One of the initial hesitations in many enterprise organizations moving into the cloud in the last decade was the question of security. Significant amounts of money had been put into corporate firewalls, and now technology companies were suggesting corporate data reside outside that security barrier. Early questions were addressed, and information began to move into the cloud. However, nothing stands still, and the extra volume of data and networking intersects with the increased complexity of attacks, and artificial intelligence (AI) is being used to keep things safe.

The initial hesitation for enterprise organizations to move to the cloud was met by data centers improving hardware and networking security, while the cloud software providers, both cloud hosts and application providers, increased software security past what was initially offered in the cloud. Much of that was taking knowledge from on-premises security and scaling it to the larger systems in the cloud. However, there’s also more flexibility for attacks in the cloud, so new techniques had to be added. In addition, most organizations are in a hybrid ecosystem, so the on-premises and cloud security must coordinate.

This means an opportunity for AI to provide enhanced security. As mentioned with other machine solutions, security is a mix different AI and non-AI techniques to fit the problem. For instance, there’s deep learning. Supervised learning can be used for known attacks, while unsupervised learning can be used to detect anomalous events in a sparse dataset. Reinforcement learning classification can even be done with statistical analysis in time series, and not always require AI. That can provide faster performance in appropriate cases.

On a quick tangent, let’s talk about supervised learning and reinforcement learning. Some folks present them as different; I think of the latter as an extension of the former. “Classic” supervised learning is when input is labeled and the labels are important for the AI system, as they are used to understand and organize the data. When there are errors, humans add more annotations and labels to existing data, or they add more data. In reinforcement learning, feedback for the neural network is given as to how far the results of an iteration are from a set goal. That feedback can be put back into the system by programmers changing weights or, in more advanced systems, by the AI software doing the comparison and adapting on its own. That is a type of supervision, but I’ll admit it’s a philosophical argument.

Back on track, let’s add another complexity. In the early days of the cloud, applications were larger but still followed a similar pattern of scale-up and scale-out. Now there’s something changing both environments: containers. Simply put, a container is a piece of software that wraps around an application, it has basic services and even a virtual operating system. That allows containers to run on multiple operating systems regardless of internal application code. It also allows cloud platforms and servers to more finely control services to their clients in order to meet service level agreements (SLA’s) that provide quality performance to the end customer.

“As more applications migrate to a container architecture, it’s important for security to keep up,” said Tanuj Gulati, CTO, Securonix. “Light weight collectors can run within application containers, such as with Docker, collecting and sending relevant event logs to the more robust security monitoring applications running separately. This provides strong security in the new environments without significant burden being added to application performance.”

 In my discussion with Tanuj Gulati, he explained that they first worked in the virtual machine (VM) environment in local data centers. That provided both an understand that helped extend security to Docker, but also in integrating security between on-premises and cloud systems in a hybrid environment.

Detection V Response

Artificial intelligence is focused on detection, but a complete system must also address the response to a perceived threat. The basic system can detect attacks, and based on known problems rules can then determine responses. Unknown problems have unknown responses. Humans must be flagged to handle those questionable transactions, then feedback can be given to reinforce the system. Depending on how complex a system is created, those new rules can be incorporated into the neural network or added to a rules set.

The state of the industry, both in technology and human comfort levels, shows that there will continue to be human oversight before responses to new attacks as the predominant method in the next few years. Advances will push the security industry into more system action and then reporting, review, and adjustment by humans, but that will happen slowly. What will help is that better explainability will be required, as the deep learning “black box” will have to become more transparent.

Cloud computing and artificial intelligence are growing in parallel. The complexity of the cloud is driving the need for AI, but the complexity of AI is also creating the need for it to work better in the cloud environment with efficiency, transparency and control.

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ClearDATA®, the leader in healthcare public cloud security, compliance and privacy, today expanded their ClearDATA Comply™ Software as a Service (SaaS) compliance management product to include Microsoft’s Azure Cloud Services. With this new addition to the ClearDATA portfolio of products, more healthcare providers, payers and life sciences organizations can adopt Azure’s Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) cloud services while mitigating risk of security vulnerabilities with real-time compliance checks and automated remediation.

By investing deeply in cloud and healthcare compliance expertise, ClearDATA protects sensitive healthcare data in the cloud by strategically mapping critical standards and regulations like HIPAA, GxP and GDPR to relevant technical controls. The company’s flagship SaaS product, Comply is designed to detect and immediately remediate compliance deviations, while providing near real-time reporting with clear, auditable and visual confirmation of the users’ compliance status. This helps healthcare organizations to meet compliance obligations throughout the lifecycle of an application and frees IT assets to invest in the latest cloud technology.

“PHI is extremely sensitive and requires a sophisticated marriage of deep cloud expertise and comprehensive compliance to keep secure. Most healthcare providers and life science organizations do not have the expertise or in house capability to tackle such a complex objective,” said Suhas Kelkar, ClearDATA’s Chief Product Officer. “With Comply, healthcare organizations can leverage the power, flexibility and inherent security of the Azure cloud with even greater confidence – thus accelerating adoption of Azure and increasing the speed of innovation.”

As of today, the solution automatically configures over 70 controls across 32 of the most commonly used Azure services for sensitive patient data (PHI/PII) in healthcare including Azure Kubernetes Service (AKS), Azure Machine Learning as well as PaaS based services like Azure SQL. ClearDATA is a Microsoft Gold Partner and has offered managed security, compliance and privacy solutions on Microsoft Azure since 2015. ClearDATA will further collaborate with Microsoft to support its sensitive data workloads, with plans to add new HIPAA-eligible Azure services to ClearDATA Comply every quarter.

“As digital patient care solutions provide more data to doctors, healthcare organizations are leveraging the Azure cloud to gain insight from their data and transform to a value-based care model focused on improving patient outcomes and experience. With ClearDATA Comply’s availability in the Azure Marketplace, we’ve brought together Microsoft’s strength in cloud services and ClearDATA’s deep understanding of complex healthcare regulations to offer a solution that allows healthcare to innovate and mitigate risk,” said David Houlding, Microsoft’s Director of Healthcare Experiences.

“Platform as a Service (PaaS), machine learning (ML) and other advanced Azure services are driving rapid healthcare innovation,” said ClearDATA Chief Technology Officer and Co-Founder, Matt Ferrari. “ClearDATA sees a need to invest in our longstanding strategic partnership with Microsoft to apply our best security, compliance and privacy practices on top of them.”

The collaboration between ClearDATA and Microsoft Azure is evidenced by the value healthcare organizations such as BehaVR are realizing by implementing ClearDATA Comply for Azure. A provider of VR digital therapeutics and wellness programs for behavioral health, BehaVR uses Comply for Azure to manage and enforce compliance and security of their application. This frees their development team to focus on continued innovation in virtual reality for healthcare versus navigating how to configure a compliant cloud environment.

ClearDATA launched its Comply multi-cloud solution in December 2019 with early availability for Amazon Web Services (AWS). With its expansion to include support for Microsoft Azure, ClearDATA Comply provides a multi-cloud view of compliance within a single pane of glass.

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Much as robots have transformed entire swaths of the manufacturing economy, artificial intelligence and automation are now changing information work, letting humans offload cognitive labor to computers. In journalism, for instance, data mining systems alert reporters to potential news stories, while newsbots offer new ways for audiences to explore information. Automated writing systems generate financial, sports and elections coverage.

A common question as these intelligent technologies infiltrate various industries is how work and labor will be affected. In this case, who – or what – will do journalism in this AI-enhanced and automated world, and how will they do it?

The evidence I’ve assembled in my new book “Automating the New: How Algorithms are Rewriting the Media” suggests that the future of AI-enabled journalism will still have plenty of people around. However, the jobs, roles and tasks of those people will evolve and look a bit different. Human work will be hybridized – blended together with algorithms – to suit AI’s capabilities and accommodate its limitations.

Augmenting, not substituting

Some estimates suggest that current levels of AI technology could automate only about 15% of a reporter’s job and 9% of an editor’s job. Humans still have an edge over non-Hollywood AI in several key areas that are essential to journalism, including complex communication, expert thinking, adaptability and creativity.

Reporting, listening, responding and pushing back, negotiating with sources, and then having the creativity to put it together – AI can do none of these indispensable journalistic tasks. It can often augment human work, though, to help people work faster or with improved quality. And it can create new opportunities for deepening news coverage and making it more personalized for an individual reader or viewer.

Newsroom work has always adapted to waves of new technology, including photography, telephones, computers – or even just the copy machine. Journalists will adapt to work with AI, too. As a technology, it is already and will continue to change newswork, often complementing but rarely substituting for a trained journalist.

New work

I’ve found that more often than not, AI technologies appear to actually be creating new types of work in journalism.

Take for instance the Associated Press, which in 2017 introduced the use of computer vision AI techniques to label the thousands of news photos it handles every day. The system can tag photos with information about what or who is in an image, its photographic style, and whether an image is depicting graphic violence.

The system gives photo editors more time to think about what they should publish and frees them from spending lots of time just labeling what they have. But developing it took a ton of work, both editorial and technical: Editors had to figure out what to tag and whether the algorithms were up to the task, then develop new test data sets to evaluate performance. When all that was done, they still had to supervise the system, manually approving the suggested tags for each image to ensure high accuracy.

Stuart Myles, the AP executive who oversees the project, told me it took about 36 person-months of work, spread over a couple of years and more than a dozen editorial, technical and administrative staff. About a third of the work, he told me, involved journalistic expertise and judgment that is especially hard to automate. While some of the human supervision may be reduced in the future, he thinks that people will still need to do ongoing editorial work as the system evolves and expands.

Semi-automated content production

In the United Kingdom, the RADAR project semi-automatically pumps out around 8,000 localized news articles per month. The system relies on a stable of six journalists who find government data sets tabulated by geographic area, identify interesting and newsworthy angles, and then develop those ideas into data-driven templates. The templates encode how to automatically tailor bits of the text to the geographic locations identified in the data. For instance, a story could talk about aging populations across Britain, and show readers in Luton how their community is changing, with different localized statistics for Bristol. The stories then go out by wire service to local media who choose which to publish.

The approach marries journalists and automation into an effective and productive process. The journalists use their expertise and communication skills to lay out options for storylines the data might follow. They also talk to sources to gather national context, and write the template. The automation then acts as a production assistant, adapting the text for different locations.

RADAR journalists use a tool called Arria Studio, which offers a glimpse of what writing automated content looks like in practice. It’s really just a more complex interface for word processing. The author writes fragments of text controlled by data-driven if-then-else rules. For instance, in an earthquake report you might want a different adjective to talk about a quake that is magnitude 8 than one that is magnitude 3. So you’d have a rule like, IF magnitude > 7 THEN text = “strong earthquake,” ELSE IF magnitude < 4 THEN text = “minor earthquake.” Tools like Arria also contain linguistic functionality to automatically conjugate verbs or decline nouns, making it easier to work with bits of text that need to change based on data.

Authoring interfaces like Arria allow people to do what they’re good at: logically structuring compelling storylines and crafting creative, nonrepetitive text. But they also require some new ways of thinking about writing. For instance, template writers need to approach a story with an understanding of what the available data could say – to imagine how the data could give rise to different angles and stories, and delineate the logic to drive those variations.

Supervision, management or what journalists might call “editing” of automated content systems are also increasingly occupying people in the newsroom. Maintaining quality and accuracy is of the utmost concern in journalism.

RADAR has developed a three-stage quality assurance process. First, a journalist will read a sample of all of the articles produced. Then another journalist traces claims in the story back to their original data source. As a third check, an editor will go through the logic of the template to try to spot any errors or omissions. It’s almost like the work a team of software engineers might do in debugging a script – and it’s all work humans must do, to ensure the automation is doing its job accurately.

Developing human resources

Initiatives like those at the Associated Press and at RADAR demonstrate that AI and automation are far from destroying jobs in journalism. They’re creating new work – as well as changing existing jobs. The journalists of tomorrow will need to be trained to design, update, tweak, validate, correct, supervise and generally maintain these systems. Many may need skills for working with data and formal logical thinking to act on that data. Fluency with the basics of computer programming wouldn’t hurt either.

As these new jobs evolve, it will be important to ensure they’re good jobs – that people don’t just become cogs in a much larger machine process. Managers and designers of this new hybrid labor will need to consider the human concerns of autonomy, effectiveness and usability. But I’m optimistic that focusing on the human experience in these systems will allow journalists to flourish, and society to reap the rewards of speed, breadth of coverage and increased quality that AI and automation can offer.

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