How Wiz is empowering organizations to remediate security risks faster with Amazon Bedrock

Wiz is a cloud security platform that enables organizations to secure everything they build and run in the cloud by rapidly identifying and removing critical risks. Over 40% of the Fortune 100 trust Wiz’s purpose-built cloud security platform to gain full-stack visibility, accurate risk prioritization, and enhanced business agility. Organizations can connect Wiz in minutes to scan the entire cloud environment without agents and identify the issues representing real risk. Security and cloud teams can then proactively remove risks and harden cloud environments with remediation workflows.

Artificial intelligence (AI) has revolutionized the way organizations function, paving the way for automation and improved efficiency in various tasks that were traditionally manual. One of these use cases is using AI in security organizations to improve security processes and increase your overall security posture. One of the major challenges in cloud security is discerning the best ways to resolve an identified issue in the most effective way to allow you to respond quickly.

Wiz has harnessed the power of generative AI to help organizations remove risks in their cloud environment faster. With Wiz’s new integration with Amazon Bedrock, Wiz customers can now generate guided remediation steps backed by foundation models (FMs) running on Amazon Bedrock to reduce their mean time to remediation (MTTR). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

“The Wiz and Amazon Bedrock integration enables organizations to further enhance security and improve remediation time by leveraging a choice of powerful foundation models to generate GenAI-powered remediation steps.”

– Vivek Singh, Senior Manager, Product Management-Tech, AWS AI

In this post, we share how Wiz uses Amazon Bedrock to generate remediation guidance for customers that allow them to quickly address security risks in their cloud environment.

Detecting security risks in the cloud with the Wiz Security Graph

Wiz scans cloud environments without agents and runs deep risk assessment across network exposures, vulnerabilities, misconfigurations, identities, data, secrets, and malware. Wiz stores the entire technology stack as well as any risks detected on the Wiz Security Graph, which is backed by Amazon Neptune. Neptune enables Wiz to quickly traverse the graph and understand interconnected risk factors in seconds and how they create an attack path. The Security Graph allows Wiz to surface these critical attack paths in the form of Wiz Issues. For example, a Wiz Issue can alert of a publicly exposed Amazon Elastic Compute Cloud (Amazon EC2) instance that is vulnerable, has admin permissions, and can access sensitive data. The following graph illustrates this attack path.

With its Security Graph, Wiz provides customers with pinpoint-accurate alerts on security risks in their environment, reduces the noise faced with traditional security tools, and enables organizations to focus on the most critical risks in their environment.

Remediating cloud risks with guided remediation provided by Amazon Bedrock

To help customers remediate security risks even faster, Wiz uses Amazon Bedrock to analyze metadata from Wiz Issues to generate effective remediation recommendations for customers. With Amazon Bedrock, Wiz combines its deep risk context with cutting-edge FMs to offer enhanced remediation guidance to customers. Customers can scale their remediation workflow and minimize their MTTR by generating straightforward-to-use copy-paste remediation steps that can be directly implemented into the tool of their choice, such as the AWS Command Line Interface (AWS CLI), Terraform, AWS CloudFormation, Pulumi, Go, and Python, or directly using the cloud environment console. The following screenshot showcases an example of the remediation steps generated by Amazon Bedrock for a Wiz Issue.

Wiz sends a prompt with all the relevant context around a security risk to Amazon Bedrock with instructions on how to present the results based on the target platform. Amazon Bedrock native APIs allow Wiz to select the best model for the use case to answer the request, so when it’s received, it’s parsed and presented in a straightforward manner in the Wiz portal.

To fully operationalize this functionality in production, the Wiz backend has a service running on Amazon Elastic Kubernetes Service (Amazon EKS) that receives the customer request to generate remediation steps, collects the context of the alert the customer wants to remediate, and runs personally identifiable information (PII) redaction on the data to remove any sensitive data. Then, another service running on Amazon EKS pulls the resulting data and sends it to Amazon Bedrock. Such a flow can run in each needed AWS Region supported by Amazon Bedrock to address any compliance needs of their customers. In addition, to secure the usage of Amazon Bedrock with least privilege, Wiz uses AWS permission sets and follows AWS best practices. The Wiz service sending the prompt to Amazon Bedrock has a dedicated AWS Identity and Access Management (IAM) role that allows it to communicate only with the specific Amazon Bedrock service and to only generate those requests. Amazon Bedrock also has restrictions to block any data coming from a non-authorized service. Using these AWS services and the Wiz Security Graph, Wiz helps its customers adopt the most advanced LLMs to speed up the process of addressing complex security issues in a straightforward and secure manner. The following diagram illustrates this architecture.

Wiz customers are already experiencing the advantages of our new AI-driven remediation:

“The faster we can remediate security risks, the more we can focus on driving broader strategic initiatives. With Wiz’s AI-powered remediation, we can quickly generate remediation steps that our security team and developers can simply copy-paste to remediate the issue.”

– Rohit Kohli, Deputy CISO, Genpact

By using Amazon Bedrock for generating AI-powered remediation steps, we learnt that security teams are able to minimize the time spent investigating complex risks by 40%, allowing them to focus on mitigating more risks. Furthermore, they are able to empower developers to remediate risks by removing the need for security expertise and providing them with exact steps to take. Not only does Wiz use AI to enhance security processes for customers, but it also makes it effortless for customers to securely adopt AI in their organization with its AI Security Posture Management capabilities, empowering them to protect their AI models while increasing innovation.

Conclusion

Using generative AI for generating enhanced remediation steps marks a significant advancement in the realm of problem-solving and automation. By harnessing the power of AI models powered by Amazon Bedrock, Wiz users can quickly remediate risks with straightforward remediation guidance, reducing manual efforts and improving MTTR. Learn more about Wiz and check out a live demo.

About the Authors

Shaked Rotlevi is a Technical Product Marketing Manager at Wiz focusing on AI security. Prior to Wiz she was a Solutions Architect at AWS working with public sector customers as well as a Technical Program Manager for a security service team. In her spare time she enjoys playing beach volleyball and hiking.

Itay Arbel is a Lead Product Manager at Wiz. Before joining Wiz, Itay was a product manager at Microsoft and did an MBA in Oxford University, majoring in high tech and emerging technologies. Itay is Wiz’s product lead for the effort of helping organizations securing their AI pipeline and usage of this new emerging technology.

Eitan Sela is a Generative AI and Machine Learning Specialist Solutions Architect at AWS. He works with AWS customers to provide guidance and technical assistance, helping them build and operate Generative AI and Machine Learning solutions on AWS. In his spare time, Eitan enjoys jogging and reading the latest machine learning articles.

Adi Avni is a Senior Solutions Architect at AWS based in Israel. Adi works with AWS ISV customers, helping them to build innovative, scalable and cost-effective solutions on AWS. In his spare time, he enjoys sports and traveling with family and friends.

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