The State of AI Risk in 2025: New Benchmarks, Taxonomies, and Global Safeguards
As AI capabilities accelerate, so do the risks—and with them, the need for objective, standardized ways to assess them. In August 2024, WIRED spotlighted a breakthrough in this effort: the debut of AIR-Bench 2024, a benchmarking system that evaluates large language models (LLMs) against real-world safety criteria. Grounded in both governmental regulations and corporate policies, AIR-Bench has provided the clearest comparative snapshot yet of how today’s most advanced AI models stack up when it comes to risk.
Among the findings, Anthropic’s Claude 3 Opus and Google’s Gemini 1.5 Pro ranked highly in safety-focused categories, while Databricks' DBRX Instruct scored significantly lower across the board. But that was only the beginning.
Since that report, a wave of new tools and international collaborations have reshaped how we understand and manage AI risk. Here's what leaders, technologists, and policy shapers need to know now.
🔍 AIR 2024: Building a Shared Language of Risk
In the wake of AIR-Bench, researchers have developed AIR 2024, a comprehensive taxonomy identifying 314 unique AI risk categories. This taxonomy merges insights from 8 governmental frameworks and 16 corporate AI safety policies, transforming fragmented guidelines into a structured, actionable framework.
These risks are grouped into four main domains:
System & Operational Risks (e.g., failure under stress, reliability issues)
Content Safety Risks (e.g., generation of harmful, biased, or deceptive outputs)
Societal Risks (e.g., economic displacement, social polarization)
Legal & Rights Risks (e.g., IP infringement, privacy violations)
By tying these categories to tangible policies and operational safeguards, AIR 2024 helps developers and executives speak a common language about AI safety—one that can translate across sectors and borders.
🧭 MIT’s AI Risk Repository: A Living Map for Mitigation
To further operationalize safety, researchers at MIT unveiled the AI Risk Repository—a dynamic, searchable database cataloging over 1,600 AI-related risks.
Pulled from 65 frameworks across academic, government, and industry sources, the repository classifies risks by both root cause and application domain, making it easier to design risk-aware systems from the ground up.
Whether you're a policymaker, engineer, or strategic leader, this evolving tool makes it possible to pinpoint which risks are most relevant to your systems and plan accordingly.
🌐 A Global Consensus: The First International AI Safety Report
Acknowledging that AI risk is a borderless concern, 30 nations that participated in the 2023 AI Safety Summit commissioned a first-of-its-kind report led by AI luminary Yoshua Bengio. Released in January 2025, the Independent International AI Safety Report sets a precedent for global cooperation on AI safety.
Key takeaways include:
An assessment of general-purpose AI (GPAI) risks
Proposals for international model certifications
Recommendations for post-deployment monitoring and audits
Urging greater transparency in how models are trained and fine-tuned
The report marks a turning point: moving AI safety from isolated efforts to international coordination with enforceable standards.
🛡️ Weak Links Exposed: Security Challenges Remain
While frameworks and audits have made strides, model vulnerabilities remain an active threat. Recent research uncovered significant security gaps in some widely used systems:
DeepSeek’s R1 was shown to be vulnerable to prompt injection, failing to block malicious inputs that trick the model into unsafe behavior.
Google’s Gemini faced scrutiny after researchers found it could be manipulated via hidden instructions embedded in long-term memory—raising critical questions about reliability in adaptive AI systems.
These weaknesses illustrate that while evaluation and taxonomy tools are vital, they must be paired with robust model defenses and continuous monitoring.
🤝 A Long-Term Commitment to Responsible AI
This isn’t the first time we’ve emphasized the importance of responsible innovation. Nearly a year ago, I reflected on this very challenge in a LinkedIn post, sharing how decades of work at the intersection of technology, market dynamics, and leadership coaching have shaped my lens on AI. That commitment—to advancing progress without compromising human values—remains the foundation of everything we build and advise on at MarketTecNexus.
Whether we’re helping clients evaluate AI readiness or guiding enterprise-level adoption strategies, our focus remains on creating AI-powered systems that are not only effective but also ethical, secure, and human-aligned.
🧩 Final Thoughts: Safety as the New Success Metric
The developments since AIR-Bench 2024 reflect a crucial shift: we’re no longer just trying to build powerful AI—we’re learning how to build trustworthy AI. Risk taxonomies, global policy efforts, and living repositories all help us see the full picture of what responsible innovation looks like.
But the work is far from done.
As AI systems grow more autonomous and embedded in our lives, the ability to foresee, measure, and address risk isn’t a luxury—it’s a strategic necessity. For leaders navigating the intersection of AI, business, and human behavior, the ability to act on these insights will define not just success, but survival.
In the era of general-purpose AI, performance may open doors—but trust will keep them open.
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About the author
Adriana Vela is a best-selling author and CEO of MarketTecNexus, a consulting practice that helps companies scale their businesses with AI Business Scale Up™ solutions, which include AI business systems, business process engineering, NeuroAI implementation, and data management, among others. Named one of the “10 Most Empowering Women Leaders of 2021”, she is a multiple award-winning entrepreneur, an AI strategist, and a certified business and brain science leadership and performance optimization coach. She is the creator of the Human-First Performance Systems™ and the Coaching-as-a-Benefit™ system.
