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Before yesterdayMicrosoft Security Blog

Enhancing AI security through global AI red teaming

✇Microsoft Security Blog
By: Ram Shankar Siva Kumar
27 July 2026 at 12:25

Most AI safety testing still happens inside the walls of individual organizations. That has resulted in a fundamental disconnect: many of the highest-risk failure modes in modern AI systems require deep domain expertise, multilingual context, or regional understanding that no single internal team can fully replicate on its own. 

As frontier models become more capable, the attack surface expands with them. AI red teaming is no longer just about prompt injection or content safety edge cases. It increasingly involves security operations, misuse scenarios, multilingual harms, alignment failures, and domain-specific abuse patterns that can vary significantly across geographies and languages. 

Microsoft’s AI Red Team has observed that meaningful testing of advanced AI systems- and models similarly requires broader participation from researchers and practitioners who operate outside traditional corporate security boundaries. To address that gap, today we are announcing the External Red Team Alliance (EXTRA), a formalized global extension of Microsoft’s AI Red Team designed to support and encourage external expertise to advance AI safety and security testing.  We are proud to share we are funding the development of new AI safety assessments on six continents through unrestricted gifts. 

Building a global alliance

EXTRA is a two-part initiative focused on expanding AI safety research and strengthening external collaboration. 

The first component supports a global academic network focused on advancing AI safety and security research. Microsoft’s AI Red Team has provided unrestricted gifts to 18 university labs spanning six continents. The goal is intentionally broad: support researchers who are already investigating difficult, unresolved questions in AI safety and help them continue pushing that work forward independently. 

Some of the supporting institutions include: 

“Academic research is critical to understanding the cyber security landscape and finding solutions that work for all of society – and partnerships like this with industry are essential to delivering on that promise. Through partnerships, civil society and public institutions researchers gain access to frontier technology to understand how models work and bring their expertise to the task of determining risk and developing more effective countermeasures for the benefit of society as a whole.”

Nicolas Papernot, professor, University of Toronto. 

The second component of EXTRA focuses on operational collaboration. Microsoft is building a distributed network of specialists who can participate directly in red teaming highly specialized areas where deeper expertise is required. That includes researchers, practitioners, and regional experts who understand specific attack classes, languages, cultural contexts, or technical domains that internal teams may not fully cover alone. 

Beyond expanding participation, EXTRA is also intended to help advance the science of AI safety evaluation. By bringing together academic researchers, security practitioners, and domain experts from around the world, the initiative aims to contribute to the development of more robust methodologies and testing practices for increasingly capable AI systems. Today’s cybersecurity ecosystem depends on coordinated vulnerability research, responsible disclosure programs, academic inquiry, and global communities of independent security researchers who routinely identify risks that vendors alone would not find. Likewise, advancing AI safety will benefit from ongoing contributions from experts across institutions, disciplines, and geographies to identify emerging threats, strengthen safeguards, and improve evaluation practices. 

“As frontier model capabilities advance, they create new risk opportunities, particularly in low-resource settings. Partnerships, such as this EXTRA, bring greater attention to the study of the local risk landscape and can enable broader impact of the work carried out around the globe in smaller academic settings.”

Balaraman Ravindran, head of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI) at IIT Madras. 

What the research focuses on

The research areas funded through EXTRA reflect several of the emerging areas Microsoft’s AI Red Team continues to encounter when evaluating advanced AI systems. 

Some universities are examining the cybersecurity implications of AI systems themselves — including how models can be attacked, manipulated, or abused in operational environments. Other labs are exploring the inverse problem: how AI systems can assist defenders and improve cyber operations. 

The structure of the program is intentional. The funding is unrestricted because the objective is not to direct research outcomes toward product requirements or predefined deliverables. The goal is to strengthen independent safety research capacity globally and create stronger long-term collaboration between academia and operational AI security teams. 

Why this matters

“Managing frontier AI risk requires more than internal safeguards. It requires continuous engagement with experts who understand how these systems behave across different technical, linguistic, and cultural contexts. EXTRA reflects Microsoft’s broader Frontier Governance Framework approach: combining rigorous internal governance with external support and collaboration to better identify, assess, and mitigate emerging risks as AI capabilities advance. By supporting independent research and building stronger connections with universities and specialists around the world, we are helping strengthen the broader ecosystem needed for trustworthy and secure AI development,” says Natasha Crampton, Chief Responsible AI Officer, Microsoft 

Governments too are increasingly focused on understanding the capabilities and security implications of frontier AI systems to strengthen resilience. But just as coordinated international research helped unlock the benefits of previous technological revolutions, diverse expertise from researchers and practitioners around the world is essential to identify emerging threats, improve defenses, and build greater confidence in AI systems.   

“Frontier AI is already shaping the future of both cybersecurity and national security. Understanding how these systems can be misused, and identifying risks before they become real-world threats, requires expertise that spans institutions, disciplines, and borders.” says Mike Yeh, VP & Deputy General Counsel, Customer Security and Trust, Microsoft 

AI red teaming is becoming more interdisciplinary, multilingual, and globally distributed. The expertise needed to identify meaningful failure modes increasingly lives across universities, independent research communities, and regional specialists. 

EXTRA reflects a practical shift in how AI security testing must operate going forward. External expertise is no longer supplemental to red teaming; in many cases, it is essential. 

Microsoft would like to thank these people for their important contributions with this project: Steph Ballard, Blake Bullwinkel, Nicholas Butts, Janelle Bryant, Kaja Ciglic, Hector de Rivoire, Eugenia Kim, Amanda Minnich, Shujaat Mirza, Jingxia Ni, Saphir Qi, Giorgio Severi, Hilary Solan, Hiwot Tesfaye, Sam Vaughan, Marguerita Wicklander, and the many teams at these schools around the world who helped coordinate 

The post Enhancing AI security through global AI red teaming appeared first on Microsoft Security Blog.

Enhancing AI security through global AI red teaming

✇Microsoft Security Blog
By: Ram Shankar Siva Kumar
27 July 2026 at 12:25

Most AI safety testing still happens inside the walls of individual organizations. That has resulted in a fundamental disconnect: many of the highest-risk failure modes in modern AI systems require deep domain expertise, multilingual context, or regional understanding that no single internal team can fully replicate on its own. 

As frontier models become more capable, the attack surface expands with them. AI red teaming is no longer just about prompt injection or content safety edge cases. It increasingly involves security operations, misuse scenarios, multilingual harms, alignment failures, and domain-specific abuse patterns that can vary significantly across geographies and languages. 

Microsoft’s AI Red Team has observed that meaningful testing of advanced AI systems- and models similarly requires broader participation from researchers and practitioners who operate outside traditional corporate security boundaries. To address that gap, today we are announcing the External Red Team Alliance (EXTRA), a formalized global extension of Microsoft’s AI Red Team designed to support and encourage external expertise to advance AI safety and security testing.  We are proud to share we are funding the development of new AI safety assessments on six continents through unrestricted gifts. 

Building a global alliance

EXTRA is a two-part initiative focused on expanding AI safety research and strengthening external collaboration. 

The first component supports a global academic network focused on advancing AI safety and security research. Microsoft’s AI Red Team has provided unrestricted gifts to 18 university labs spanning six continents. The goal is intentionally broad: support researchers who are already investigating difficult, unresolved questions in AI safety and help them continue pushing that work forward independently. 

Some of the supporting institutions include: 

“Academic research is critical to understanding the cyber security landscape and finding solutions that work for all of society – and partnerships like this with industry are essential to delivering on that promise. Through partnerships, civil society and public institutions researchers gain access to frontier technology to understand how models work and bring their expertise to the task of determining risk and developing more effective countermeasures for the benefit of society as a whole.”

Nicolas Papernot, professor, University of Toronto. 

The second component of EXTRA focuses on operational collaboration. Microsoft is building a distributed network of specialists who can participate directly in red teaming highly specialized areas where deeper expertise is required. That includes researchers, practitioners, and regional experts who understand specific attack classes, languages, cultural contexts, or technical domains that internal teams may not fully cover alone. 

Beyond expanding participation, EXTRA is also intended to help advance the science of AI safety evaluation. By bringing together academic researchers, security practitioners, and domain experts from around the world, the initiative aims to contribute to the development of more robust methodologies and testing practices for increasingly capable AI systems. Today’s cybersecurity ecosystem depends on coordinated vulnerability research, responsible disclosure programs, academic inquiry, and global communities of independent security researchers who routinely identify risks that vendors alone would not find. Likewise, advancing AI safety will benefit from ongoing contributions from experts across institutions, disciplines, and geographies to identify emerging threats, strengthen safeguards, and improve evaluation practices. 

“As frontier model capabilities advance, they create new risk opportunities, particularly in low-resource settings. Partnerships, such as this EXTRA, bring greater attention to the study of the local risk landscape and can enable broader impact of the work carried out around the globe in smaller academic settings.”

Balaraman Ravindran, head of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI) at IIT Madras. 

What the research focuses on

The research areas funded through EXTRA reflect several of the emerging areas Microsoft’s AI Red Team continues to encounter when evaluating advanced AI systems. 

Some universities are examining the cybersecurity implications of AI systems themselves — including how models can be attacked, manipulated, or abused in operational environments. Other labs are exploring the inverse problem: how AI systems can assist defenders and improve cyber operations. 

The structure of the program is intentional. The funding is unrestricted because the objective is not to direct research outcomes toward product requirements or predefined deliverables. The goal is to strengthen independent safety research capacity globally and create stronger long-term collaboration between academia and operational AI security teams. 

Why this matters

“Managing frontier AI risk requires more than internal safeguards. It requires continuous engagement with experts who understand how these systems behave across different technical, linguistic, and cultural contexts. EXTRA reflects Microsoft’s broader Frontier Governance Framework approach: combining rigorous internal governance with external support and collaboration to better identify, assess, and mitigate emerging risks as AI capabilities advance. By supporting independent research and building stronger connections with universities and specialists around the world, we are helping strengthen the broader ecosystem needed for trustworthy and secure AI development,” says Natasha Crampton, Chief Responsible AI Officer, Microsoft 

Governments too are increasingly focused on understanding the capabilities and security implications of frontier AI systems to strengthen resilience. But just as coordinated international research helped unlock the benefits of previous technological revolutions, diverse expertise from researchers and practitioners around the world is essential to identify emerging threats, improve defenses, and build greater confidence in AI systems.   

“Frontier AI is already shaping the future of both cybersecurity and national security. Understanding how these systems can be misused, and identifying risks before they become real-world threats, requires expertise that spans institutions, disciplines, and borders.” says Mike Yeh, VP & Deputy General Counsel, Customer Security and Trust, Microsoft 

AI red teaming is becoming more interdisciplinary, multilingual, and globally distributed. The expertise needed to identify meaningful failure modes increasingly lives across universities, independent research communities, and regional specialists. 

EXTRA reflects a practical shift in how AI security testing must operate going forward. External expertise is no longer supplemental to red teaming; in many cases, it is essential. 

Microsoft would like to thank these people for their important contributions with this project: Steph Ballard, Blake Bullwinkel, Nicholas Butts, Janelle Bryant, Kaja Ciglic, Hector de Rivoire, Eugenia Kim, Amanda Minnich, Shujaat Mirza, Jingxia Ni, Saphir Qi, Giorgio Severi, Hilary Solan, Hiwot Tesfaye, Sam Vaughan, Marguerita Wicklander, and the many teams at these schools around the world who helped coordinate 

The post Enhancing AI security through global AI red teaming appeared first on Microsoft Security Blog.

Introducing RAMPART and Clarity: Open source tools to bring safety into Agent development workflow

✇Microsoft Security Blog
By: Ram Shankar Siva Kumar
20 May 2026 at 11:00

The AI systems shipping inside enterprises today are fundamentally different from the ones we were building even two years ago, because they have moved well past answering questions and into accessing your email, retrieving records from your CRM, writing and executing code, and taking actions on your behalf across dozens of connected systems. That shift from “generate text” to “do things in the world” changes the safety equation entirely, because an agent that can act can also potentially act in ways nobody intended.

Today Microsoft is open-sourcing two tools designed to help engineers: Microsoft RAMPART, an agent test framework for encoding adversarial and benign scenarios as repeatable tests that can run in CI, making it easy to turn red-team findings and AI incidents into lasting regression coverage; and Clarity, a structured sounding board that helps teams figure out whether they are building the right thing before they write a single line of code.

We built these tools because we believe that AI safety has to become a continuous engineering discipline rather than a periodic checkpoint, and we think the best way to make that happen is to put practical, open tools in the hands of the people doing the building.

Why we are investing in this

  1. Helping teams think through the “why,” before the “how” of software building: In the vibe coding era, execution is easy and the harder question is the “why.” The most expensive safety failures we see almost always trace back to design mistakes that nobody questioned early enough, long before any adversary got involved — say, when a product team decided their agent should have access to a tool, or handle a particular user flow, without fully working through what could go wrong. By the time a red team engagement surfaces the issue, the system is largely built, and addressing it means going back to the drawing board. We wanted to give product managers and engineers a way to pressure-test their assumptions at the start of a project, when changing course is cheap and the right conversation can save months of rework.
  2. Scaling the lessons of red teaming across the industry. The techniques that uncover vulnerabilities in one agentic product almost always shed light on another. A cross-prompt injection attack that works against one system will often work, with minor variations, against a customer service agent or a coding assistant. But those lessons tend to stay locked inside individual engagement reports. Our goal was to build a system where the lessons of red teaming exercises can be turned into runnable engineering assets.  
  3. Making incidents reproducible and mitigations verifiable. If something goes wrong in production AI systems, the team responding needs to do two things quickly: replicate the incident so they understand exactly what happened, and verify that whatever fix they ship actually holds up against variants of the original attack. Both of those tasks are harder than they sound with probabilistic LLMpowered systems, and most teams end up doing them manually in an ad hoc way. We wanted tooling that is purpose-built for exactly this workflow, so that incident response becomes a repeatable engineering process rather than a scramble.

RAMPART: Continuous safety testing for agentic AI

RAMPART is an open-source testing framework that brings red teaming techniques directly into the development workflow. It is built on top of PyRIT, Microsoft’s open automation framework for red teaming generative AI systems so that RAMPART leverages the best in class, out of the box adversarial tests. Where PyRIT is optimized for black-box discovery by security researchers after the system is built, RAMPART is built for engineers as the system is being built.

The developer experience will feel familiar to anyone who has written integration tests. Teams write standard pytest tests that describe scenarios drawn from their threat model. Each test connects to the agent through a thin adapter, orchestrates an interaction, and evaluates observable outcomes. Tests return a clear pass or fail signal and can be gated in CI just like any other integration test. When a new tool or data source is added to the agent, the corresponding safety test can be added in the same pull request.

RAMPART is different from conventional testing in the following ways:

  1. Built for prompt injection attacks: RAMPART’s most mature coverage today focuses on cross-prompt injection attacks, scenarios in which an agent retrieves or processes potentially poisoned content from documents, emails, tickets, or other data sources that manipulate its behavior indirectly.  New threat categories can be added incrementally as attack patterns evolve, and the framework’s extension points are all defined as Python protocols, so integration stays lightweight even for complex agent architectures.<
  2. Built for probabilistic behavior: Because LLM behavior is probabilistic, RAMPART supports statistical trials. The same test can run multiple times with policies like “this action must be safe in at least 80 percent of runs.” This reflects how agents actually behave in production far more accurately than single-shot validation ever could.
  3. Built to reproduce your AI red team findings and AI incidents: RAMPART is designed to work alongside dedicated red teaming, and the two reinforce each other. Findings from a red team engagement can be encoded as RAMPART tests, which means the issue is permanently covered, runs on every change, and never silently regresses. The ownership model is intentionally flipped from the traditional approach: engineers write the tests, engineers run them, and engineers treat failures like any other bug. The framework supplies the attack strategies, adversarial payload generation, and evaluation logic. The test author focuses on expressing expectations about what their agent should and should not do.

Agent safety ultimately comes down to what the agent does, which means evaluators need to look at which tools it invokes, what side effects occur, and whether those actions stay within expected boundaries. RAMPART’s evaluators are designed to inspect all of that. They are composable, so teams can combine them with boolean logic to express nuanced safety conditions rather than relying on a single binary signal.

Clarity: Helping check software engineering assumptions

Where most AI tools are designed to help teams execute faster, Clarity was designed by Microsoft to help them figure out whether they are executing on the right thing in the first place. It asks the kinds of questions that experienced architects, product managers, and safety engineers would ask, the ones that are easy to skip when a team is excited about building something new.

Consider a team that wants to add real-time collaboration to a document editor. Instead of jumping straight to implementation options, Clarity will ask what happens when two people edit the same paragraph at the same time, and whether the team actually needs true real-time collaboration with cursors and presence indicators, or whether “nobody loses their work” is the real requirement. Those two answers can lead to very different architectures with very different failure modes, and getting clarity on that distinction early can save months of rework.

Clarity runs as a desktop app, a web UI, or embedded directly in a coding agent. It guides engineers through structured conversations covering problem clarification, solution exploration, failure analysis, and decision tracking. As the conversation progresses, the results are written to a .clarity-protocol/ directory in the repo as plain, human-readable markdown files that get committed, reviewed in pull requests, and diffed just like source code. They capture the problem statement, the solution rationale, the failure analysis, and the key decisions made along the way.

The failure analysis deserves a closer look, because it goes well beyond what a single reviewer would typically catch. Multiple AI “thinkers” independently examine the system from different angles, including security, human factors, adversarial scenarios, and operational concerns. The team then works through the results together with Clarity, grouping related failures, tracing causal chains, and building management plans.  

Clarity also tracks staleness across these documents, because they form a dependency graph. When a problem statement changes, Clarity knows that the solution description and failure analysis might need revisiting and nudges the team to do so. Important decisions are captured with their criteria, the options considered, and the rationale behind each choice, so that six months later anyone on the team can revisit the full reasoning, including which alternatives were ruled out and why.

The .clarity-protocol/ directory becomes a shared artifact that everyone on the team can see and contribute to, and for stakeholders who need a summary before a review, Clarity can generate a review packet that tells a coherent narrative.

RAMPART and Clarity are part of a broader movement toward spec-driven, engineering-native AI safety. They complement Microsoft’s work on policy-to-measurement systems: Clarity helps teams clarify design intent and capture assumptions; RAMPART gives teams the building blocks to write concrete agent safety testsand keep them running as agents evolve.. Together, these approaches move AI safety from a one-time review to a set of living artifacts that developers can use throughout the lifecycle.

RAMPART and Clarity available now

Both RAMPART and Clarity are available today as open source projects from Microsoft.

We look forward to working with the community. For feedback, and partnership in deploying this in the enterprise setting, please contact aisafetytools@microsoft.com.

Contributions

Microsoft RAMPART is led by Bashir Partovi with contributions from Elliot H Omiya, Richard Lundeen, Nina Chikanov, Spencer Schoenberg, and Toby Kohlenberg. Clarity is joint project from Yonatan Zunger, Dharmin Shah, Elliot H Omiya, Eve Kazarian, Sarah Cooley, and Neil Coles. We would like to thank Minsoo Thigpen, Abby Palia, Mehrnoosh Sameki, Hilary Solan, Elliot Volkman, Pete Bryan, Roman Lutz, and Shiven Chawla for their helpful comments.

The post Introducing RAMPART and Clarity: Open source tools to bring safety into Agent development workflow appeared first on Microsoft Security Blog.

Introducing RAMPART and Clarity: Open source tools to bring safety into Agent development workflow

✇Microsoft Security Blog
By: Ram Shankar Siva Kumar
20 May 2026 at 11:00

The AI systems shipping inside enterprises today are fundamentally different from the ones we were building even two years ago, because they have moved well past answering questions and into accessing your email, retrieving records from your CRM, writing and executing code, and taking actions on your behalf across dozens of connected systems. That shift from “generate text” to “do things in the world” changes the safety equation entirely, because an agent that can act can also potentially act in ways nobody intended.

Today Microsoft is open-sourcing two tools designed to help engineers: Microsoft RAMPART, an agent test framework for encoding adversarial and benign scenarios as repeatable tests that can run in CI, making it easy to turn red-team findings and AI incidents into lasting regression coverage; and Clarity, a structured sounding board that helps teams figure out whether they are building the right thing before they write a single line of code.

We built these tools because we believe that AI safety has to become a continuous engineering discipline rather than a periodic checkpoint, and we think the best way to make that happen is to put practical, open tools in the hands of the people doing the building.

Why we are investing in this

  1. Helping teams think through the “why,” before the “how” of software building: In the vibe coding era, execution is easy and the harder question is the “why.” The most expensive safety failures we see almost always trace back to design mistakes that nobody questioned early enough, long before any adversary got involved — say, when a product team decided their agent should have access to a tool, or handle a particular user flow, without fully working through what could go wrong. By the time a red team engagement surfaces the issue, the system is largely built, and addressing it means going back to the drawing board. We wanted to give product managers and engineers a way to pressure-test their assumptions at the start of a project, when changing course is cheap and the right conversation can save months of rework.
  2. Scaling the lessons of red teaming across the industry. The techniques that uncover vulnerabilities in one agentic product almost always shed light on another. A cross-prompt injection attack that works against one system will often work, with minor variations, against a customer service agent or a coding assistant. But those lessons tend to stay locked inside individual engagement reports. Our goal was to build a system where the lessons of red teaming exercises can be turned into runnable engineering assets.  
  3. Making incidents reproducible and mitigations verifiable. If something goes wrong in production AI systems, the team responding needs to do two things quickly: replicate the incident so they understand exactly what happened, and verify that whatever fix they ship actually holds up against variants of the original attack. Both of those tasks are harder than they sound with probabilistic LLMpowered systems, and most teams end up doing them manually in an ad hoc way. We wanted tooling that is purpose-built for exactly this workflow, so that incident response becomes a repeatable engineering process rather than a scramble.

RAMPART: Continuous safety testing for agentic AI

RAMPART is an open-source testing framework that brings red teaming techniques directly into the development workflow. It is built on top of PyRIT, Microsoft’s open automation framework for red teaming generative AI systems so that RAMPART leverages the best in class, out of the box adversarial tests. Where PyRIT is optimized for black-box discovery by security researchers after the system is built, RAMPART is built for engineers as the system is being built.

The developer experience will feel familiar to anyone who has written integration tests. Teams write standard pytest tests that describe scenarios drawn from their threat model. Each test connects to the agent through a thin adapter, orchestrates an interaction, and evaluates observable outcomes. Tests return a clear pass or fail signal and can be gated in CI just like any other integration test. When a new tool or data source is added to the agent, the corresponding safety test can be added in the same pull request.

RAMPART is different from conventional testing in the following ways:

  1. Built for prompt injection attacks: RAMPART’s most mature coverage today focuses on cross-prompt injection attacks, scenarios in which an agent retrieves or processes potentially poisoned content from documents, emails, tickets, or other data sources that manipulate its behavior indirectly.  New threat categories can be added incrementally as attack patterns evolve, and the framework’s extension points are all defined as Python protocols, so integration stays lightweight even for complex agent architectures.<
  2. Built for probabilistic behavior: Because LLM behavior is probabilistic, RAMPART supports statistical trials. The same test can run multiple times with policies like “this action must be safe in at least 80 percent of runs.” This reflects how agents actually behave in production far more accurately than single-shot validation ever could.
  3. Built to reproduce your AI red team findings and AI incidents: RAMPART is designed to work alongside dedicated red teaming, and the two reinforce each other. Findings from a red team engagement can be encoded as RAMPART tests, which means the issue is permanently covered, runs on every change, and never silently regresses. The ownership model is intentionally flipped from the traditional approach: engineers write the tests, engineers run them, and engineers treat failures like any other bug. The framework supplies the attack strategies, adversarial payload generation, and evaluation logic. The test author focuses on expressing expectations about what their agent should and should not do.

Agent safety ultimately comes down to what the agent does, which means evaluators need to look at which tools it invokes, what side effects occur, and whether those actions stay within expected boundaries. RAMPART’s evaluators are designed to inspect all of that. They are composable, so teams can combine them with boolean logic to express nuanced safety conditions rather than relying on a single binary signal.

Clarity: Helping check software engineering assumptions

Where most AI tools are designed to help teams execute faster, Clarity was designed by Microsoft to help them figure out whether they are executing on the right thing in the first place. It asks the kinds of questions that experienced architects, product managers, and safety engineers would ask, the ones that are easy to skip when a team is excited about building something new.

Consider a team that wants to add real-time collaboration to a document editor. Instead of jumping straight to implementation options, Clarity will ask what happens when two people edit the same paragraph at the same time, and whether the team actually needs true real-time collaboration with cursors and presence indicators, or whether “nobody loses their work” is the real requirement. Those two answers can lead to very different architectures with very different failure modes, and getting clarity on that distinction early can save months of rework.

Clarity runs as a desktop app, a web UI, or embedded directly in a coding agent. It guides engineers through structured conversations covering problem clarification, solution exploration, failure analysis, and decision tracking. As the conversation progresses, the results are written to a .clarity-protocol/ directory in the repo as plain, human-readable markdown files that get committed, reviewed in pull requests, and diffed just like source code. They capture the problem statement, the solution rationale, the failure analysis, and the key decisions made along the way.

The failure analysis deserves a closer look, because it goes well beyond what a single reviewer would typically catch. Multiple AI “thinkers” independently examine the system from different angles, including security, human factors, adversarial scenarios, and operational concerns. The team then works through the results together with Clarity, grouping related failures, tracing causal chains, and building management plans.  

Clarity also tracks staleness across these documents, because they form a dependency graph. When a problem statement changes, Clarity knows that the solution description and failure analysis might need revisiting and nudges the team to do so. Important decisions are captured with their criteria, the options considered, and the rationale behind each choice, so that six months later anyone on the team can revisit the full reasoning, including which alternatives were ruled out and why.

The .clarity-protocol/ directory becomes a shared artifact that everyone on the team can see and contribute to, and for stakeholders who need a summary before a review, Clarity can generate a review packet that tells a coherent narrative.

RAMPART and Clarity are part of a broader movement toward spec-driven, engineering-native AI safety. They complement Microsoft’s work on policy-to-measurement systems: Clarity helps teams clarify design intent and capture assumptions; RAMPART gives teams the building blocks to write concrete agent safety testsand keep them running as agents evolve.. Together, these approaches move AI safety from a one-time review to a set of living artifacts that developers can use throughout the lifecycle.

RAMPART and Clarity available now

Both RAMPART and Clarity are available today as open source projects from Microsoft.

We look forward to working with the community. For feedback, and partnership in deploying this in the enterprise setting, please contact aisafetytools@microsoft.com.

Contributions

Microsoft RAMPART is led by Bashir Partovi with contributions from Elliot H Omiya, Richard Lundeen, Nina Chikanov, Spencer Schoenberg, and Toby Kohlenberg. Clarity is joint project from Yonatan Zunger, Dharmin Shah, Elliot H Omiya, Eve Kazarian, Sarah Cooley, and Neil Coles. We would like to thank Minsoo Thigpen, Abby Palia, Mehrnoosh Sameki, Hilary Solan, Elliot Volkman, Pete Bryan, Roman Lutz, and Shiven Chawla for their helpful comments.

The post Introducing RAMPART and Clarity: Open source tools to bring safety into Agent development workflow appeared first on Microsoft Security Blog.

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