Today, we are releasing an AI security risk assessment framework as a step to empower organizations to reliably audit, track, and improve the security of the AI systems. In addition, we are providing new updates to Counterfit, our open-source tool to simplify assessing the security posture of AI systems. There is a marked interest in securing AI systems from adversaries. Counterfit has been heavily downloaded and explored by organizations of all sizes--from startups to governments and large-scale organizations--to proactively secure their AI systems. From a different vantage point, the Machine Learning Evasion Competition we organized to help security professionals exercise their muscles to defend and attack AI systems in a realistic setting saw record participation, doubling the amount of participants and techniques than the previous year.

On September 1, 2021 Gartner published their 2021 "Market Guide for AI Trust, Risk and Security Management". Per Gartner, "This Market Guide identifies new capabilities that data and analytics leaders must have to ensure model reliability, trustworthiness and security, and presents representative vendors who implement these functions."1 At HPE, we believe HPE Ezmeral ML Ops was recognized for the advantages our solution provides to our customers. As such, we're proud to announce that Gartner listed HPE Ezmeral ML Ops as a Representative ModelOps Vendor in the 2021 "Market Guide for AI Trust, Risk and Security Management." Gartner defines the AI Trust, Risk and Security Management (TRiSM) market as being made up of multiple software segments.

BRIDGEi2i Analytics Solutions announced its inclusion in Gartner's Market Guide for Artificial Intelligence Service Providers, 2021. The Gartner report compiles a list of service providers that represent the breadth of offerings in the AI market. Organizations of varied size, complexity, type and location are listed here. This list is based on client interactions and publicly available information. BRIDGEi2i CEO and Co-Founder - Prithvijit Roy remarked, "At BRIDGEi2i, we are excited to leverage AI to build the digital enterprise. Our pre-built, ready-to-deploy algorithmic engines, the AI Accelerators, enable enterprises to transform their businesses and achieve powerful outcomes using AI. Being featured in the Gartner's Market Guide for Artificial Intelligence Service Providers, 2021, takes us one step further on the journey of making AI real for enterprises. I'm grateful to all our employees and clients who are partnered with us in reimagining businesses using AI, and also for the Gartner analyst team's support."

Choosing a conversational AI platform is a daunting task with a lot of noise and hyperbole surrounding it. Tools for designing and deploying conversational AI applications are getting easier and better, and some have focused their design around user experience. We took our best shot at evaluating and summarizing the list of top platforms that Gartner Research included in their 2019 Market Guide for Conversational Platforms. Gartner evaluated over 1,000 platforms and widdled the market down to 20 top platforms in 2018. In 2019, Gartner narrowed their list to 16 platforms, noting that the majority of today's market is "very simple platforms using modified open-source components to deliver simple question and answer chatbots."

As organizations venture into the brave new world of artificial intelligence (AI) in the enterprise, the first stop in the journey is in many cases the organization's own IT operations department. This trend is underscored in recent industry research. A study by Enterprise Strategy Group (ESG), for example, found that AI in IT is a common entry point into the use of AI and machine learning in the enterprise. In a survey of IT leaders, the firm determined that the top two use cases for AI were IT systems management-and-orchestration functions and IT system log file error analysis.1 The majority of the respondents in the ESG survey liked the idea of using AI as a recommendation engine, while a significant percentage of the remaining respondents wanted a system that can automatically detect, analyze, recommend and apply changes as needed.

Self-driving cars and medical advances may grab all the headlines, but artificial intelligence is being applied to any number of less high-profile applications across a variety of industries. Perhaps the most unsung and yet closest to home for IT organizations comes in the form of "AIOps." That could be because applying artificial intelligence to IT operations is still an emerging area, so it hasn't garnered much attention. But early indications are that this is a growing area. It's too small for the Magic Quadrant treatment at Gartner, but in August 2017, Gartner issued a Market Guide for AIOps platforms.

In Gartner's 2018 Market Guide for CRM in Tier 1 Retail, Kelsie Marian states, "The next phase of CRM includes leveraging advanced technologies to better understand and predict customer behavior across a variety of contextual settings." I was reading Gartner's report because Symphony RetailAI was listed as a representative vendor for our SR Personalized Marketing solution. I was struck by how Kelsie's statement is reflected back to me almost weekly. In my job, I'm fortunate to speak to executives both within my company and outside. I'd expect our execs to promote technologies that help retailers understand customers and predict their behavior – the essence of AI-enabled customer relationship management.

We'd like to offer you a complimentary copy of Gartner's Market Guide for Data Preparation. Tamr, the leader in enterprise data unification, has again been recognized as a Representative Vendor in Gartner's Market Guide for Data Preparation. "Data and analytics leaders are struggling due to their over-reliance on IT-centric tools for finding, cataloging and transforming relevant data and making it accessible to the growing number of distributed users in the enterprise both inside and outside of centralized data and analytics teams. Due to this, organizations report that they spend more than 60% of their time in data preparation, leaving little time for actual analysis." Our view of the underlying cause of the problem is that the deterministic, rules-based approaches inherent in traditional approaches to data integration can't keep pace with the number of sources that must be unified to generate big breakthroughs.

SOUTHBOROUGH, Mass., – (March 08, 2018)–Virtusa Corporation (NASDAQ GS: VRTU), a global business consulting and IT outsourcing company that accelerates business outcomes for its clients,has been included in Gartner'sMarket Guide for Data Science and Machine Learning Service Providers. The report, published on October 31, 2017, states: "Data and analytics leaders looking for support for their data science and machine learning projects should use this research to identify and engage with candidate service providers to fill the analytics deficit and augment their existing data scientists with specific skills." According to Gartner, "the growing demand for DS&ML as a competitive differentiator is forcing organizations to acquire an even wider portfolio of skilled resources – from statisticians and data scientists, to chief analytics officers. However, there is a shortage of data science skills in the market, making it difficult to source the right skills." "We feel that we are one of the most visible DS&ML service partners worldwide and are proud to be recognized in Gartner's Market Guide," said Kumar Ramamurthy, senior vice president and global head, Data & Analytics, Virtusa.

AIOps is an emerging technology and addresses something I'm a big fan of – improving IT Operations. Roughly three years ago, it was looking like we were going to see many enterprise IT operations leaders put themselves in the precarious role of "the cobbler's children" by forgoing investment in Artificial Intelligence (AI) to help them do their work better, faster, and cheaper. We were hearing from many IT ops leaders building incredibly sophisticated Big Data and Advanced Analytics systems for business stakeholders, but were themselves using rudimentary, reactive red/yellow/green lights and manual steps to help run the infrastructure required to keep those same systems up and running. Now I wouldn't say everything has changed since that time, but a lot has, and for the better. Since then, we have seen a number of exciting developments including a rapid shift in the acceptance of and interest in applying a broad spectrum of Artificial Intelligence (AI) capabilities to enterprise IT operations management challenges. As a result, We introduced the concept of AIOps (originally called Algorithmic IT Operations, now Artificial Intelligence for IT Operations) as a means to describe growing interest and investment in these technologies.