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Secure coding learning that reflects real AI usage

Secure coding learning aligned to real AI usage, developer workflows, and modern software development practices.

AI-assisted development is already becoming part of everyday engineering work.

A developer may manually write core application logic in the morning, use AI to generate test coverage in the afternoon, then review AI-assisted pull requests before the end of the day. The workflows are fluid. The tooling changes quickly. Usage patterns evolve week to week, sometimes team to team.

Historically, secure coding learning operated on relatively fixed cycles — onboarding pathways, annual assignments, broad role-based training, or periodic awareness campaigns. Those models made sense when development tooling evolved more gradually and learning requirements stayed relatively stable over time.

AI-assisted development moves differently. Security teams now need a more responsive way to keep secure coding guidance connected to how developers are actually working in the moment.

Adaptive Learning is designed to help organizations align secure coding learning to real software development activity and software risk signals across the SDLC.

‍That includes AI-assisted development activity, vulnerability findings, and evolving developer behavior tied to how software is actually being built.

In this post, we’re focusing specifically on Adaptive Learning with AI Signals powered by Trust Agent: AI — using AI-assisted development detections to help organizations dynamically align secure coding guidance to developers actively using AI coding tools in day-to-day development work.

Learning That Reflects Real AI Usage

Secure coding learning has always been most effective when it reflects the way developers actually work. Organizations already align learning by role, coding language, technology stack, and vulnerability focus areas to make training more relevant across engineering teams.

AI-assisted development introduces an additional layer of context.

An engineer experimenting with AI-generated Python code today may spend next month reviewing infrastructure-as-code in Terraform or using AI to accelerate frontend testing workflows. Some developers rely heavily on AI coding assistants. Others barely touch them.

Adaptive Learning helps organizations turn AI visibility into targeted secure coding guidance. When Trust Agent: AI identifies AI-assisted development activity, organizations can automatically assign learning aligned to those workflows.

That means developers actively using AI coding assistants can receive targeted learning tied to the work already happening inside their environment — without security teams manually identifying individual developers and repeatedly reassigning learning as AI usage expands across engineering teams.

Adaptive Learning in Practice

Adaptive Learning powered by Trust Agent: AI is designed to fit naturally into existing software development workflows.

Security teams can create targeted learning aligned to secure AI-assisted development practices, then use Trust Agent: AI detections to dynamically assign that learning to developers actively using AI coding tools in their day-to-day workflows. As developers begin interacting with AI-assisted development environments, relevant secure coding guidance is automatically assigned based on that activity.

So now we can detect the AI usage, we can understand where it's happening, and we can trigger that targeted training for the right developers. And that's what I'm gonna show you today, how we take that AI signal and turn it into adaptive learning. So there are a few parts of the process which I'll walk through today. The first piece is in the policy setup on TA, then we can go to the Quest side to trigger the training. For the policy, it's super simple and straightforward. So if you want to ensure that developers who use AI need a training, you can head over to this policy section within configuration. And what you wanna do is ensure that the adaptive AI learning is toggled on. Once that's set up, that's all you need to do on the Trust Agent AI side. In this demo itself, we've already gone ahead and set up the installer and all that piece over here. We have various internal external documentation on how to locally install it or use various scripts like Jamf, there'll be other documentation on that. But in this, like I say, in this demo, I'm just gonna head on with the actual policy setup and the adaptive learning creation. Okay. So now this is set up. I'm over into quests, and what I'm gonna do is create a quest to target the specific learners who have used AI. So So once you're in the quest, if you scroll down and you look at the participant section, what we have now is a new participant type, which is trust agent detection. And what this will do is automatically pull the developers who have been flagged in that TA AI report. So what we're gonna do is select this, and we can start to fill out the rest of the quest like normal. So we give it a title, a badge, we'll scroll down to the objectives. And what's really nice about this is the flexibility that the admins have to define the criteria for the training needed. For example, they can go in and they can select the specific content. So they could just go with Coding With AI. They could go with the AI agents and the protocols as well. For this demo, I'm gonna select both of these and keep them as part of the criteria. I can also then decide the schedule, so I'm gonna use fixed dates, and then I can go ahead and configure some of the quest settings as well. One important area to note is the count pass completions. This again is flexible based on customer's needs where we can define if they want learners to complete the training regardless if they've done any of the content before, or they can set a specific time period like three or six months where they have done it recently. So for this demo, I'm gonna set it to redo all content. Once that's filled out, you can then go ahead and save and publish. Now that's set up. That's gonna be available for them developers that were flagged for the Trust Agent AI, and the two steps are complete from an admin's perspective. So we've gone in and we've set up the policy and made sure that's enabled. And we've gone over to Quest and ensure we've created a Quest that's using the new participation type, the trust agent detection. So what I can do now is quickly log in as a learner just so we can see if they have received that training. So just to quickly show you what it's like from the learner perspective, here I am logged in as a learner. I was one of the learners who flagged up based on AI usage, and therefore, I have now received the quest, which you can see here on the screen. And in the normal process, I would then go through and then go through the topics within the quest. Once I have completed this, then I've completed the quest. And that concludes the demo today. So what I've gone through is really just an overview of the importance of Trust Agent AI and adaptive learning, and then gone through to the demo of the admin experience and then quickly briefly through to the learning experience as well. As always, any questions on adaptive learning, please feel free to reach out. Always happy to help. Thank you very much.

The walkthrough above demonstrates how Adaptive Learning with AI Signals works in practice, including configuring Trust Agent Detection, creating adaptive Quests, dynamically assigning learning, and tracking participation and completion.

For step-by-step setup instructions and configuration details, explore the adaptive Learning with Trust Agent: AI knowledge base article.

Secure Coding Guidance Should Reflect How Developers Actually Work

AI-assisted development is already part of everyday engineering workflows. Developers are moving quickly between AI-generated suggestions, manually written code, automated testing, and pull request review throughout the day.

As those workflows continue evolving, secure coding guidance needs to stay connected to the way software is actually being built.

Adaptive Learning powered by Trust Agent: AI helps organizations do exactly that — aligning learning to real AI-assisted development activity so guidance reaches developers when it is most relevant and actionable.

The result is secure coding guidance that stays relevant as AI tooling evolves — without adding overhead to the security teams responsible for running the program.

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Secure coding learning aligned to real AI usage, developer workflows, and modern software development practices.

AI-assisted development is already becoming part of everyday engineering work.

A developer may manually write core application logic in the morning, use AI to generate test coverage in the afternoon, then review AI-assisted pull requests before the end of the day. The workflows are fluid. The tooling changes quickly. Usage patterns evolve week to week, sometimes team to team.

Historically, secure coding learning operated on relatively fixed cycles — onboarding pathways, annual assignments, broad role-based training, or periodic awareness campaigns. Those models made sense when development tooling evolved more gradually and learning requirements stayed relatively stable over time.

AI-assisted development moves differently. Security teams now need a more responsive way to keep secure coding guidance connected to how developers are actually working in the moment.

Adaptive Learning is designed to help organizations align secure coding learning to real software development activity and software risk signals across the SDLC.

‍That includes AI-assisted development activity, vulnerability findings, and evolving developer behavior tied to how software is actually being built.

In this post, we’re focusing specifically on Adaptive Learning with AI Signals powered by Trust Agent: AI — using AI-assisted development detections to help organizations dynamically align secure coding guidance to developers actively using AI coding tools in day-to-day development work.

Learning That Reflects Real AI Usage

Secure coding learning has always been most effective when it reflects the way developers actually work. Organizations already align learning by role, coding language, technology stack, and vulnerability focus areas to make training more relevant across engineering teams.

AI-assisted development introduces an additional layer of context.

An engineer experimenting with AI-generated Python code today may spend next month reviewing infrastructure-as-code in Terraform or using AI to accelerate frontend testing workflows. Some developers rely heavily on AI coding assistants. Others barely touch them.

Adaptive Learning helps organizations turn AI visibility into targeted secure coding guidance. When Trust Agent: AI identifies AI-assisted development activity, organizations can automatically assign learning aligned to those workflows.

That means developers actively using AI coding assistants can receive targeted learning tied to the work already happening inside their environment — without security teams manually identifying individual developers and repeatedly reassigning learning as AI usage expands across engineering teams.

Adaptive Learning in Practice

Adaptive Learning powered by Trust Agent: AI is designed to fit naturally into existing software development workflows.

Security teams can create targeted learning aligned to secure AI-assisted development practices, then use Trust Agent: AI detections to dynamically assign that learning to developers actively using AI coding tools in their day-to-day workflows. As developers begin interacting with AI-assisted development environments, relevant secure coding guidance is automatically assigned based on that activity.

So now we can detect the AI usage, we can understand where it's happening, and we can trigger that targeted training for the right developers. And that's what I'm gonna show you today, how we take that AI signal and turn it into adaptive learning. So there are a few parts of the process which I'll walk through today. The first piece is in the policy setup on TA, then we can go to the Quest side to trigger the training. For the policy, it's super simple and straightforward. So if you want to ensure that developers who use AI need a training, you can head over to this policy section within configuration. And what you wanna do is ensure that the adaptive AI learning is toggled on. Once that's set up, that's all you need to do on the Trust Agent AI side. In this demo itself, we've already gone ahead and set up the installer and all that piece over here. We have various internal external documentation on how to locally install it or use various scripts like Jamf, there'll be other documentation on that. But in this, like I say, in this demo, I'm just gonna head on with the actual policy setup and the adaptive learning creation. Okay. So now this is set up. I'm over into quests, and what I'm gonna do is create a quest to target the specific learners who have used AI. So So once you're in the quest, if you scroll down and you look at the participant section, what we have now is a new participant type, which is trust agent detection. And what this will do is automatically pull the developers who have been flagged in that TA AI report. So what we're gonna do is select this, and we can start to fill out the rest of the quest like normal. So we give it a title, a badge, we'll scroll down to the objectives. And what's really nice about this is the flexibility that the admins have to define the criteria for the training needed. For example, they can go in and they can select the specific content. So they could just go with Coding With AI. They could go with the AI agents and the protocols as well. For this demo, I'm gonna select both of these and keep them as part of the criteria. I can also then decide the schedule, so I'm gonna use fixed dates, and then I can go ahead and configure some of the quest settings as well. One important area to note is the count pass completions. This again is flexible based on customer's needs where we can define if they want learners to complete the training regardless if they've done any of the content before, or they can set a specific time period like three or six months where they have done it recently. So for this demo, I'm gonna set it to redo all content. Once that's filled out, you can then go ahead and save and publish. Now that's set up. That's gonna be available for them developers that were flagged for the Trust Agent AI, and the two steps are complete from an admin's perspective. So we've gone in and we've set up the policy and made sure that's enabled. And we've gone over to Quest and ensure we've created a Quest that's using the new participation type, the trust agent detection. So what I can do now is quickly log in as a learner just so we can see if they have received that training. So just to quickly show you what it's like from the learner perspective, here I am logged in as a learner. I was one of the learners who flagged up based on AI usage, and therefore, I have now received the quest, which you can see here on the screen. And in the normal process, I would then go through and then go through the topics within the quest. Once I have completed this, then I've completed the quest. And that concludes the demo today. So what I've gone through is really just an overview of the importance of Trust Agent AI and adaptive learning, and then gone through to the demo of the admin experience and then quickly briefly through to the learning experience as well. As always, any questions on adaptive learning, please feel free to reach out. Always happy to help. Thank you very much.

The walkthrough above demonstrates how Adaptive Learning with AI Signals works in practice, including configuring Trust Agent Detection, creating adaptive Quests, dynamically assigning learning, and tracking participation and completion.

For step-by-step setup instructions and configuration details, explore the adaptive Learning with Trust Agent: AI knowledge base article.

Secure Coding Guidance Should Reflect How Developers Actually Work

AI-assisted development is already part of everyday engineering workflows. Developers are moving quickly between AI-generated suggestions, manually written code, automated testing, and pull request review throughout the day.

As those workflows continue evolving, secure coding guidance needs to stay connected to the way software is actually being built.

Adaptive Learning powered by Trust Agent: AI helps organizations do exactly that — aligning learning to real AI-assisted development activity so guidance reaches developers when it is most relevant and actionable.

The result is secure coding guidance that stays relevant as AI tooling evolves — without adding overhead to the security teams responsible for running the program.

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Secure Code Warriorは、ソフトウェア開発ライフサイクル全体にわたってコードを保護し、サイバーセキュリティを最優先とする文化を築くお手伝いをします。アプリケーションセキュリティマネージャ、開発者、CISO、またはセキュリティ関係者のいずれであっても、安全でないコードに関連するリスクを軽減するお手伝いをします。

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Shannon Holtは、アプリケーションセキュリティ、クラウドセキュリティサービス、PCI-DSSやHITRUSTなどのコンプライアンス標準のバックグラウンドを持つサイバーセキュリティ製品マーケターです。

Shannon Holtは、アプリケーションセキュリティ、クラウドセキュリティサービス、PCI-DSSやHITRUSTなどのコンプライアンス標準のバックグラウンドを持つサイバーセキュリティ製品マーケターです。彼女は、セキュリティに対する期待と現代のソフトウェア開発の現実との間のギャップを埋めることで、安全な開発とコンプライアンスを技術チームにとってより実用的で親しみやすいものにすることに情熱を注いでいます。

Secure coding learning aligned to real AI usage, developer workflows, and modern software development practices.

AI-assisted development is already becoming part of everyday engineering work.

A developer may manually write core application logic in the morning, use AI to generate test coverage in the afternoon, then review AI-assisted pull requests before the end of the day. The workflows are fluid. The tooling changes quickly. Usage patterns evolve week to week, sometimes team to team.

Historically, secure coding learning operated on relatively fixed cycles — onboarding pathways, annual assignments, broad role-based training, or periodic awareness campaigns. Those models made sense when development tooling evolved more gradually and learning requirements stayed relatively stable over time.

AI-assisted development moves differently. Security teams now need a more responsive way to keep secure coding guidance connected to how developers are actually working in the moment.

Adaptive Learning is designed to help organizations align secure coding learning to real software development activity and software risk signals across the SDLC.

‍That includes AI-assisted development activity, vulnerability findings, and evolving developer behavior tied to how software is actually being built.

In this post, we’re focusing specifically on Adaptive Learning with AI Signals powered by Trust Agent: AI — using AI-assisted development detections to help organizations dynamically align secure coding guidance to developers actively using AI coding tools in day-to-day development work.

Learning That Reflects Real AI Usage

Secure coding learning has always been most effective when it reflects the way developers actually work. Organizations already align learning by role, coding language, technology stack, and vulnerability focus areas to make training more relevant across engineering teams.

AI-assisted development introduces an additional layer of context.

An engineer experimenting with AI-generated Python code today may spend next month reviewing infrastructure-as-code in Terraform or using AI to accelerate frontend testing workflows. Some developers rely heavily on AI coding assistants. Others barely touch them.

Adaptive Learning helps organizations turn AI visibility into targeted secure coding guidance. When Trust Agent: AI identifies AI-assisted development activity, organizations can automatically assign learning aligned to those workflows.

That means developers actively using AI coding assistants can receive targeted learning tied to the work already happening inside their environment — without security teams manually identifying individual developers and repeatedly reassigning learning as AI usage expands across engineering teams.

Adaptive Learning in Practice

Adaptive Learning powered by Trust Agent: AI is designed to fit naturally into existing software development workflows.

Security teams can create targeted learning aligned to secure AI-assisted development practices, then use Trust Agent: AI detections to dynamically assign that learning to developers actively using AI coding tools in their day-to-day workflows. As developers begin interacting with AI-assisted development environments, relevant secure coding guidance is automatically assigned based on that activity.

So now we can detect the AI usage, we can understand where it's happening, and we can trigger that targeted training for the right developers. And that's what I'm gonna show you today, how we take that AI signal and turn it into adaptive learning. So there are a few parts of the process which I'll walk through today. The first piece is in the policy setup on TA, then we can go to the Quest side to trigger the training. For the policy, it's super simple and straightforward. So if you want to ensure that developers who use AI need a training, you can head over to this policy section within configuration. And what you wanna do is ensure that the adaptive AI learning is toggled on. Once that's set up, that's all you need to do on the Trust Agent AI side. In this demo itself, we've already gone ahead and set up the installer and all that piece over here. We have various internal external documentation on how to locally install it or use various scripts like Jamf, there'll be other documentation on that. But in this, like I say, in this demo, I'm just gonna head on with the actual policy setup and the adaptive learning creation. Okay. So now this is set up. I'm over into quests, and what I'm gonna do is create a quest to target the specific learners who have used AI. So So once you're in the quest, if you scroll down and you look at the participant section, what we have now is a new participant type, which is trust agent detection. And what this will do is automatically pull the developers who have been flagged in that TA AI report. So what we're gonna do is select this, and we can start to fill out the rest of the quest like normal. So we give it a title, a badge, we'll scroll down to the objectives. And what's really nice about this is the flexibility that the admins have to define the criteria for the training needed. For example, they can go in and they can select the specific content. So they could just go with Coding With AI. They could go with the AI agents and the protocols as well. For this demo, I'm gonna select both of these and keep them as part of the criteria. I can also then decide the schedule, so I'm gonna use fixed dates, and then I can go ahead and configure some of the quest settings as well. One important area to note is the count pass completions. This again is flexible based on customer's needs where we can define if they want learners to complete the training regardless if they've done any of the content before, or they can set a specific time period like three or six months where they have done it recently. So for this demo, I'm gonna set it to redo all content. Once that's filled out, you can then go ahead and save and publish. Now that's set up. That's gonna be available for them developers that were flagged for the Trust Agent AI, and the two steps are complete from an admin's perspective. So we've gone in and we've set up the policy and made sure that's enabled. And we've gone over to Quest and ensure we've created a Quest that's using the new participation type, the trust agent detection. So what I can do now is quickly log in as a learner just so we can see if they have received that training. So just to quickly show you what it's like from the learner perspective, here I am logged in as a learner. I was one of the learners who flagged up based on AI usage, and therefore, I have now received the quest, which you can see here on the screen. And in the normal process, I would then go through and then go through the topics within the quest. Once I have completed this, then I've completed the quest. And that concludes the demo today. So what I've gone through is really just an overview of the importance of Trust Agent AI and adaptive learning, and then gone through to the demo of the admin experience and then quickly briefly through to the learning experience as well. As always, any questions on adaptive learning, please feel free to reach out. Always happy to help. Thank you very much.

The walkthrough above demonstrates how Adaptive Learning with AI Signals works in practice, including configuring Trust Agent Detection, creating adaptive Quests, dynamically assigning learning, and tracking participation and completion.

For step-by-step setup instructions and configuration details, explore the adaptive Learning with Trust Agent: AI knowledge base article.

Secure Coding Guidance Should Reflect How Developers Actually Work

AI-assisted development is already part of everyday engineering workflows. Developers are moving quickly between AI-generated suggestions, manually written code, automated testing, and pull request review throughout the day.

As those workflows continue evolving, secure coding guidance needs to stay connected to the way software is actually being built.

Adaptive Learning powered by Trust Agent: AI helps organizations do exactly that — aligning learning to real AI-assisted development activity so guidance reaches developers when it is most relevant and actionable.

The result is secure coding guidance that stays relevant as AI tooling evolves — without adding overhead to the security teams responsible for running the program.

Secure Code Warriorは、ソフトウェア開発ライフサイクル全体にわたってコードを保護し、サイバーセキュリティを最優先とする文化を築くお手伝いをします。アプリケーションセキュリティマネージャ、開発者、CISO、またはセキュリティ関係者のいずれであっても、安全でないコードに関連するリスクを軽減するお手伝いをします。

デモを予約 [ダウンロード]

リソースハブ

始めるためのリソース

その他の投稿

SCW Learning Content for KnowBe4

Secure Code Warrior content available through KnowBe4 helps technical teams build secure coding and AI governance awareness through structured learning covering OWASP Top 10 risks, AI-assisted development, and modern secure coding practices.

さらに詳しく

May 15, 2026

Secure Code Warrior × KnowBe4 partnership

Guides

One Pager

Secure AI-driven development with KnowBe4 + Secure Code Warrior

Secure Code Warrior joins KnowBe4 to bring hands-on secure coding training into security awareness programs — covering OWASP, AI development, and 10 languages.

May 13, 2026

One Pager

Secure Code Warrior Learning: Enable Secure AI-Driven Development at Scale

Secure code for the AI era: Learn how Secure Code Warrior builds developer capability to reduce vulnerabilities and secure AI-generated code at scale.

Apr 27, 2026

One Pager

Trust Agent:AI - Secure and scale AI-Drive development

AI is writing code. Who’s governing it? With up to 50% of AI-generated code containing security weaknesses, managing AI risk is critical. Discover how SCW's Trust Agent: AI provides the real-time visibility, proactive governance, and targeted upskilling needed to scale AI-driven development securely.

Apr 1, 2026

リソースハブ

始めるためのリソース

その他の投稿

Announcing Adaptive Learning: The Antidote to AI Software Security Risk and Skill Gaps

Adaptive Learning bridges SCW Trust Agent with our entire learning platform, ensuring training stays perfectly aligned with real-time developer activity.

さらに詳しく

Jun 1, 2026

Blog

Train developers on the real risks in their code, whether human-written or AI-generated

Adaptive Learning auto-assigns targeted secure coding training to the developers introducing real vulnerabilities, reducing recurring risks at the source.Secure Code Warrior blog banner with a blue overlay over a developer working at a multi-monitor desk displaying code, alongside the headline 'Train developers on the real risks in their code.'l

Jun 1, 2026

Blog

Securing the Future of Software: Why Secure Code Warrior and KnowBe4 Are Joining Forces

I am thrilled to announce today an upcoming strategic partnership between Secure Code Warrior and KnowBe4. KnowBe4 is a world-renowned leader in comprehensively managing human and agentic AI risk, making them the perfect partner to help us distribute foundational security awareness to organizations across the globe.

May 6, 2026

Blog

Post-Quantum Cryptography: Quantum Computers Will Break Today’s Encryption – Are You Ready?

Post-quantum cryptography (PQC) is critical for protecting data from quantum computing threats. Learn how “harvest now, decrypt later” exposes risk and how developers can prepare for quantum-safe security.

Apr 29, 2026

Secure coding learning that reflects real AI usage

Secure coding learning aligned to real AI usage, developer workflows, and modern software development practices.

Learning That Reflects Real AI Usage

Adaptive Learning in Practice

Secure Coding Guidance Should Reflect How Developers Actually Work

Secure coding learning aligned to real AI usage, developer workflows, and modern software development practices.

Learning That Reflects Real AI Usage

Adaptive Learning in Practice

Secure Coding Guidance Should Reflect How Developers Actually Work

レポートをダウンロードするには、以下のフォームに記入してください

Secure coding learning aligned to real AI usage, developer workflows, and modern software development practices.

Learning That Reflects Real AI Usage

Adaptive Learning in Practice

Secure Coding Guidance Should Reflect How Developers Actually Work

Secure coding learning aligned to real AI usage, developer workflows, and modern software development practices.

Learning That Reflects Real AI Usage

Adaptive Learning in Practice

Secure Coding Guidance Should Reflect How Developers Actually Work

目次

始めるためのリソース

SCW Learning Content for KnowBe4

Secure AI-driven development with KnowBe4 + Secure Code Warrior

Secure Code Warrior Learning: Enable Secure AI-Driven Development at Scale

Trust Agent:AI - Secure and scale AI-Drive development

始めるためのリソース

Announcing Adaptive Learning: The Antidote to AI Software Security Risk and Skill Gaps

Train developers on the real risks in their code, whether human-written or AI-generated

Securing the Future of Software: Why Secure Code Warrior and KnowBe4 Are Joining Forces

Post-Quantum Cryptography: Quantum Computers Will Break Today’s Encryption – Are You Ready?