help organizations with their compliance efforts, visit our Cloud Compliance resource center.

reCAPTCHA Enterprise is Google’s online fraud detection service that leverages more than a decade of experience defending the internet. reCAPTCHA Enterprise can be used to prevent fraud and attacks perpetrated by scripts, bot software, and humans. When installed inside a mobile app at the point of action, such as login, purchase, or account creation, reCAPTCHA Enterprise can block fake users and bots while allowing legitimate users to proceed.

To provide more complete coverage for native mobile iOS and Android applications, we’re announcing the general availability of the reCAPTCHA Enterprise Mobile SDK. Designed with digital-first and mobile-first organizations in mind, the new Mobile SDK fully integrates reCAPTCHA Enterprise’s frictionless experience on end-users’ mobile devices.

Why should I use the Mobile SDK?

Unlike most web applications, iOS and Android apps run on physical devices that can provide a wealth of device telemetry to help identify fraud and bot activity. By combining both device and network signals, the new mobile SDK can better protect native mobile applications from bot attacks while unlocking the full potential of reCAPTCHA Enterprise. It provides:

• Frictionless customer experience — no picking fire hydrants from a grid
• Easy integration to your native mobile app with support for popular frameworks like Cocoa Pods and Swift Package Manager
• A regularly-updated device threat model to help stay ahead of attack evolution

Protecting against fraud across all your channels

Customers will be able to leverage the new mobile SDK to implement native iOS and Android protection against the OWASP Top 10 automated attacks common on the internet, which include fraudulent account creation, financial hijacking, and credential stuffing. This is particularly important for mobile workforces and end users who use a mobile app to access products and services. Since mobile traffic surpasses web traffic in many industries, it’s even more important to implement a comprehensive mobile app protection strategy to protect against the most prevalent attacks.

Integrating the new Mobile SDK

If you’re interested in learning more about how to integrate the new Mobile SDK, check out the documentation for iOS and Android. Mobile and Web integrations leverage the same easy to understand pricing for Assessments, found here.