Apr 30, 2021

Vectra AI raises $130M at a $1.2B valuation

AI
Cybersecurity
IoT
Cloud
Tilly Kenyon
2 min
AI-powered cybersecurity platform Vectra AI has raised $130M at a post-money valuation of $1.2 billion
AI-powered cybersecurity platform Vectra AI has raised $130M at a post-money valuation of $1.2 billion...

Vectra AI, a company that applies artificial intelligence (AI) that detects and responds to hidden cyber attackers inside cloud, data center, and enterprise networks, has announced it has raised $130 million in a funding round that values the company at $1.2 billion.

The California-based company has said the investment will continue to help their growth and expansion into new markets and countries. It will also ‘solidifying its Cognito platform as a market-leading solution for artificial intelligence (AI)-driven cloud security for threat detection and response.’

The funding is being led by funds managed by Blackstone Growth (BXG), with unnamed existing investors participating, increasing the company’s total funding to more than $350 million.

Security threats 

Throughout the past year with more people working from home, security threats have become a more prominent issue. 

According to Vectra AI this product provides the contextual awareness security teams need to combat the expanding attack surface, created by the increased use of cloud resources and SaaS applications, mobile devices, work-from-home access and the Internet of Things (IoT).

“Over the past year, we have witnessed a continuous series of the most impactful and widespread cyberattacks in history. To protect their employees and digital assets, our customers require security solutions that are smarter than today’s adversaries and provide coverage for cloud, data centers, and SaaS applications” said Hitesh Sheth, President, and Chief Executive Officer at Vectra.

“As we look to the future, Blackstone’s global presence, operational resources, and in-house technology expertise will help us achieve our mission to become one of the dominant cybersecurity companies in the world.”

Customer demand for threat detection and response in the private and public clouds are major forces driving the company’s global growth. Vectra’s AI-driven Cognito platform detects attacker behaviors and protects its users from being compromised, regardless of location. The platform works across the complex enterprise IT infrastructure to continuously learn and deploys advanced AI, threat intelligence feeds, and known attack profiles to identify breaches as they are being executed. This approach provides security teams with time to respond to threats and seamlessly integrates with existing security tools to automatically remediate threats.

“As organizations around the world increasingly focus on cybersecurity, we believe Vectra is providing a critical solution that levels the playing field against the sophisticated actors that generate the most damaging cyber threats,” said James Socas, a Managing Director at Blackstone.

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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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