Building Responsible AI
Helping organizations worldwide ensure the security, privacy, safety, trustworthiness, fairness, explainability, and transparency of AI models and applications.
What We Do
Aethercloud is an independent responsible AI test lab based in Silicon Valley, California that helps organizations worldwide ensure the security, privacy, safety, trustworthiness, fairness, explainability, and transparency of deep learning and large language models, and of applications and devices that use AI models.
Aethercloud helps clients with responsible AI testing, technical and product requirements, risk assessment, technical audit readiness, solution engineering, and cybersecurity, as well as with AI governance, risk management, and regulatory compliance.
Responsible AI Services
Technical and Product Requirements
Organizations often struggle to determine the exact technical and product requirements for responsible AI. Aethercloud conducts workshops with clients to help them better understand what responsible AI means in practice. Technical requirements vary depending on industry, applicable laws and regulations, and as a function of use case, technology stack, and application.
AI Cybersecurity
Aethercloud helps clients architect, design, build, monitor, and manage security solutions for AI models and applications. AI applications are like living systems that operate in continuous cycles from data ingestion for training, fine tuning, and retrieval augmented generation (RAG), to end user access. Cyberattacks, including new and novel LLM attacks, can take place at any lifecycle stage and at any trust boundary.
Responsible AI Program Development
Aethercloud helps clients develop and implement responsible AI governance, risk management, and legal and regulatory compliance (GRC) programs, as well as technical solutions, compatible with the ISO/IEC 42001:2023 Information Technology Artificial Intelligence Management System (AIMS), NIST Artificial Intelligence Risk Management Framework (AI RMF), and with other national and international frameworks and standards.
AI Risk Assessment
Aethercloud helps clients assess the technical risks associated with AI models and applications throughout the ML Operations lifecycle. RAI Risk Assessments include discovering and analyzing AI model risks, assessing the probably of risks being realized, and their potential impact. Comprehensive findings and recommendations based on industry best practices provide an objective risk assessments and actionable roadmaps to effectively prioritize and manage risk.
AI Technical Audit Readiness
Aethercloud performs technical audits of AI models, systems, and applications to ensure that real-world implementations are compatible with applicable frameworks, laws, and regulations. Technical audits map controls both to legal and regulatory requirements and to threats. Technical audits establish a responsible AI baseline and are an important step to ensure readiness for regulatory audits or legal discovery.
Responsible AI Solution Engineering
Aethercloud provides architecture, design, and implementation of responsible AI technical controls and monitoring solutions for legal and regulatory compliance purposes. Applicable laws include, for example, the California, Colorado, and Utah state AI laws in the United States, the European Union Artificial Intelligence Act (AI Act) in Europe, the Canadian Artificial Intelligence and Data Act in Canada, and other national and international laws and regulations consistent with the International AI Convention.
Responsible AI Testing
Independent AI model and application testing is the best practice for organizations to manage AI risks. Aethercloud uses the latest AI tools and methods to perform robust testing covering the security, privacy, safety, trustworthiness, fairness, explainability, and transparency of deep learning and large language models, and of applications and devices that use AI models.
AI Red Teaming
AI applications should ever go live without having undergone AI red teaming. AI red teaming tests AI models and applications for vulnerabilities and flaws by simulating AI-specific attacks, such as prompt injection and jail breaking, or privacy attacks, such as data reconstruction and membership inference. Standard AI red teaming includes black box testing.
