BRK423 - Forging Modern Security with Copilot for Security
If you will be delivering this session, consult the session-delivery-resources page for slides, demo scripts, and other resources.
Session Desciption
Discover how Copilot for Security modernizes security operations and defense intelligence through Generative AI and Microsoft’s Threat Intelligence. A fusion of innovation, vigilance, and adaptability where efficiency and excellence converge.
Learning Outcomes
- Learn how Copilot for Security can help streamline security workflows and processes, making them more efficient and effective.
- Discover how Copilot for Security can enhance security visibility and situational awareness, providing better insights into potential threats.
- See how Copilot for Security can improve security decision making and response time, enabling quicker and more accurate responses to threats.
- Learn how Copilot for Security can integrate with existing security tools and data sources, providing a seamless and comprehensive security solution.
- Understand how organizations can leverage Copilot for Security to resolve security knowledge gaps.
Technology Used
- Microsoft Copilot for Security
- Microosft Defender Threat Intelligence
- Microsoft Defender XDR
- Microsoft Intune
- KQL
Additional Resources and Continued Learning
| Resources | Links | Description |
|---|---|---|
| Docs | Docs | Learn more about Copilot for Security |
| Community | Community | Join the community |
| Prompt Library | Prompts | Get Ideas for Security Prompts |
| Blog | News and Content | Keep current on the latest CfS updates |
Content Owners
Responsible AI
Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments. Many of these resources can be found at https://aka.ms/RAI. Microsoft’s approach to responsible AI is grounded in our AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the Azure OpenAI service Transparency note to be informed about risks and limitations.
The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. Azure AI Content Safety provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. We also have an interactive Content Safety Studio that allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following quickstart documentation guides you through making requests to the service.
Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using Performance and Quality and Risk and Safety evaluators. You also have the ability to create and evaluate with custom evaluators.
You can evaluate your AI application in your development environment using the Azure AI Evaluation SDK. Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the prompt flow sdk to evaluate your system, you can follow the quickstart guide. Once you execute an evaluation run, you can visualize the results in Azure AI Studio.