Datadog LLM Observability: Revolutionizing AI Application Monitoring and Security

The advent of large language models (LLMs) has brought about a significant shift in the way AI applications are developed and deployed. However, as these models become increasingly complex, monitoring and securing them has become a major challenge. Datadog, a leading monitoring and security platform, has recently announced the general availability of LLM Observability, a solution designed to help organizations overcome these challenges.

LLM Observability: A Game-Changer for AI Application Development

With LLM Observability, companies can now efficiently monitor, improve, and secure their LLM applications. This solution provides visibility into each step of the LLM chain, enabling developers to identify the root cause of errors and unexpected responses such as hallucinations. Additionally, users can monitor operational metrics like latency and token usage to optimize performance and cost.

“WHOOP Coach is powered by the latest and greatest in LLM AI. Datadog’s LLM Observability allows our engineering teams to evaluate performance of model changes, monitor production performance and increase quality of Coach interactions. LLM Observability allows WHOOP to provide and maintain coaching for all our members 24/7,” said Bobby Johansen, Senior Director Software at WHOOP.

The Importance of LLM Observability in AI Application Development

The development and deployment of LLM applications have become increasingly complex, with multiple challenges arising from their non-deterministic nature and security risks. LLM Observability helps organizations overcome these challenges by providing deep visibility into the LLM chain, enabling them to detect drifts or biases, and resolve issues before they have a significant impact on the business or end-user experience.

“There’s a rush to adopt new LLM-based technologies, but organizations of all sizes and industries are finding it difficult to do so in a way that is both cost-effective and doesn’t negatively impact the end-user experience,” said Yrieix Garnier, VP of Product at Datadog.

The Skeleton Key Attack: A New Threat to LLM Security

A recent study has revealed a novel AI jailbreak technique, known as the Skeleton Key attack, which allows attackers to cause LLMs to produce typically forbidden responses, such as generating harmful content or violating ethical guidelines. This technique can convince the model to augment its behavior guidelines rather than change them outright, making the model comply with virtually any request despite the potential for offensive, harmful, or illegal output.

“This threat is in the jailbreak category and therefore relies on the attacker already having legitimate access to the AI model. In bypassing safeguards, Skeleton Key allows the user to cause the model to produce ordinarily forbidden behaviors, which could range from the production of harmful content to overriding its usual decision-making rules,” researchers note.

The Future of LLM Development: Overcoming Security Threats

As LLMs continue to evolve and become more pervasive in our daily lives, it is essential to address the security threats they pose. The Skeleton Key attack highlights the need for robust security measures to prevent malicious actors from exploiting LLMs for harmful purposes.

Datadog LLM Observability provides deep visibility into the LLM chain, enabling organizations to detect drifts or biases and resolve issues before they have a significant impact on the business or end-user experience.

In conclusion, the development and deployment of LLM applications require a comprehensive approach to monitoring and security. Datadog’s LLM Observability is a significant step forward in this direction, providing organizations with the tools they need to overcome the challenges of LLM development and ensure the security and integrity of their AI applications.

The Skeleton Key attack is a novel AI jailbreak technique that allows attackers to cause LLMs to produce typically forbidden responses, such as generating harmful content or violating ethical guidelines.