Mar 12, 2021

Dimension Data selects Cortex XSOAR as extended platform

Automation
Cybersecurity
Data
Dimension Data
Tom Swallow
2 min
Mohammad Hejazi, Managing Director, Dimension Data Middle East, on the company’s adoption of extended Cortex SOAR platform from Palo Alto Network
Mohammad Hejazi, Managing Director, Dimension Data Middle East, on the company’s adoption of extended Cortex SOAR platform from Palo Alto Network...

Cortex XSOAR, by Palo Alto Networks, has been chosen as an extended SOAR (Security, Orchestration, Automation and Remediation) platform by leading technology partner, Dimension Data Middle East.

Managed Security Services are used to predict, respond to and remediate security issues as quickly as possible and, through the use of this service, Dimension Data Middle East now has a platform to accurately assess threat vectors.

“Dimension Data’s cybersecurity investment and skills development illustrates an unwavering determination to be the most-trusted, innovative and operationally excellent managed services partner that clients need in an ever-changing threat landscape,” says Mohammad Hejazi, Managing Director, Dimension Data Middle East.

Why Dimension Data is using Cortex XSOAR

Dimension Data will have access to hundreds of standard integrations in different security technologies, as well as Cortex XSOAR custom integration capabilities.

“From an operational perspective, this gives us the ability to provide an even more robust service to our customers, and rapidly respond to major security incidents. Our security engineers understand the enormity of the responsibility they shoulder in protecting our clients’ critical assets,” says Hejazi.

The company will also benefit from improved support for cross-regional teams across the Middle East and Africa, and the standardised response workflows offered by the extended platform.

“With the Cortex XSOAR platform, we are able to govern the operational processes resulting in material service improvements which our clients will experience when they consume our SOAR-enabled Managed Services. After all, the provision of security services is first and foremost about trust,” says Hejazi.

The benefits of Cortex XSOAR for customers

By integrating the capabilities of Cortex XSOAR, Dimension Data customers will benefit from:

  • Significant reduction in the amount of data that security analysts have to manually review
  • Standardisation of operational response workflows across the MEA regions
  • Standardisation of security vendor interactions
  • Seamless enablement of new and existing managed security services
  • Lowered input costs due to automation and orchestration
  • Standard orchestrated response workflow (playbooks) will enable cross-regional teams to support each other

“Dimension Data is a specialist IT services and solution provider that helps clients plan, build, support and manage their IT infrastructures. Headquartered in Johannesburg, Dimension Data operates in 51 countries across five regions – Middle East & Africa, Europe, Asia Pacific, Australia and the Americas.”

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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