Webinar Description
Key Takeaways
- Examines why leaderboard rankings alone provide insufficient guidance for selecting production-ready large language models
- Presents findings from the Phare LLM benchmark, which evaluates 71 models on safety and security criteria
- Addresses persistent vulnerabilities including jailbreaks, hallucinations and biased outputs across leading model providers
- Designed for CISOs, security architects, AI engineers and product managers responsible for deploying LLMs in enterprise environments
- Provides practical frameworks for transitioning from public benchmarks to red teaming and continuous monitoring
Introduction
Giskard AI is hosting a live online session examining the gap between large language model benchmark performance and real-world safety requirements. Titled “Choosing Safer LLMs: From LLM Benchmarks to Your Production Agents,” the 45-minute event targets AI practitioners and security professionals who must evaluate and deploy language models in production environments. The session addresses a growing concern within enterprise AI adoption: models that achieve impressive scores on public leaderboards may still exhibit critical vulnerabilities when deployed in operational contexts.
As organisations accelerate their integration of generative AI into customer-facing applications and internal workflows, the consequences of selecting an inadequately vetted model have become increasingly significant. Regulatory scrutiny of AI systems continues to intensify globally, while high-profile incidents involving model failures have heightened awareness of reputational and operational risks. This session arrives at a moment when the distance between academic evaluation methods and practical deployment requirements has become a pressing concern for technical and security leadership alike.
About This Event
The virtual session is led by Pierre Le Jeune, Lead AI Researcher at Giskard, who will translate research findings into actionable guidance for practitioners. The presentation draws on Giskard’s independent evaluation work, including results from the Phare LLM benchmark and the organisation’s StereoTales study examining stereotypes and harmful outputs in open-ended AI generation.
The format combines research presentation with practical recommendations, followed by a question-and-answer segment. This structure allows attendees to engage directly with the research team on specific deployment scenarios and evaluation challenges they face within their own organisations.
The Limitations of Leaderboard-Driven Model Selection
Public benchmarks have become the default mechanism for comparing large language models, offering standardised metrics that allow rapid comparison across dozens of competing systems. However, these evaluations typically emphasise capability measures such as reasoning, coding proficiency and general knowledge retrieval. Safety and security characteristics receive comparatively less attention in the rankings that dominate industry discourse.
The session challenges the assumption that high benchmark performance correlates with production readiness. A model may demonstrate exceptional performance on standardised tests while remaining susceptible to adversarial prompts, generating hallucinated information with apparent confidence, or producing outputs that reflect harmful stereotypes. These failure modes often emerge only under the specific conditions of real-world deployment, where users interact with systems in unpredictable ways and adversarial actors actively probe for exploitable weaknesses.
For organisations deploying AI agents that interact with customers, process sensitive information or make consequential recommendations, these hidden vulnerabilities represent material risks that leaderboard rankings fail to capture.
Phare Benchmark Findings and Safety Evaluation
Central to the session is the presentation of results from the Phare LLM benchmark, an independent evaluation framework developed by Giskard that assesses 71 models specifically on safety and security dimensions. Unlike capability-focused benchmarks, Phare examines how models respond to adversarial inputs, their propensity for generating harmful content, and their resilience against common exploitation techniques.
The benchmark evaluates models from multiple providers, including recent releases such as GPT 5.5, Claude 5 Sonnet, Kimi K2.6 and DeepSeek V4. By testing across this range of systems, the research identifies patterns in safety performance that cut across different architectural approaches and training methodologies. The findings reveal that safety characteristics do not necessarily track with overall capability scores, meaning that model selection decisions based purely on performance metrics may inadvertently introduce significant risk exposure.
The StereoTales study, also featured in the presentation, specifically examines how models handle open-ended generation tasks where harmful stereotypes and biased outputs are more likely to emerge. This research addresses a particularly challenging aspect of model safety: the subtle ways in which training data biases manifest in generated content, often in contexts where explicit safety filters fail to intervene.
From Benchmarks to Production Monitoring
The session moves beyond evaluation into practical guidance for organisations deploying language models in production environments. This includes frameworks for conducting internal red teaming exercises that probe for vulnerabilities specific to particular use cases and deployment contexts. Generic benchmark results, while informative, cannot substitute for targeted testing against the specific risks an organisation faces.
Continuous monitoring represents another critical component of the practical recommendations. Production AI systems encounter inputs and usage patterns that differ substantially from controlled evaluation environments. Effective monitoring systems must detect emerging failure modes, track safety-relevant metrics over time, and provide early warning when model behaviour begins to drift from acceptable parameters.
The transition from benchmark evaluation to operational oversight requires organisations to develop internal capabilities for ongoing assessment rather than treating model selection as a one-time decision. As models are updated, fine-tuned or replaced, safety characteristics may change in ways that require renewed evaluation.
Who Should Attend
The session is designed for professionals who bear responsibility for AI system selection, deployment and governance within their organisations. Chief Information Security Officers and security architects will find relevant material on threat vectors specific to language model deployments and frameworks for incorporating AI safety into broader security programmes.
Heads of AI and machine learning engineers responsible for technical implementation will benefit from the detailed benchmark findings and practical evaluation methodologies. AI product managers tasked with balancing capability requirements against risk considerations will gain frameworks for making more informed trade-off decisions. Researchers involved in auditing or validating AI systems will find the independent benchmark methodology and findings directly applicable to their work.
The content assumes familiarity with large language model concepts and enterprise deployment considerations, making it most suitable for practitioners already working with these technologies rather than those seeking introductory material.
The Broader Context of AI Governance
This session reflects a maturing conversation within the AI industry about the gap between model capabilities and deployment readiness. As generative AI moves from experimental applications into mission-critical systems, the standards for acceptable risk have tightened considerably. Regulatory frameworks in multiple jurisdictions now impose specific requirements on AI system transparency, safety testing and ongoing monitoring.
The challenge for practitioners lies in translating these governance requirements into concrete technical practices. Independent benchmarks focused on safety and security characteristics provide one essential input, but they must be complemented by organisation-specific evaluation, continuous monitoring and clear escalation procedures when problems emerge. The session aims to bridge the gap between high-level governance principles and the practical decisions that AI teams make when selecting and deploying language models.