May 10, 2021

How is AI contributing to cyber security processes?

Tilly Kenyon
3 min
As cyberattacks grow in volume and complexity, artificial intelligence (AI) is helping companies stay ahead of threats
As cyberattacks grow in volume and complexity, artificial intelligence (AI) is helping companies stay ahead of threats...

Cyber security is becoming increasingly important as cyber criminals can pose a threat to all organisations and businesses, plus the customers and consumers who use them. 

Cybercriminals can deploy AI to increase the success of their attacks. For example, they can use AI to spot patterns in user behaviour, which hackers can take advantage of, or deploy it to identify new network vulnerabilities. AI also works at immense speed, in real-time.

The US Government has taken action after a ransomware attack shut down a major US pipeline. The Colonial Pipeline carries 2.5 million barrels a day, 45% of the East Coast's supply of diesel, gasoline, and jet fuel. The pipeline was knocked offline by a cyber criminal gang on Friday and the Government has issued emergency legislation that relaxes rules on fuel being transported by road. 

This means drivers in 18 states can work extra or more flexible hours when transporting gasoline, diesel, jet fuel and other refined petroleum products.

How can AI help with cyber security? 

As businesses evolve and grow, so too does the threat of cyberattacks. Verizon found that in 2020 86% of breaches were financially motivated and 10% were motivated by espionage. 45% of breaches featured hacking, 17% involved malware and 22% involved phishing. 

Artificial intelligence (AI) and machine learning (ML) are playing an increasing role in cyber security. AI gathers insights and uses reasoning to identify the relationships between threats, such as malicious files, suspicious IP addresses, or insiders. This analysis takes seconds or minutes, allowing security analysts to respond to threats much faster.

Passwords have always been an issue when it comes to security, and are often the link between cyber criminals and our identities. Biometric authentication is starting to be used as an alternative to passwords, but attackers can still easily bypass these controls. Developers are utilising AI to improve current biometric authentication and eliminate any imperfections to make it a robust application. One example is Apple's face recognition technology, called Face ID. The device detects the user's facial features by built-in infrared sensors and neural engines. AI software produces a sophisticated face model by recognising key similarities and patterns.

AI can optimise and monitor many essential data center processes like backup power, cooling filters, power consumption, and internal temperatures. The calculative powers and continuous monitoring capabilities of AI provide insights into what values would improve the effectiveness and security of hardware and infrastructure.

The future of AI in cyber security

An important element to the success of AI in cyber security is making sure that your business’s IT team has the skills and is up to date with the latest information. Building a roadmap before deploying AI capabilities can also be beneficial so companies can see where it will be best used. 

There is no doubt that AI will be used in cyber security for the foreseeable future, but while AI’s ability to process information is impressive, we cannot forget it can only work as well as it was programmed to. 

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Jun 23, 2021

Google launches Visual Inspection AI tool for manufacturers

3 min
Google has launched Visual Inspection AI, a new Google Cloud Platform solution designed to help reduce defects during the manufacturing process

Google Cloud has launched Visual Inspection AI, a new tool to help manufacturers identify defects in products before they're shipped. 

Poor production quality control often leads to significant operational and financial costs. The American Society for Quality estimates that for many organisations this cost of quality is as high as 15-20% of annual sales revenue, or billions of dollars annually for larger manufacturers. Google Cloud’s new Visual Inspection AI solution has been purpose-built for the industry to solve this problem at production scale. 

How does it work? 

The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.

Google built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI. 

  • Run autonomously on-premises: Manufacturers can run inspection models at the network edge or on-premises. The inspection can run either in Google Cloud or fully autonomous on your factory shop floor. 
  • Short time-to-value: Customers can deploy in weeks, not the months typical of traditional machine learning (ML) solutions. Built for process and quality engineers, no computer vision or ML experience required. An interactive user interface guides users through all the steps. 
  • Superior computer vision and AI technology: In production trials, Visual Inspection AI customers improved accuracy by up to 10x compared with general-purpose ML approaches, according to benchmarks from several Google Cloud customers. 
  • Get started quickly, with little effort: Visual Inspection AI can build accurate models with up to 300x fewer human-labeled images than general-purpose ML platforms, based on pilots run by several Google Cloud customers.
  • Highly scalable deployment: Manufacturers can flexibly deploy and manage the lifecycle of ML models, scaling the solution across production lines and factories.

Industry use cases

The demo video shows how Visual Inspection AI addresses use cases to solve specific quality control problems in industries such as automotive manufacturing, semiconductor manufacturing, electronics manufacturing and general-purpose manufacturing. 

Kyocera Communications Systems, a manufacturer of mobile phones for wireless service providers, has been able to scale its AI and ML expertise through the use of the solution. “With the shortage of AI engineers, Visual Inspection AI is an innovative service that can be used by non-AI engineers,” said Masaharu Akieda, Division Manager, Digital Solution Division, KYOCERA Communication Systems. “We have found that we are able to create highly accurate models with as few as 10-20 defective images with Visual Inspection AI. We will continue to strengthen our partnership with Google to develop solutions that will lead our customers' digital transformation projects to success.”

Visual Inspection AI has fully integrated with Google Cloud's portfolio of analytics and ML/AI solutions, giving manufacturers the ability to combine its insights with other data sources. The tool integrates with existing products from Google Cloud partners, including SOTEC, Siemens, GFT, QuantiPhi, Kyocer and Accenture. 

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