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Ian Swanson
Proven leader in Artificial Intelligence and Machine Learning
Ian Swanson is a prominent figure in the field of artificial intelligence and machine learning. He served as the Worldwide Leader for Artificial Intelligence and Machine Learning at Amazon Web Services (AWS) from February 2020 to July 2021.1 In this role, he was responsible for leading the global go-to-market teams for AI and ML, delivering digital transformation results for tens of thousands of companies.1
Currently, Ian Swanson is the CEO and Founder of Protect AI, a cybersecurity company focused on AI and ML systems, which he started in April 2022.1 Protect AI aims to help organizations deploy more secure artificial intelligence applications and machine learning systems.2
Prior to his current role and his position at AWS, Ian Swanson's career included several notable positions:
- Vice President of Artificial Intelligence and Machine Learning at Oracle (June 2018 - August 2019)1
- CEO and Founder of DataScience.com, which was acquired by Oracle in May 20181
- Executive positions at American Express and Sprint2
- CEO of Sometrics, a global virtual currency platform acquired by American Express in 20112
Ian Swanson is recognized as an expert in AI, ML, digital transformation, data science, big data, and performance-based analytics. He is a sought-after speaker and actively advises Fortune 500 companies.2 Additionally, he is an angel investor in startups and has been a member of the Forbes Technology Council.1
Most recently, in October 2024, Ian Swanson was honored by Goldman Sachs for entrepreneurship, recognizing his achievements as the Co-Founder and CEO of Protect AI.3
Highlights
Boom! Protect AI has raised a $60M Series B to accelerate our leading position in this new and fast growing market, security of AI. Read more here --> https://lnkd.in/gmyc-BjU
Since raising $35M in Series A funding one year ago, Protect AI has solidified its leadership in the AI security market. The Protect AI security posture management (AI-SPM) platform is now used by private and public sector customers to secure traditional ML models, LLMs, ML systems, and AI applications. The platform has expanded from one to five products, becoming the most comprehensive end-to-end AI security solution available.
The company's huntr AI/ML threat research community has grown to more than 15,000 members who identify and fix vulnerabilities in crucial AI/ML supply chain projects. Protect AI’s five open-source offerings have been downloaded millions of times, and MLSecOps Community continues to lead AI security education and knowledge sharing. With four acquisitions to date — Rebuff, Huntr, Laiyer AI, and SydeLabs — and a 300% year-over-year team growth, we have plans to add 50 more employees by the end of 2024.
Protect AI will use the new financing to drive the next phase of innovation and capabilities for its customers, enhancing its AI security posture management platform. This capital infusion will accelerate the company's growth by expanding customer success and sales resources, advancing R&D, and strengthening channel programs. Protect AI is positioned to extend its lead in the AI security market, providing unmatched protection for AI applications and systems worldwide.
Huge thanks to Evolution Equity Partners, boldstart ventures, Salesforce Ventures, Acrew Capital, Pelion Venture Partners, 01 Advisors, Samsung Next, Knollwood Investment Advisory, StepStone Group... and the entire team at Protect AI!!!
#protectai #mlsecops #huntr #aisecurity #aispm
Check out the draft National Institute of Standards and Technology (NIST) Assessing Risks and Impacts of AI (ARIA) Pilot Evaluation Plan, updated on May 21, 2024. Link --> https://lnkd.in/gWG8te8F It outlines the ARIA 0.1 pilot program's objectives, methodologies, and evaluation processes for assessing the risks and impacts of large language models (LLMs). Below are some of the key points.
Purpose: ARIA aims to improve risk and impact assessments for AI, specifically focusing on safe and trustworthy AI technologies.
Important terms and definitions include 1/ Applications: LLMs with a text-based user interface, 2/ Assessors: Trained professionals evaluating application output, 3/ Participants: Teams submitting AI applications for evaluation, 4/ Redlines: Definitions differentiating safe and unsafe application behavior, and 5/ Scenarios: Contexts for structured evaluation activities.
There are three evaluation levels and methodologies 1/ Model Testing: Confirms claimed model capabilities and safeguards through scripted dialogues and assessor evaluations, 2/ Red Teaming: Identifies potential adverse outcomes and stress tests model safeguards, and 3/ Field Testing: Investigates how people interact with AI in regular use and assess their impacts in realistic settings.
What are your thoughts on ARIA 0.1?
Happy to schedule a call with the Protect AI team and/or we can schedule this as a topic for a MLSecOps Community podcast... let me know!
#nist #ARIA #aisecurity #protectai #mlsecops
