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Yes, there are security ramifications to serverless computing

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At least one in five organizations, 21%, have implemented serverless computing as part of their cloud-based infrastructure. That’s the finding of a recent survey of 108 IT managers conducted by Datamation. Another 39% are planning or considering serverless resources. 


Photo: Joe McKendrick

The question is, will serverless computing soon gain critical mass, used by a majority of enterprises? Along with this, what are the ramifications for security? 

Existing on-premises systems and applications — you can call some of them “legacy” — still require more traditional care and feeding. Even existing cloud-based applications are still structured around the more serverful mode of development and delivery. 

That’s what many enterprises are dealing with now — loads of traditional applications to manage even while they begin a transition to serverless mode. Again, even if applications or systems are in the cloud, that still is closer to traditional IT than serverless on the continuum, says Marc Feghali, founder and VP product management for Attivo Networks. “Traditional IT architectures use a server infrastructure, that requires managing the systems and services required for an application to function,” he says. It doesn’t matter if the servers happen to be on-premises or cloud-based. “The application must always be running, and the organization must spin up other instances of the application to handle more load which tends to be resource-intensive.”  

Serverless architecture goes much deeper than traditional cloud arrangements, which are still modeled on the serverful model. Serverless, Feghali says, is more granular, “focusing instead on having the infrastructure provided by a third party, with the organization only providing the code for the applications broken down into functions that are hosted by the third party. This allows the application to scale based on function usage. It’s more cost-effective since the third-party charges for how often the application uses the function, instead of having the application running all the time.”

How should the existing or legacy architecture be phased out? Is it an instant cut over, or should it be a more gradual migration? Feghali urges a gradual migration, paying close attention to security requirements. “There are specific use cases that will still require existing legacy architecture,” and serverless computing “is constrained by performance requirements, resource limits, and security concerns,” Feghali points out. The advantage serverless offers is that it “excels at reducing costs for compute. That being said, where feasible, one should gradually migrate over to serverless infrastructure to make sure it can handle the application requirements before phasing out the legacy infrastructure.”   

Importantly, a serverless architecture calls for looking at security in new ways, says Feghali, “With the new service or solution, security frameworks need to be evaluated to see what new gaps and risks will present themselves. They will then need to reassess their controls and processes to refine them to address these new risk models.”

Security protocols and processes differ in a serverless environment. Namely, with the use of serverless computing, an enterprise’s attack surface widens. “The attack surface is much larger as attackers can leverage every component of the application as an entry point,” Feghali says, which includes “the application layer, code, dependencies, configurations and any cloud resources their application requires to run properly. There is no OS to worry about securing, but there is no way to install endpoint or network-level detection solutions such as antivirus or [intrusion protection or prevention systems[. This lack of visibility allows attackers to remain undetected as they leverage vulnerable functions for their attacks, whether to steal data or compromise certificates, keys, and credentials to access the organization.”

At this point, introducing the security measures needed to better protect serverless environments may add more cost and overhead, according to a study out of the University of California at Berkeley, led by Eric Jonas. “Serverless computing reshuffles security responsibilities, shifting many of them from the cloud user to the cloud provider without fundamentally changing them,” their report states. “However, serverless computing must also grapple with the risks inherent in both application disaggregation multi-tenant resource sharing.”

One approach to securing serverless is “oblivious algorithms,” the UC Berkeley team continues. “The tendency to decompose serverless applications into many small functions exacerbates this security exposure. While the primary security concern is from external attackers, the network patterns can be protected from employees by adopting oblivious algorithms. Unfortunately, these tend to have high overhead.” 

Physical isolation of serverless resources and functions is another approach — but this, of course, comes with premium pricing from cloud providers. Jonas and his team also see possibilities with generating very rapid instances of serverless functions. “The challenge in providing function-level sandboxing is to maintain a short startup time without caching the execution environments in a way that shares state between repeated function invocations. One possibility would be to locally snapshot the instances so that each function can start from clean state.” 

Feghali’s firm, Attivio Networks, focuses on adoption of “deception technologies” intended to provide greater visibility across the various components in a serverless stack, “as a way to understand when security controls are not working as they should, detect attacks that have by-passed them, and for notification of policy violations by insiders, suppliers, or external threat actors.”  

The bottom line is handing over the keys of the server stack to a third-party cloud provider doesn’t mean outsourcing security as well. Security needs to remain the enterprise customer’s responsibility, because it is they who will need to answer in the event of a breach.

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Cloud Data Security

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Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.

The requirements have grown well beyond the simplistic features provided by data platforms, and as a result a competitive industry has emerged to address the security layer. The capabilities of this layer must be more than thorough, they must also be usable and streamlined, adding a minimum of overhead to existing processes.

To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number of access control policy management scenarios based on real world use cases we have observed for cloud data workloads. We tested two options: Apache Ranger with Apache Atlas and Immuta. This study contrasts the differences between a largely role-based access control model with object tagging (OT-RBAC) to a pure attribute-based access control (ABAC) model using these respective technologies.

This study captures the time and effort involved in managing the ever-evolving access control policies at a modern data-driven enterprise. With this study, we show the impacts of data access control policy management in terms of:

  • Dynamic versus static
  • Scalability
  • Evolvability

In our scenarios, Ranger alone took 76x more policy changes than Immuta to accomplish the same data security objectives, while Ranger with Apache Atlas took 63x more policy changes. For our advanced use cases, Immuta only required one policy change each, while Ranger was not able to fulfill the data security requirement at all.

This study exposed the limitations of extending legacy Hadoop security components into cloud use cases. Apache Ranger uses static policies in an OT-RBAC model for the Hadoop ecosystem with very limited support for attributes. The difference between it and Immuta’s attribute-based access control model (ABAC) became clear. By leveraging dynamic variables, nested attributes, and global row-level policies and row-level security, Immuta can be quickly implemented and updated in comparison with Ranger.

Using Ranger as a data security mechanism creates a high policy-management burden compared to Immuta, as organizations migrate and expand cloud data use—which is shown here to provide scalability, clarity, and evolvability in a complex enterprise’s data security and governance needs.

The chart in Figure 1 reveals the difference in cumulative policy changes required for each platform configuration.

Figure 1. Difference in Cumulative Policy Changes

The assessment and scoring rubric and methodology is detailed in the report. We leave the issue of fairness for the reader to determine. We strongly encourage you, as the reader, to discern for yourself what is of value. We hope this report is informative and helpful in uncovering some of the challenges and nuances of data governance platform selection. You are encouraged to compile your own representative use cases and workflows and review these platforms in a way that is applicable to your requirements.

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GigaOm Radar for Data Loss Prevention

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Data is at the core of modern business: It is our intellectual property, the lifeblood of our interactions with our employees, partners, and customers, and a true business asset. But in a world of increasingly distributed workforces, a growing threat from cybercriminals and bad actors, and ever more stringent regulation, our data is at risk and the impact of losing it, or losing access to it, can be catastrophic.

With this in mind, ensuring a strong data management and security strategy must be high on the agenda of any modern enterprise. Security of our data has to be a primary concern. Ensuring we know how, why, and where our data is used is crucial, as is the need to be sure that data does not leave the organization without appropriate checks and balances.

Keeping ahead of this challenge and mitigating the risk requires a multi-faceted approach. People and processes are key, as, of course, is technology in any data loss prevention (DLP) strategy.

This has led to a reevaluation of both technology and approach to DLP; a recognition that we must evolve an approach that is holistic, intelligent, and able to apply context to our data usage. DLP must form part of a broader risk management strategy.

Within this report, we evaluate the leading vendors who are offering solutions that can form part of your DLP strategy—tools that understand data as well as evaluate insider risk to help mitigate the threat of data loss. This report aims to give enterprise decision-makers an overview of how these offerings can be a part of a wider data security approach.

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Key Criteria for Evaluating Data Loss Prevention Platforms

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Data is a crucial asset for modern businesses and has to be protected in the same way as any other corporate asset, with diligence and care. Loss of data can have catastrophic effects, from reputational damage to significant fines for breaking increasingly stringent regulations.

While the risk of data loss is not new, the landscape we operate in is evolving rapidly. Data can leave data centers in many ways, whether accidental or malicious. The routes for exfiltration also continue to grow, ranging from email, USB sticks, and laptops to ever-more-widely-adopted cloud applications, collaboration tools, and mobile devices. This is driving a resurgence in the enterprise’s need to ensure that no data leaves the organization without appropriate checks and balances in place.

Keeping ahead of this challenge and mitigating the risk requires a multi-faceted approach. Policy, people, and technology are critical components in a data loss prevention (DLP) strategy.

As with any information security strategy, technology plays a significant role. DLP technology has traditionally played a part in helping organizations to mitigate some of the risks of uncontrolled data exfiltration. However, both the technology and threat landscape have shifted significantly, which has led to a reevaluation of DLP tools and strategy.

The modern approach to the challenge needs to be holistic and intelligent, capable of applying context to data usage by building a broader understanding of what the data is, who is using it, and why. Systems in place must also be able to learn when user activity should be classified as unusual so they can better interpret signs of a potential breach.

This advanced approach is also driving new ways of defining the discipline of data loss prevention. Dealing with these risks cannot be viewed in isolation; rather, it must be part of a wider insider risk-management strategy.

Stopping the loss of data, accidental or otherwise, is no small task. This GigaOM Key Criteria Report details DLP solutions and identifies key criteria and evaluation metrics for selecting such a solution. The corresponding GigOm Radar Report identifies vendors and products in this sector that excel. Together, these reports will give decision-makers an overview of the market to help them evaluate existing platforms and decide where to invest.

How to Read this Report

This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding consider reviewing the following reports:

Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.

GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.

Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.

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