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Cybersecurity: The key lessons of the Triton malware cyberattack you need to learn

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Triton malware targeting industrial facilities in Middle East
The malware has been designed to target industrial systems and critical infrastructure.

The Triton malware attack was far from the first time that hackers have attempted to target the networks of an industrial facility, but it was the first time that malware designed to attack safety systems was ever seen in the wild.

The malware was designed to manipulate Schneider Electric’s Triconex Safety Instrumented System (SIS) controllers – emergency shutdown systems – and was uncovered on the network at a critical infrastructure operator in the Middle East.

The malware campaign was extremely stealthy and was only uncovered because the attackers made a mistake and triggered the safety system, shutting down the plant. The outcome could’ve been much worse.

“We can speculate that their mission is of some physical consequence. They wanted to either stop production at this facility, stop things from working or potentially cause physical harm,” says Dan Caban, incident response manager at FireEye’s Mandiant.

SEE: A winning strategy for cybersecurity (ZDNet special report) | Download the report as a PDF (TechRepublic) 

Speaking during a session on Triton at the National Cyber Security Centre’s CYBERUK 19 conference, Caban argued that it was fortunate the malware was uncovered, alerting the world to dangerous cyberattacks that can alter or damage physical systems.

“We were very lucky that this accident happened, it opened the door for people to start thinking about this physical consequence which may have cybersecurity origins – that’s how this investigation kicked off and now so much has come to public light,” he says.

Following the initial point of compromise, the malware was able to use techniques such as harvesting credentials and moved across the network to reach the SIS controllers.

However, Triton was only able to reach its goal because of some lax attitudes to security throughout the facility: the safety controllers should have been disconnected from the network but were connected to internet-facing operational systems, allowing attackers to gain access.

Other failures — like a key being left inside a machine — provided attackers with access they should never have gained without physically being inside the facility.

While the malware has the potential to be highly damaging to valves, switches and sensors in an industrial environment, the threat can be countered by implementing some relatively simple cybersecurity techniques that make movement between systems almost impossible.

“Network segregation can help you avoid this happening. You should be separating them logically, but also based on criticality and by following industry best practice and industry standards,” Caban explains. “You should also consider directional gateways so it’s not possible to move certain ways.”

Organisations can also take a step towards this by ensuring there’s proper management around cybersecurity and that there’s plenty of information around systems for staff of all levels to understand what’s going on – and what to do if something goes wrong.

“In a cyber context, it’s absolutely essential that you have governance; leadership from the very top level. Without proper governance in your organisation, you’re probably setting up for failure,” says Victor Lough, head of UK business at Schneider Electric.

“For cybersecurity, you must consider the physical safety because you’re considering kinetic systems. And on the flip-side of that, physical safety must always consider cybersecurity, so they’re opposite sides of the same coin – without security we have no safety,” he says.

There was once a time when the security of cyber systems and the security of physical systems might have been able to be considered separately, but not any more: in many cases, they’re now one and the same.

“This is the blending of the cyber and the physical security – the things you can put bollards around. You kind of could have in this case – they left the key in and left it in programme mode,” said Deborah Petterson, deputy director for critical national infrastructure at the UK’s NCSC.

SEE: Industroyer: An in-depth look at the culprit behind Ukraine’s power grid blackout

In this incident, realising that the key had been left in the machine would have gone a long way to preventing hackers from gaining access to conduct malicious activity.

“People knowing where their safety systems are and how they’re connected – it’s really basic,” she said, suggesting that those running these systems should regularly be examining how the networks operate and should keep logs about updates – especially about dated systems like the industrial facility was running on.

“The one in this example was 15 years old – when was the last time you looked at risk management around that? The churn in security people is one to two years with CISOs. When was the last time you dusted off and used this as a point to go and have a look?” Petterson asked.

Triton targeted critical infrastructure in the Middle East, but there are lessons from the incident that can be applied to organisations in every sector, no matter where they are in the world.

“If you take this out of the context of safety systems, you can apply almost all of them to any enterprise system. They’re the same sort of controls we just ask any business to do to make themselves cyber safe,” says Dr Ian Levy, technical director at the NCSC.

The hacking group behind Triton – which has has been linked to Russia – remains active, with researchers at FireEye recently disclosing a new campaign targeting a fresh critical infrastructure facility.

However, with the tactics of the group now in the public eye, it’s possible to detect and protect against malicious activity.

“All these backdoors, lateral movement techniques and credential harvesting: they can be detected, it’s possible, we don’t have to give up hope,” said FireEye’s Caban.

“They can be detected in IT, detected between the IT and OT DMZ – those are easy places to start looking.”

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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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