By combining the roles of software development and IT operations, DevOps (opens in new tab) often encompasses so many tools and skills that too many of us get stuck working in a complex and time-consuming environment. Time that could be spent solving problems gets wasted on mundane tasks. By using artificial intelligence (AI), DevOps can automate complicated tasks that are easy for computers but hard (or boring) for humans. AI can also help streamline processes across the software development lifecycle (SDLC), allowing DevOps to focus on the work itself. In this guide, we cover how AI can be used throughout the DevOps cycle to improve productivity and security.

Artificial intelligence must be the future for network security, according to Fortinet. With the threat landscape constantly evolving and increasing in complexity, continued digital innovation, technological developments, and the introduction of 5G, coupled with the challenges of accelerated remote working practices and a growing cybersecurity skills gap, have collectively exacerbated the challenges that CISOs face in terms of protecting their companies' digital assets. As CISOs assess their cybersecurity posture, it's essential that they consider how to leverage new and emerging technologies to best protect their infrastructure, the company says. There have been significant developments in the artificial intelligence (AI) space that make it an increasingly strategic investment. However, Fortinet says it can be challenging for CISOs to cut through the hype and understand which AI-based solution is best suited to their organisation.

Perhaps the most resource-intensive task required of security teams is the correlation and analysis of the massive volumes of data being produced by security devices and network sensors. This challenge is probably most apparent in the fact that network breaches often remain undetected for months, allowing cybercriminals to plant time-bombs, establish elaborate botnets, and slowly exfiltrate millions of records containing customer information and intellectual property. This challenge is compounded with the growing skills shortage the cybersecurity industry is facing globally, further adding to organizations' risks. In fact, a recent Fortinet survey found that 73% of organizations had at least one intrusion or breach over the past year that can be partially attributed to a gap in cybersecurity skills. There are steps organizations can take to close the cyber skills gap.

The technology environment in which businesses and society operate is becoming increasingly complex. Learn how organizations can use artificial Intelligence to secure our digital society moving forward.

John Maddison, EVP of products and CMO at Fortinet "Fortinet has invested heavily in FortiGuard Labs cloud-based, AI-driven threat intelligence, allowing us to detect more threats, more quickly and more accurately. FortiAI takes the artificial intelligence knowledge from FortiGuard Labs and packages it specifically for on-premises deployments. This gives customers the power of FortiGuard Labs directly in their environment, with self-learning AI to identify, classify and investigate sophisticated threats in sub-seconds." Fortinet, a global leader in broad, integrated and automated cybersecurity solutions, announced FortiAI, a first-of-its-kind on-premises appliance that leverages self-learning Deep Neural Networks (DNN) to speed threat remediation and handle time consuming, manual security analyst tasks. FortiAI's Virtual Security Analyst embeds one of the industry's most mature cybersecurity artificial intelligence developed by Fortinet's FortiGuard Labs – directly into an organization's network to deliver sub-second detection of advanced threats.

John Maddison, EVP of products and CMO at Fortinet "Fortinet has invested heavily in FortiGuard Labs cloud-based, AI-driven threat intelligence, allowing us to detect more threats, more quickly and more accurately. FortiAI takes the artificial intelligence knowledge from FortiGuard Labs and packages it specifically for on-premises deployments. This gives customers the power of FortiGuard Labs directly in their environment, with self-learning AI to identify, classify and investigate sophisticated threats in sub-seconds." Fortinet, a global leader in broad, integrated and automated cybersecurity solutions, announced FortiAI, a first-of-its-kind on-premises appliance that leverages self-learning Deep Neural Networks (DNN) to speed threat remediation and handle time consuming, manual security analyst tasks. FortiAI's Virtual Security Analyst embeds one of the industry's most mature cybersecurity artificial intelligence developed by Fortinet's FortiGuard Labs – directly into an organization's network to deliver sub-second detection of advanced threats.

Fortinet, a global integrated and automated cybersecurity solutions, today announced FortiAI, an appliance that leverages self-learning Deep Neural Networks (DNN) to speed threat remediation and handle time-consuming, manual security analyst tasks. FortiAI's Virtual Security AnalystÔ embeds a mature cybersecurity artificial intelligence, developed by Fortinet's FortiGuard Labs, directly into an organization's network to deliver sub-second detection of advanced threats. To address the challenges faced by security professionals today, Fortinet is unveiling FortiAI Virtual Security AnalystÔ to accelerate threat remediation. FortiAI handles many of the time consuming, manual tasks currently expected of security professionals, preserving their time for higher-value security functions. FortiAI's self-learning capabilities continue to get smarter once deployed in an organization's network.

Swarm-based network attacks are still likely a couple years away, according to Derek Manky, chief of security insights and global threat alliances at FortiGuard Labs. But, especially as they start deploying 5G networks, enterprises should prepare for these types of attacks now. FortiGuard Labs is the global threat intelligence and research team at Fortinet. In a conversation at the recent RSA Conference, Manky explained the strategy behind swarm attacks -- one of the scarier emerging threats that he's been following for a few years. "And I do think this is on the horizon," he said.

Ambarella AMBA recently announced a collaboration with Amazon's AMZN cloud computing arm, AWS, enabling customers to use Amazon SageMaker Neo cloud service to run ML models on devices based on Ambarella's CVflow-powered AI vision SoC (system on chip). Reportedly, this collaboration eliminates the need for developers to manually optimize ML models for devices based on Ambarella AI vision SoCs, preventing delays and errors in application development. The company will exhibit the collaboration with Amazon SageMaker Neo during CES 2020, to be held on Jan 7-10. Ambarella also named IP surveillance solution provider, VIVOTEK, as the first joint customer to leverage the single-click ML solution for edge applications. The company's CVflow suit of SoCs runs on an advanced 10-nanometer process, which enables the development of compact, high-performance vision systems with ultra-low-power operation.

Machine learning (ML) is hailed as one of the most impactful technologies in the AI spectrum. Comprising algorithms, ML applications are capable of enabling devices to learn, improve and make decisions automatically, without any explicit programming or human involvement. The application of ML has grown rapidly over the past years, bringing out the power of data in a whole new way. Devices operating on voice commands, recommendations from online search engines, real-time advertisements on web pages, image recognition and cyber fraud detection are some of the most common instances of ML. The technology has proven itself to be ground-breaking for the transportation industry by making self-driving cars a reality.