Sure! Here’s the translation into American English:
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With the growing interest in adopting generative artificial intelligence applications, Amazon Bedrock is established as a fundamental tool for organizations looking to innovate securely in managing their data. A recent study on the AWS Generative AI Adoption Index indicates that 45% of IT leaders surveyed plan to prioritize such tools in their budgets for 2025. This trend underscores the urgent need to protect against misconfigurations that could result in data leaks or unauthorized access to models.
Amazon Bedrock includes enterprise-grade security features that ensure data privacy through encryption both in transit and at rest, allowing organizations to maintain full control over their encryption keys. Additionally, it ensures that the information entered into its models is not shared with providers or used for training foundational models, reinforcing a high level of confidentiality. Its security measures encompass crucial compliance regulations such as ISO, HIPAA, and GDPR, making it a suitable option for highly regulated industries.
To further strengthen this security infrastructure, AWS has partnered with Datadog, which offers comprehensive security monitoring in artificial intelligence environments. The integration of Datadog Cloud Security allows for real-time identification of risks and misconfigurations, providing a holistic view of the AI infrastructure and prioritizing findings based on their business criticality.
Recently, Datadog announced new security capabilities that facilitate the detection and correction of misconfigurations in Amazon Bedrock before they escalate into security incidents. This integration ensures that organizations can implement robust security controls while leveraging Amazon Bedrock’s capabilities.
The partnership between AWS and Datadog responds to the growing demand for comprehensive security solutions in a context where the adoption of AI technologies is accelerating. The interconnection between both platforms enables companies to prioritize security risks, enriching findings with observational data and strengthening their security posture in AI implementations.
As regulations related to artificial intelligence evolve, it becomes crucial for organizations not only to focus on the risks associated with this technology but also to contextualize them within a broader framework that addresses risks of identity exposures and misconfigurations. In this regard, Datadog emphasizes the importance of detecting such misconfigurations to prevent cyberattacks that could compromise the integrity of AI models.
With this approach, organizations can not only accelerate their adoption of artificial intelligence but do so securely and responsibly, complying with emerging regulations and ensuring the protection of their data.
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If you need anything else, feel free to ask!
Source: MiMub in Spanish
