Gear Up for Change: New AI Regulations
Organizations must proactively engage in comprehensive assessments to futureproof AI initiatives and maintain a competitive edge in an increasingly regulated landscape. A thorough and multifaceted assessment process is required to accurately identify, track, and manage AI-related risks and comply effectively with impending regulation, like the EU AI Act.
When rapid growth
leads to exponential risks
We’ve identified and mapped over 1,600 AI risks sourced from AI regulations, industry standards, and scientific best practices. The rapid advancement of AI technologies, including the proliferation of Large Language Models (LLMs) and intelligent agents tools bring immense potential but also necessitate a heightened sense of responsibility.
Regulations and Compliance
staying ahead of regulatory compliance is not just about adherence; it’s about survival.
AI regulations carry the potential for significant fines, as well as damage to reputation and trust.
Affect a business’s ability to operate in certain markets, like the EU.
While AI promises significant business value, mismanagement or poor implementation can lead to substantial risks and financial losses.
Implementation issues may incur risks such as costly implementation,
lack of adoption and insufficient oversight.
Effective governance is crucial to mitigate risks and ensure responsible use. It includes establishing AI policies and integrating AI risks into corporate governance.
AssessAI provides organizations with the tools to navigate the evolving AI landscape. Our platform offers in-depth assessments and a robust framework for tracking and managing AI risks at both organizational and system-specific levels. Designed for a responsible approach, it ensures compliance and fosters a secure AI ecosystem.
What You’ll Get
SOME ANIMATIONS HERE
What We Cover
Evaluating and ensuring consistency and uniformity across different AI systems and use cases.
Alignment with Business Needs
Ensuring AI models are up-to-date and aligned with changing business needs.
Evaluating each stage of the AI lifecycle to guarantee operational procedures are robust and adhere to regulation.
AI Assets Integrity
Assuring data quality and accuracy, as well as the accuracy of data sources and models.
Mitigations and Controls
Reviewing the design and implementation of current governance assurance and controls.
Legal and Ethical Issues
Ensuring responsible and fair practices such as data privacy, bias mitigation, and transparent communication.