AI Security Meets MLOps: AIShield and Databricks Partners for Stronger AI Protection

Databricks: Leader in MLOps with Powerful Features

Databricks, the creator of the lakehouse category and leader in the MLOps market, offers a powerful Machine Learning platform that enables data science teams to accelerate end-to-end ML operations using an open lakehouse architecture. Their platform provides robust security features to ensure the seamless and efficient protection of ML models throughout the entire MLOps lifecycle. The Lakehouse Platform of Databricks is a widely used tool among data science teams globally, as it offers numerous benefits such as:

· Databricks enables collaboration and sharing among multiple users, allowing them to work on the same data and projects with ease

· With support for multiple programming languages such as Python, R, SQL, and Scala, Databricks enables data scientists to use their preferred language for data analysis and Machine Learning

· Databricks provides built-in libraries for Machine Learning, such as TensorFlow, Keras, PyTorch, and scikit-learn, making it easy to perform advanced Machine Learning tasks

AIShield: Innovating AI Security with API-Based Digital AI Protection

AIShield is an innovative API-based digital AI security product and solution, leveraging over 30 homegrown patents in AI Security to provide protection and resilience for AI-based application workloads across cloud, edge, and on-premises. Its full-stack AI AppSec solution offers threat-informed defense, vulnerability detection, and real-time attack detection and remediation to ensure the trust and security of AI systems. It offers distinctive features, including:

· Vulnerability testing for various Al/ML models against different attack types such as model extraction, evasion, poisoning, inference, sponge etc.

· Threat-informed defense generation and real-time prevention as well as monitoring of new attacks in the cloud, edge, on-premises, and on devices

· Active threat hunting with integration with SIEM/SOAR providers for remediation

With microservice-based REST-API offerings for seamless integration into MLOPS toolchains (with help of reference implementations and python packages), AIShield allows organizations to scale Al security initiatives for preventing incidents like autonomous vehicle mishaps and manipulated Al in clinical settings while ensuring trust and safety in Al systems.

The Partnership: A Powerful Solution for Securing AI-Based Application Workloads

With the partnership between AIShield and Databricks, the integration creates a powerful solution for securing AI-based application workloads across cloud, edge, and on-premises environments. The integration with Databricks provides additional benefits for data science teams, enabling them to embed security throughout the entire ML Ops lifecycle, from development to deployment.

The AIShield-Databricks integration provides model scanning and model detection and response, which enables Data Scientists and ML Engineers to add security to their models with no code or behavioral changes to their environment. As the model is loaded, it will be scanned by AIShield’s model scanner to ensure integrity as well as security. If an attack is detected, the integration will handle the response accordingly without any human interaction needed. With the peace of mind of ML models protected by AIShield, data science teams can focus their attention on building their advantage without sacrificing integrity or security. Additional resources for integrating AIShield with Databricks can be found on AIShield’s Github repository, which includes a readme file and reference implementation, as well as a video tutorial.

In conclusion, the partnership between AIShield and Databricks provides a powerful solution for securing AI-based application workloads across cloud, edge, and on-premises environments. The Databricks Lakehouse Platform allows data science teams to create, test, and deploy machine learning models quickly. With AIShield’s innovative AI security product and Databricks’ MLOps platform, data science teams can embed security throughout the entire ML Ops lifecycle, from development to deployment, without sacrificing integrity or security. As the global AI market continues to grow, the need for trustworthy and secure AI systems becomes more critical. This combined solution enables organizations to execute their AI strategies quickly and confidently without sacrificing security or efficiency.