The ability of artificial intelligence (AI) to partner with the software engineer, doctor, or warfighter depends on whether these end users trust the AI system to partner effectively with them and deliver the outcome promised. To build appropriate levels of trust, expectations must be managed for what AI can realistically deliver. In this podcast from the SEI’s AI Division, Carol Smith, a senior research scientist specializing in human-machine interaction, joins design researchers Katherine-Marie Robinson and Alex Steiner, to discuss how to measure the trustworthiness of an AI system as well as questions that organizations should ask before determining if it wants to employ a new AI technology.
Zero Trust Architecture: Best Practices Observed in Industry
Automating Infrastructure as Code with Ansible and Molecule
Identifying and Preventing the Next SolarWinds
A Penetration Testing Findings Repository
Understanding Vulnerabilities in the Rust Programming Language
We Live in Software: Engineering Societal-Scale Systems
Secure by Design, Secure by Default
Key Steps to Integrate Secure by Design into Acquisition and Development
An Exploration of Enterprise Technical Debt
The Messy Middle of Large Language Models
An Infrastructure-Focused Framework for Adopting DevSecOps
Software Security in Rust
Improving Interoperability in Coordinated Vulnerability Disclosure with Vultron
Asking the Right Questions to Coordinate Security in the Supply Chain
Securing Open Source Software in the DoD
A Model-Based Tool for Designing Safety-Critical Systems
Managing Developer Velocity and System Security with DevSecOps
A Method for Assessing Cloud Adoption Risks
Software Architecture Patterns for Deployability
ML-Driven Decision Making in Realistic Cyber Exercises
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