Secure Code insights

Your AI Coding Assistant might be your fastest and most productive teammate, but it can also be your biggest security risk. 

In our latest whitepaper, our co-founders Pieter Danhieux and Dr. Matias Madou, Ph.D., explore the double-edged sword that is AI Coding Assistants and how they can be a welcome addition and a significant security liability at the same time. 

AI Coding Assistants – The New Normal? 

There is no escaping the rise of LLM coding assistants. From GitHub, Copilot, to the newly minted Deepseek, they’re pretty much everywhere, with new ones popping up almost every week. They became an extra team member, producing code in record time, even paving the way for ‘vibe coding’ where one without qualified programming skills can create an entire application in seconds by just giving the right prompts. AI coding assistants became so invaluable that by mid-2023, 92% of developers surveyed by GitHub reported using AI tools on the job or even outside of work. 

But there’s a hitch, AI-generated code can’t be blindly trusted and left entirely on its own.

Despite their rapid adoption, organizations must remain vigilant about the risks. While these tools accelerate delivery, they can also introduce significant flaws if left unchecked. The convenience they offer often comes with hidden risks, that’s where we took a deeper look into how these assistants perform in our latest whitepaper.

‍When Speed And Risk Go Hand In Hand 

AI Coding Assistant tools are trained on billions of lines of open source code, which often contain unsafe patterns. When copied, those weaknesses don’t just land in your codebase, they spread, creating a ripple effect of vulnerabilities in the wider SDLC. 

Alarmingly, a recent Snyk survey found that 80% of developers admit that they don’t apply AI Code security policies, while 76% of respondents believed that AI-generated code was more secure than code written by humans. These are numbers we can’t quite ignore. 

Setting AI guidelines is a good start, but without measured and verified security skills, they won’t stop insecure code from slipping into production. Traditional guard rails just can’t keep pace with the sheer volume of code that AI can produce. 

Arming Your Devs For The Future

The only scalable solution? Equip developers with the ability to spot and fix vulnerabilities before code ever goes live and always remain one step ahead of AI. 

A strong AI-era developer risk management program should have the following:

• Benchmark security skills: Establish a baseline, track progress and identify skill gaps. 
• Verify AI Trustworthiness: Vet the backend LLM and the tool itself. 
• Validate every commit: Embed security checks into the workflow.
• Maintain continuous observability: Monitor all repositories for insecure patterns and automatically enforce policies. 
• Offer role and language-specific learning: Focus training on the exact frameworks, platforms, and tools your team uses. 
• Stay agile: Update training as new tech and business needs evolve. 

Security and AI, Moving Forward 

AI Coding assistants are here to stay, and their productivity benefits are too big to ignore. But if security isn’t built into the process from day one, organisations risk trading short-term speed and convenience for long-term (and more complicated) vulnerability and security issues.

The future of software security isn’t just about choosing between AI and human developers. It’s about combining their strengths, with security-first thinking as the glue. That means every developer, whether they’re writing code or prompting the AI, needs the skills to know what to look for and ensure code safety. 

AI-driven productivity boosts productivity, but don’t forget, secure code safeguards the future.

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Download the full whitepaper today!

As cyber threats become increasingly sophisticated, businesses of every size must proactively assess and address their cybersecurity risks to protect sensitive data, maintain customer trust, and comply with legal requirements. A comprehensive understanding of potential vulnerabilities enables organizations to take targeted actions and allocate resources effectively, minimizing the chances of a damaging incident.

Assessing cybersecurity risks is a complex task, given the variety of threats your company may face, from malware, to insecure coding practices, to data breaches. By conducting regular cybersecurity risk assessments, your business can stay ahead of evolving threats and ensure it has the necessary measures in place to safeguard its assets. Let’s take a look at how your company can conduct an effective risk assessment to identify weaknesses and build a resilient security framework.

What is a cybersecurity risk assessment?

A cybersecurity risk assessment is the process of identifying, evaluating, and prioritizing risks to your organization's information technology systems. The goal of a cybersecurity risk assessment is to detect current vulnerabilities and predict future threats, like injection attacks or cross-site scripting (XSS), so your company can understand the potential impact of those vulnerabilities and how to best mitigate them. Many of these issues can be addressed before they cause serious damage by implementing secure coding practices from the start of the software development lifecycle (SDLC).

The cybersecurity landscape is always changing, with new threats emerging and existing ones evolving. That’s why organizations should regularly conduct risk assessments, rather than treating them as a one-and-done exercise. 

Using a structured framework, such as the NIST (National Institute of Standards and Technology) or ISO/IEC 27001 standards, can improve and streamline cybersecurity risk assessments by providing a well-defined, proven approach. These frameworks offer guidelines on best practices for risk identification, evaluation, and mitigation. While your organization may use these frameworks as a baseline, in many cases, it’s best to develop a customized methodology tailored to your specific needs. This ensures that the assessment addresses risks specific to your industry or operational environment.

The importance and benefits of cybersecurity risk assessments

Cyberattacks pose significant organizational risks in terms of both financial loss and reputational damage. For instance, a ransomware attack can cost companies millions in downtime and recovery costs, while a data breach may lead to lost customer trust and regulatory fines. The longer a vulnerability remains unaddressed, the higher the likelihood of an attack and the greater the associated costs.

Conducting regular cybersecurity risk assessments also allows your company to gain a deep, accurate understanding of its security vulnerabilities on an ongoing basis. Your business can then make more informed decisions on how to invest its limited cybersecurity resources by prioritizing vulnerabilities based on the severity and likelihood of an attack.

How to perform a cybersecurity risk assessment in 7 steps

Performing a cybersecurity risk assessment involves identifying, analyzing, and addressing the risks your organization faces. While there are steps that apply to most risk assessments, it’s important to tailor the process to your company’s specific needs, size, industry, and security requirements. Here is a breakdown of the critical steps for your company to follow.

1. Define objectives and scope

First, you need to clearly define the objectives and scope of the risk assessment. This means understanding what the assessment aims to achieve and the areas of your business it will cover. This step is critical because it sets the foundation for the entire assessment. A well-defined scope prevents the assessment from becoming overwhelming, ensuring that time and resources are spent evaluating what matters most.

Involving team members from relevant departments during this phase helps ensure that no essential area is overlooked. Engage key stakeholders across departments like IT, legal, operations, and compliance to outline the areas of greatest concern, and collaboratively set clear goals for the assessment. Then establish a timeline and budget for the project to avoid scope creep and maintain focus. And ensure that you don’t neglect training on secure coding practices to address the risk of vulnerabilities introduced early on in the development process.

2. Prioritize IT assets

After defining the scope, it’s time to identify and prioritize your organization’s IT assets. By determining which assets are most critical to your business operations, you can allocate resources more effectively during the risk mitigation process. This is especially important for smaller organizations that may not have the capability to address every potential risk in a timely fashion. Prioritizing IT assets ensures that you focus on the areas that, if compromised, would have the most significant impact on your organization’s functionality or reputation.

Begin by collaborating with IT and business units to identify and categorize assets based on their importance to the organization. Map out key systems, databases, intellectual property, and any other resources that are essential to your organization’s operation. This should include determining the confidentiality, integrity, and availability needs of each asset. Once the assets are prioritized, you can begin evaluating the risks and vulnerabilities associated with them, ensuring that high-value assets are given the attention they deserve.

3. Identify threats and vulnerabilities

The next step is to identify potential threats and vulnerabilities that could impact your prioritized IT assets. Threats refer to any external or internal factors that could exploit weaknesses in your system, like cybercriminals, malware, or natural disasters. Vulnerabilities are weaknesses in your system that could be exploited by these threats, such as insufficient encryption, misconfigured systems, or insecure software development practices.

This is where your company begins to understand the specific risks it faces and the gaps in its current security posture. For example, if your organization uses outdated software or weak password policies, these could become points of entry for cybercriminals. A thorough understanding of your system’s vulnerabilities and attack surface gives you a roadmap for implementing fixes. It also sets the stage for the next steps in the risk assessment process, which involves assessing the impact and likelihood of these threats.

As part of this process, conduct a vulnerability scan using tools that can detect weaknesses in your network and systems. Supplement this technical analysis with a review of past incidents, common attack vectors for your industry, and emerging cyber threats. Engage key IT and security staff in identifying areas of concern and updating the list of known vulnerabilities. You should also carefully assess any vulnerabilities in your software development practices, as part of creating a Secure Software Development Life Cycle (SSDLC) framework.

4. Determine risk levels and prioritize risks

Once threats and vulnerabilities are identified, your organization should determine the risk levels associated with each. This step involves evaluating both the likelihood of a threat actor exploiting a vulnerability and the potential impact on your organization. Risk levels can be categorized as low, medium, or high, based on factors such as the severity of the impact, the ease with which an attacker can exploit the vulnerability, and the asset's exposure.

Determining risk levels allows your organization to understand which threats pose the greatest danger to its operations and reputation. A vulnerability in a public-facing website might be classified as high risk because an attack could compromise sensitive customer data and lead to significant damage to your brand. On the other hand, an internal risk, such as a misconfigured server with limited access, might be categorized as having a lower risk.

Your company can use a risk matrix to calculate the probability and potential impact of each identified risk. Engage your cybersecurity team to define the factors that should influence risk scoring, such as the importance of the asset, ease of exploitation, and the business impact of a successful attack. You should also involve senior management in assessing risk levels to ensure that the organization’s business objectives are aligned with its security priorities.

Once you’ve determined risk levels, prioritize those risks based on their severity and impact. Not all risks can or need to be mitigated immediately, and some may require long-term solutions or strategic planning. By addressing high-priority risks first, your business can reduce its exposure to catastrophic events, such as data breaches or system outages.

5. Address risks with security measures

The next step is to implement appropriate security measures to mitigate the risks you’ve prioritized. The goal is to reduce the likelihood of a security breach occurring and minimize the potential damage if an attack does happen. Start by applying high-priority security measures for your most critical assets. Involve your IT and security teams to determine the most suitable solutions and integrate them into your company’s overall cybersecurity strategy.

Each security solution should be tailored to the specific threat or vulnerability it addresses. This could involve applying technical solutions such as firewalls, intrusion detection systems, encryption, and multi-factor authentication (MFA), as well as new policies and developer training in areas like secure coding.

6. Implement secure coding practices

Developer risk management plays a pivotal role in addressing cybersecurity risks. Developers must be trained to recognize and mitigate security threats at each stage of the SDLC, from requirements gathering to testing and deployment. Integrating security early in the SDLC helps in identifying vulnerabilities before they become critical issues in production. Secure coding also better equips your developers to better assess AI-generated code for potential security flaws before it goes into production, so they can maintain both efficiency and safety.

Organizations must train developers in secure coding practices and implement continuous integration/continuous delivery (CI/CD) pipelines that automatically detect and address vulnerabilities during development. These secure coding practices, like input validation, secure data storage, and secure session management, can greatly reduce the risk of common development-related vulnerabilities, including SQL injection, cross-site scripting (XSS), and buffer overflows.

7. Conduct continuous monitoring and document risks

Cybersecurity risk management should be a continuous process to keep your business resilient against evolving threats. Implement practices like conducting risk assessments on a set cadence, setting up real-time monitoring tools, conducting regular vulnerability scans, and reviewing access logs to detect any abnormal activities. Review and update your risk assessment process regularly, with input from relevant stakeholders across your organization. 

Finally, to ensure future assessments are built on what’s come before, always document the risks you identify and the measures taken to mitigate them. An easily-accessible single source of truth that tracks each risk, its status, and related actions taken can serve as an invaluable aid to your ongoing cybersecurity efforts.

Carry out and act on cybersecurity risk assessments

Cybersecurity risks, if left unaddressed, can lead to devastating consequences, including costly data breaches, regulatory penalties, legal fees, and lasting damage to your company’s reputation. To ensure your business is adequately protected, you should conduct and act on cybersecurity risk assessments. One of the best ways to achieve this is by incorporating secure coding practices throughout the SDLC to drastically reduce vulnerabilities.

Secure Code Warrior’s learning platform equips your developers with the skills and knowledge needed to address security flaws before software reaches production. Upskilled development teams that take advantage of Secure Code Warrior’s platform can reduce vulnerabilities by 53%, leading to cost savings of up to $14 million. With 2x to 3x gains in risk reduction, it’s no surprise that developers keep coming back for more learning, with 92% of them eager for additional training.

If you’re ready to reduce your cybersecurity risk profile and ensure your software is built securely from the ground up, schedule a demo of Secure Code Warrior’s platform today.

October has long been the month when organizations double down on cybersecurity awareness. But in 2025, the stakes are very different. AI-assisted software development is no longer a pilot project – it’s mainstream. What began as autocomplete copilots has accelerated into agentic AI systems generating entire applications. Developers are no longer just authors of code; they are reviewers, editors, and defenders of AI-generated output.

This shift is both an opportunity and a risk. AI promises faster delivery, but it also magnifies exposure, compliance pressure, and the need for resilient developer security practices. Cybersecurity Awareness Month can’t keep relying on the same old phishing reminders and scam-spotting exercises. It must evolve into AI-aware readiness that equips enterprises to secure code at the speed of AI.

Why Cybersecurity Awareness Month Must Evolve

For two decades, Cybersecurity Awareness Month has followed a familiar script: spotlighting phishing and scam emails, urging stronger password hygiene, promoting multi-factor authentication, reminding employees to update software, and warning about social engineering. Some years bring emphasis on safe browsing, ransomware, data privacy, or securing remote work. These are all important, but they represent the same old playbook – a checklist of repeated themes that no longer reflect the risks enterprises actually face in an AI-driven development world.

To stay relevant, Cybersecurity Awareness Month needs to go beyond generic awareness and instead emphasize developer-first practices – from secure coding exercises to AI-powered code review – that reflect the realities of today’s software development lifecycle.

Some mature organizations have already begun moving in this direction, incorporating activities like OWASP Top 10 training or developer-focused video modules into their awareness efforts. Still, even these approaches need to evolve. The reality is that AI is fundamentally changing how code is created, reviewed, and deployed. Awareness must shift from surface-level reminders to preparing enterprises for a future where AI-generated code is the new attack surface.

At Secure Code Warrior, we believe that developers equipped with the right AI security skills can be 10x more productive for the organization – but without those skills, they can generate 10x more code, tech debt, and vulnerabilities.

The implications for enterprise security leaders are clear:

• More code, more exposure: AI accelerates delivery, but without oversight it can exponentially increase vulnerabilities and technical debt, raising enterprise risk.
• Governance, not just compliance: Governments and industry bodies are signaling that today’s standards will not be enough. For example, the EU’s AI Act already introduces requirements for transparency, risk classification, and oversight in AI systems, setting a precedent other regions may follow. CISOs should anticipate future regulation and proactively address shadow AI with visibility and governance controls. 
• Skills gaps widen: Developers must be equipped to handle AI-specific attack surfaces such as prompt injection and insecure APIs. In fact, the 2025 Thales Data Threat Report found that nearly 70% of enterprises rank the pace of AI development as their top security concern, underscoring the growing mismatch between adoption and readiness.

CISOs can no longer treat AI adoption as a side initiative. It is now central to how enterprises build, deliver, and secure software.

Secure Code Warrior’s Mission: From Awareness to Action

At Secure Code Warrior, we believe awareness is only the starting point. Enterprises need measurable action to manage developer risk at scale. Our platform equips CISOs to embed secure coding into organizational DNA, delivering the visibility and outcomes required to reduce risk and strengthen resilience.

• 10,000+ secure coding lessons covering 600+ vulnerabilities across 70+ languages and frameworks.
• AI Challenges that simulate real AI workflows, giving developers realistic practice in reviewing and securing AI-generated code.
• SCW Trust Score^® benchmarks that provide CISOs with objective visibility into developer risk posture and measurable progress.
• SCW Trust Agent: AI, which extends observability and governance to AI-assisted development, bringing shadow AI into the light, connecting AI-generated code to real risk metrics, and ensuring responsible adoption. 

These are not just training modules; they are powerful learning and governance tools that reduce organizational risk, demonstrate resilience to boards and regulators, and deliver proven outcomes – with customers achieving up to 53% fewer vulnerabilities and up to 3X faster MTTR.

Cybermon 2025: The Age of AI

To make Cybersecurity Awareness Month tangible, Secure Code Warrior runs Cybermon, our annual October event that turns global awareness into enterprise action.

This year’s theme, The Age of AI, reflects the seismic shifts underway in software development. Cybermon 2025 begins on October 6, enabling organizations to:

• Pressure-test secure code review skills in AI-driven environments.
• Launch measurable, gamified campaigns that demonstrate progress to boards and regulators.
• Foster a culture of secure development aligned with enterprise risk management priorities.

Cybermon helps CISOs operationalize awareness, transforming it from a slogan into measurable, organization-wide impact.

A Strategic Call to Action for CISOs

AI-driven development is no longer on the horizon, it is here. The speed of code delivery is increasing, the risks are multiplying, and the responsibility to secure AI-generated code now sits squarely with enterprise leaders.

The mandate is clear:

• Close the AI security skills gap.
• Measure and reduce developer risk at scale.
• Proactively address shadow AI with visibility and governance controls, anticipating the likelihood of future regulation. 

At Secure Code Warrior, we are committed to helping CISOs navigate this new era with the expertise, visibility, and governance required to secure software at enterprise scale. With capabilities like SCW Trust Score^® and Trust Agent: AI, organizations can turn awareness into measurable enterprise-wide resilience. Learn more about SCW Trust Agent: AI - Visibility and Governance for Your AI-Assisted SDLC.

This October, let us move beyond surface-level awareness and embrace the Age of AI with clarity, confidence, and resilience – one secure line of code at a time.

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The widespread adoption of AI coding tools is transforming software development. With 78% of developers¹ now using AI to increase productivity, the speed of innovation has never been greater. But this rapid acceleration comes with a critical risk.

Studies reveal that as much as 50% of functionally correct, AI-generated code is insecure². This isn’t a small bug; it’s a systemic challenge. It means every time a developer uses a tool like GitHub Copilot or ChatGPT, they could be unknowingly introducing new vulnerabilities into your codebase. The result is a dangerous mix of speed and security risks that most organizations are not equipped to manage.

The Challenge of “Shadow AI”

Without a way to manage AI coding tool usage, CISO, AppSec, and engineering leaders are exposed to new risks they can't see or measure. How can you answer crucial questions like:

• What percentage of our code is AI-generated?
• What MCPs are being used?
• What unapproved models are being used?
• What vulnerabilities are being generated by different models?

The lack of visibility and governance creates a new layer of risk and uncertainty. It’s the very definition of “shadow IT,” but for your codebase.‍‍

Our Solution – Trust Agent: AI

We believe you can have both speed and security. We're proud to launch Trust Agent: AI, a powerful new capability of our Trust Agent product that provides the deep observability and control you need to confidently embrace AI in your software development lifecycle.  Using a unique combination of signals, Trust Agent: AI provides:

• Visibility: See which developers are using which AI coding tools, LLMs and MCPs, and on what codebases. No more "shadow AI."
• Risk Metrics: Connect AI-generated code to a developer's skill level and introduced vulnerabilities to understand the true risk being introduced, at the commit level.
• Governance: Automate policy enforcement to ensure AI-enabled developers meet secure coding standards.

The Trust Agent: AI dashboard provides insights into AI coding tool usage, contributing developer, code repository, and more.‍

The Developer's Role: The Last Line of Defense

While Trust Agent: AI gives you unprecedented governance, we know that the developer remains the last, and most critical, line of defense. The most effective way to manage AI-generated code risk is to ensure your developers have the security skills to review, validate, and secure that code. This is where SCW Learning, part of our comprehensive Developer Risk Management platform, comes in. With SCW Learning, we equip your developers with the hands-on skills they need to safely leverage AI for productivity. The SCW Learning product includes:

• SCW Trust Score: An industry-first benchmark that quantifies developer security proficiency, enabling you to identify which developers are best equipped to handle AI-generated code.
• AI Challenges: Interactive, real-world coding challenges that specifically teach developers how to find and fix vulnerabilities in AI-generated code.
• Targeted Learning: Curated learning paths from 200+ AI Challenges, Guidelines, Walkthroughs, Missions, Quests, and Courses that reinforce secure coding principles and help developers master the security skills needed to mitigate AI risks.

By combining the powerful governance of Trust Agent: AI with the skill development of our learning product, you can create a truly secure SDLC. You’ll be able to identify risk, enforce policy, and empower your developers to build code faster and more securely than ever before.

Ready to Secure Your AI Journey?

The era of AI is here, and as a product manager, my goal is to build solutions that don't just solve today's problems but anticipate tomorrow's. Trust Agent: AI is designed to do just that, giving you the visibility and control to manage AI risks while empowering your teams to innovate. The early access beta is now live, and we'd love for you to be a part of it. This isn't just about a new product; it's about pioneering a new standard for secure software development in an AI-first world.

Join the Early Access Waitlist today!

AI-assisted coding isn’t coming – it’s already here. Development teams everywhere are adopting tools like GitHub Copilot to accelerate delivery, and this shift is happening fast. Gartner predicts that by 2028, 90% of enterprise software engineers will regularly use AI coding assistants, up from just 14% in early 2024.

While AI tools make writing code faster, they also change the way developers work. As AI takes on more of the authoring, developers are spending more time reviewing, analyzing, and fixing code generated by machines. Yet many developers lack the specific security skills needed to perform these reviews effectively. This growing knowledge gap introduces new risks, and underscores the urgent need to build security proficiency as a core part of modern code review. 

A Hands-On Way to Build Secure Review Skills

That’s why we’ve redesigned our AI Challenges with a brand-new Copilot-style experience: a hands-on way for developers to practice and strengthen those secure review skills in realistic workflows. These interactive challenges mirror the real-world workflows developers are already using in their IDEs with AI tooling, helping them practice secure review skills in context.

Instead of only reviewing pull requests, learners now interact with a simulated AI coding assistant named Obi that generates code for them to review, analyze, and fix – just like coding in the real world.
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Here’s what makes the new AI Challenges different:

• A streamlined, intuitive UI – Obi, the simulated AI coding assistant, guides the experience, generating realistic code snippets that challenge developers to think critically about security.
• Structured, step-by-step learning – Move through Locate → Impact → Fix to build repeatable skills.
• Built-in learning feedback – No hidden answers. Learners always leave with the full context and clear takeaways.
• Focused challenges – Less noise, more learning. Only the code and information you need to solve each activity.

AI-assisted coding is transforming software development, and secure code review is quickly becoming one of the most critical skills for developers. The faster code is generated, the faster vulnerabilities can slip into production – unless teams are equipped to spot, understand, and fix security issues early.

Our Copilot-style AI Challenges, guided by Obi, are designed to make those skills second nature. It’s a modern, interactive, and effective way to prepare developers for the future of secure coding.

Try the new Copilot-Style AI Challenges today or Learn More here: How to play an AI Challenge.

Your Guide to the AI/LLM Security Intro Series

AI coding tools like GitHub Copilot, Cursor, and others are reshaping how software is built — but they also introduce new security challenges that developers must understand to build safe, reliable applications. To help teams adopt AI securely, we’ve created a free, 12-week AI/LLM Security Intro Video Series on YouTube.

This post serves as your central hub for the series. Each week, we’ll update it with a new video and description, covering essential concepts like prompt injection, data and model poisoning, supply chain risks, secure prompting, and more. Bookmark this page to follow along weekly, or subscribe to our YouTube channel to get every lesson as soon as it’s released.

If you want to dive deeper beyond these introductory lessons, explore the full AI/LLM collection in the SCW platform or request a demo if you’re not yet a customer. Subscribe to our YouTube channel to catch every new episode as it’s released. And if you’d like to stay connected with the latest content, updates, and resources, opt-in here to join our community of developers and security leaders.

Episodes (Updated Weekly)

Week 1 — AI Coding Risks: Dangers of Using LLMs
In this video, we explore the potential dangers of using AI/LLMs when writing code and highlight key risks developers face when integrating AI-powered tools into their workflows.

Week 2 — AI Coding Benefits: Secure AI-Assisted Development
‍AI coding tools aren’t just risky — when used securely, they can help developers work faster and smarter. In this video, we explore the advantages of using AI/LLMs when writing code, highlighting how teams can harness AI responsibly while avoiding common security pitfalls.

Week 3 — Prompt Injection Explained: Protecting AI-Generated Code
‍Prompt injection is one of the most common AI/LLM vulnerabilities — and one every developer should know how to prevent. In this video, we introduce dire

Week 4 — Sensitive Info Disclosure: Avoiding AI Data Leaks
AI-powered tools can inadvertently leak sensitive information, putting your applications and data at risk. In this video, we cover sensitive information disclosure vulnerabilities, explain how they arise when using AI/LLMs, and share practical steps developers can take to reduce exposure.

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Week 5 — AI Supply Chain Risks: Securing Dependencies
‍AI-assisted development accelerates coding — but it also introduces supply chain risks that can impact every layer of your applications. In this video, we explore vulnerabilities related to AI/LLMs, explain how third-party models and APIs can expand your attack surface, and share strategies for minimizing exposure.

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Week 6 — Data Poisoning: Securing AI Models and Outputs
‍AI systems are only as secure as their training data — and compromised inputs can create vulnerabilities that ripple across your applications. In this video, we introduce data and model poisoning attacks, explain how malicious inputs can manipulate AI outputs, and share strategies to safeguard your systems.

Week 7 — Improper Output Handling: Validating AI-Generated Code
‍AI-powered tools can generate code fast — but if outputs aren’t validated, vulnerabilities can creep in unnoticed. In this video, we examine improper output handling in AI-assisted development, explain how risky outputs can compromise your applications, and share techniques for safeguarding generated code.

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Week 8 — Excessive Agency: Controlling AI Autonomy Risks
‍As AI systems become more autonomous, excessive agency creates new risks where models act beyond their intended scope. In this video, we explore excessive agency vulnerabilities in AI-assisted development, explain how overstepping behaviors arise, and discuss techniques for maintaining control over AI-driven processes.

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Week 9 — System Prompt Leakage: Hidden AI Security Risks
‍System prompts often include hidden instructions that guide AI behavior — but if these are exposed, attackers can manipulate models or extract sensitive information. In this video, we cover system prompt leakage vulnerabilities, explain how they occur, and discuss steps developers can take to safeguard their AI-powered workflows.

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Week 10 — Vector Weaknesses: Securing AI Retrieval Workflows
‍AI models often rely on vector databases and embeddings to deliver powerful capabilities — but misconfigurations and insecure implementations can expose sensitive data and create new attack vectors. In this video, we dive into vector and embedding weaknesses, explain common security challenges, and share strategies to secure your AI-powered search and retrieval workflows.

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Week 11 — AI Misinformation: Avoiding Hallucination Risks
‍AI tools can sometimes generate outputs that look correct but are wrong — creating misinformation risks that affect security, reliability, and decision-making. In this video, we explain misinformation vulnerabilities in AI-assisted coding, explore how incorrect outputs emerge, and share strategies for validating and securing AI-driven content.

Week 12 — Unbounded Consumption: Preventing AI DoS Risks
AI systems can consume resources without limits — leading to risks like denial-of-service (DoS), data exposure, and unexpected operational failures. In this video, we cover unbounded consumption vulnerabilities in AI/LLMs, explain how they happen, and share practical strategies to monitor, govern, and secure AI resource usage.

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These videos are designed to introduce the core concepts of AI/LLM security, but there’s so much more to explore within the Secure Code Warrior platform. Dive into AI Challenges that simulate real-world AI-assisted code review and remediation, explore our AI/LLM Guidelines aligned to industry best practices, and work through Walkthroughs, Missions, Quests, and Course Templates that provide hands-on experience building secure coding habits. For teams ready to advance their skills, the platform also offers a growing library of over 130 AI/LLM-focused learning activities, including topics like Coding with AI, Intro to AI Risk & Security, and the OWASP Top 10 for LLM Applications.  Request a demo to learn more. 

A free, 12-week video series to help developers code securely with AI

AI coding assistants and large language models (LLMs) are transforming how software gets built. They promise speed, flexibility, and innovation — but they also introduce new security risks that developers can’t afford to ignore.

That’s why Secure Code Warrior is launching a free, 12-week AI/LLM Security Intro Video Series on YouTube. Each short episode focuses on introducing a key AI/LLM security risk or concept developers need to know to safely embrace AI-assisted development and avoid introducing vulnerabilities into their applications.

Watch the first episode now: AI Coding Risks: Dangers of Using LLMs

Don’t miss an episode. Subscribe on YouTube. 

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Join our community of developers and security leaders — opt-in here to get the latest videos, resources, and updates delivered straight to your inbox. 

Why we created this series

AI-assisted coding is rewriting the rules of software development. Tools like GitHub Copilot, Cursor, and others enable developers to ship code faster than ever — but without a strong understanding of AI security, teams risk introducing hidden vulnerabilities and inconsistent behaviors that traditional testing may not catch.

Secure development in the age of AI requires awareness first. This series is designed to cut through the noise and give developers an introductory foundation in AI/LLM security concepts so they can confidently innovate without compromising safety.

What to expect

Across 12 weeks, we’ll explore both opportunities and risks in AI-assisted development, including:

• The benefits and dangers of using AI and LLMs in coding
• Prompt injection and how malicious inputs manipulate AI outputs
• Sensitive information disclosure and protecting secrets in AI-powered workflows
• Supply chain risks when relying on third-party models and APIs
• System prompt leakage, vector weaknesses, and retrieval vulnerabilities
• Emerging challenges like misinformation, excessive agency, and unbounded consumption
• And much more!

Want one place to binge or catch up? Bookmark our Video Hub — we’ll update it with the latest episodes and summaries.

Go to the Hub: AI/LLM Security Video Series: All Episodes, Updated Weekly.

How to follow along

• Subscribe on YouTube to get every episode as it’s released
• Join our community of developers and security leaders — opt-in here to get the latest videos, resources, and updates delivered straight to your inbox.
• Bookmark the Video Hub Blog for quick access to every episode
• If you want to dive deeper beyond these introductory lessons, explore the full AI/LLM collection in the Secure Code Warrior platform or request a demo if you’re not yet a customer.

Setting the standard for secure AI-assisted development

AI is rapidly changing the way we build software. But innovation without security isn’t sustainable. Developers need practical, developer-first guidance to understand AI-assisted risks and implement secure coding habits that scale.

This free video series is part of Secure Code Warrior’s commitment to helping the developer community thrive in this new era. From our publicly available AI Security Rules on GitHub to our expanding AI/LLM learning collection, we’re equipping teams with the tools and knowledge they need to innovate securely.

When software vulnerabilities are treated as an afterthought or roadblock to innovation, organizations open the door to data breaches, reputational damage, and costly legal liabilities. Cyberattacks often exploit weaknesses in code that might have been prevented with stronger development practices.

Secure coding addresses these challenges by embedding security principles into every stage of development. Instead of implementing fixes after vulnerabilities are discovered, developers write code with built-in protections against common threats such as injection attacks and cross-site scripting (XSS). Let’s take a closer look at how secure coding can help your company reduce risk, safeguard user trust, and comply with regulatory requirements, all while delivering reliable, high-quality software.

What is secure coding?

Secure coding is the principle of following security best practices when writing software to address potential vulnerabilities. Rather than treating security as a separate phase of development, secure coding integrates proven safeguards from the earliest stages on, ensuring developers take ownership of code security and have the skills to apply it effectively.

Recognized secure coding standards produced by organizations like the Open Worldwide Application Security Project (OWASP), or the Software Engineering Institute’s CERT Division, can act as a North Star for developers looking to avoid common pitfalls that attackers exploit. Continuously building upon foundational, hands-on secure coding skills to safely implement these strategies into existing workflows is a non-negotiable in today's cybersecurity landscape. For example, validating as many user inputs as possible can prevent SQL injection attacks, while output encoding helps block XSS. These and other secure coding practices reduce the risk of breaches and lead to more resilient applications that can stand up to evolving cyber threats.

Why is secure coding so important?

Secure coding matters because many successful cyberattacks exploit vulnerabilities that could have been prevented during development. By prioritizing secure practices from the start, you reduce the likelihood of introducing flaws that attackers can use to compromise data or disrupt operations. Building security into every stage of the software development life cycle (SDLC) ensures that every feature, update, and integration is designed with protection in mind.

Proactively addressing risks during development is far less expensive than fixing them after deployment, where remediation can require emergency patches, downtime, and incident response resources. It improves compliance with data protection regulations as well, sidestepping potential fines and legal challenges. Secure coding practices also foster consumer trust in your company and make strong security part of your brand reputation.

Common code security vulnerabilities

Secure coding aims to prevent the most frequent and dangerous vulnerabilities that attackers exploit, in addition to emerging threat vectors, like those that come with the use of AI coding tools. Here is an overview of several common vulnerabilities, the damage they can cause, and how secure coding can help mitigate them.

Deserialization flaws

Deserialization flaws occur when an application accepts and processes data from external sources without proper validation. Serialization converts objects into a format that can be stored or transmitted, while deserialization reconstructs those objects for use. The impact of a deserialization flaw can be severe, resulting in the execution of arbitrary code or escalation of privileges. Secure coding addresses this by ensuring that only trusted, validated data is deserialized and by avoiding native deserialization of untrusted inputs entirely when possible.

Injection attacks

Injection attacks occur when an attacker supplies input that is interpreted as part of a command or query by the application. The most well-known type is an SQL injection, where malicious SQL statements are inserted into queries to access or alter database contents. Other types include command injections, where attackers execute arbitrary commands, and Lightweight Directory Access Protocol (LDAP) injections.The consequences of injection attacks are broad, ranging from unauthorized data access and deletion to full system compromise. Databases containing sensitive personal, financial, or proprietary information are prime targets. Secure coding helps prevent injection vulnerabilities by using parameterized queries or prepared statements, escaping untrusted data before it’s processed, and enforcing strict input validation. These and other secure coding practices can stop attackers from altering the intended behavior of the application.

Cross-site scripting (XSS)

Cross-site scripting (XSS) is a form of injection attack that targets web applications by inserting malicious scripts into pages viewed by other users. This typically happens when an application includes unvalidated user input in its output. When another user’s browser renders the page, the malicious script runs, potentially stealing cookies, capturing keystrokes, or redirecting them to malicious sites.

The impact of XSS can include session hijacking and identity theft. For businesses, this erodes customer trust and can lead to regulatory consequences if sensitive data is compromised. Secure coding addresses XSS by sanitizing and encoding all user-supplied input before it’s displayed, using frameworks that automatically escape output, and implementing a Content Security Policy (CSP) to limit what scripts can run.

Access control

Access control vulnerabilities occur when rules about what users can see or do are not properly defined or enforced. Broken access control lets attackers bypass intended user role restrictions, potentially reading sensitive data, modifying records, or performing actions intended only for privileged users.

Access control issues pose a significant challenge, and AI coding tools, in particular, have struggled to address this vulnerability class effectively, highlighting the need for developer skill and awareness. The impact of broken access control is significant. For example, if an attacker can access admin-only features, they could disable security settings, extract private information, or impersonate other users.

Secure coding practices counter these risks by enforcing server-side authorization checks for every request, following the principle of least privilege, and avoiding sole reliance on obscurity (such as hiding links) as a security measure. Additionally, conducting rigorous access control testing helps ensure that these protections remain strong over the long term.

Cross-site request forgery (CSRF)

Cross-site request forgery (CSRF) attacks force a user to perform an unwanted action on another site where they are authenticated. This might be transferring funds, changing an email address, or modifying account settings. The attack works because the browser automatically includes valid authentication tokens, like cookies, with the forged request.

Secure coding defends against CSRF by implementing anti-CSRF tokens that are unique to each user session and validating them with each state-changing request. Additional defenses include requiring re-authentication for critical actions and setting the SameSite attribute on cookies to prevent them from being sent with cross-site requests. By embedding these protections into the development lifecycle, you can make it much more likely that your system processes only legitimate, intentional actions.

Insecure authentication

Insecure authentication occurs when the process of verifying a user’s identity is weak, predictable, or otherwise flawed. This can result from poor password policies, insecure credential storage, or a lack of multi-factor authentication (MFA). Attackers can exploit these weaknesses through a variety of methods, including brute force attacks, credential stuffing, or intercepting unencrypted credentials in transit. The impact of insecure authentication is severe because it can give attackers direct access to user accounts, administrative controls, and sensitive data. Once inside, they can further compromise systems or make off with valuable information.

Secure coding addresses this vulnerability by enforcing strong password requirements, hashing and salting stored credentials, using secure protocols like HTTPS for all authentication exchanges, and integrating MFA to provide an additional layer of verification. Developers should also design login mechanisms to limit failed attempts and detect suspicious activity early, so authentication systems act as a strong line of defense rather than a weak point.

6 secure coding practices to follow

Building secure software involves more than knowing what threats exist. It requires learning and incorporating  proven secure coding practices and patterns. The techniques below offer actionable steps developers can take to make security an integral part of every project.

1. Implement user access control

As noted above, user access control means defining and enforcing permissions for each user role in your system. Strong access control prevents unauthorized users from viewing sensitive data, modifying records, or performing administrative actions. It also limits the damage if a user account is compromised, since an attacker will only have the permissions of that account. 

Effective user access control requires robust authentication to verify identity, followed by authorization checks to confirm that the authenticated user has permission to perform the requested action. You should regularly review your access control practices to align with the principle of least privilege, giving users the minimum access needed to do their jobs. Access control also relies on regular monitoring to keep policies and users in the system up to date, along with audits that quickly flag any unusual activity.

2. Validate and sanitize data

Validating and sanitizing data involves checking all incoming inputs to ensure they meet expected formats, types, and patterns before processing, then cleaning the data to remove potentially dangerous content. These practices should apply to incoming data from any external source, as even trusted sources can be compromised, so every input should be treated as untrusted until verified. By building validation and sanitization into the development process, you keep your application resilient against common threats like injection attacks.

3. Write in a modern language

Secure coding isn’t just about how you write code. It’s also about choosing tools and environments that make it easier to avoid introducing security flaws in the first place. While moving entirely to a modern language is often not a realistic or efficient option for many enterprises, making at least some use of a modern programming language — and using the latest version of all your chosen languages — can improve software security. Modern languages and frameworks typically offer better memory safety, stronger type checking, and built-in protection against common vulnerabilities. For example, languages like Rust and Go are designed with safety in mind, helping to prevent issues like buffer overflows that older languages may be more prone to.

Established languages like Java or Python can be difficult to modernize and secure, but keeping up with the most recent releases ensures you have access to the latest security features and performance improvements. Many updates patch known vulnerabilities, deprecate unsafe functions, and provide more secure default settings. 

4. Practice code obfuscation

Code obfuscation is the process of making your source code or compiled code more difficult for attackers to understand, reverse-engineer, or manipulate. While it doesn’t replace other security measures, it adds another layer of defense by hiding the application’s logic and sensitive routines from prying eyes. Obfuscation can involve techniques like renaming variables and functions to meaningless identifiers or restructuring code in ways that make it harder to follow.

The goal is to raise the cost and effort required for an attacker to find and exploit vulnerabilities. In secure coding, obfuscation works alongside other strong security practices to make your application a less attractive target.

5. Scan and monitor your code

Secure coding practices also involve actively scanning and monitoring your code. Static application security testing (SAST) tools analyze your source code for known vulnerabilities before deployment, while dynamic application security testing (DAST) tools test running applications for exploitable flaws in real time. Combining both approaches helps you catch issues early and continuously.

Beyond scanning during development, it’s critical to implement ongoing monitoring in production. This involves setting up alerts for unusual activity, logging security events, and utilizing runtime application self-protection (RASP) tools to detect and block attacks in real-time. Regular scanning and monitoring ensure that even if vulnerabilities slip through during development, you have the visibility to address them rapidly before significant damage can be done.

6. Document and implement secure coding standards

Documenting secure coding standards consists of creating a clear set of guidelines that define how your team writes safe, maintainable, and compliant code. These standards should cover topics like input validation, error handling, encryption practices, and session management, as well as how to address common vulnerabilities specific to your tech stack.

Having these standards in place ensures that all developers, from junior engineers to senior architects, follow the same security principles. When paired with training and regular updates, these standards become a living resource that keeps your development process aligned with the latest security requirements.

Secure coding standards and frameworks

If you’re looking for help in crafting your own coding standards, the popular guidelines below can help. They cover a range of practices that address common vulnerabilities and can help clarify how to align your coding efforts with industry best practices.

OWASP Secure Coding Practices

OWASP is one of the most widely recognized sources for developers who want to embed security into their code from start to finish. It produces key secure coding resources like the OWASP Developer Guide and the OWASP Top 10. OWASP’s approach is highly actionable, offering checklists and coding tips that developers can apply during development.

Following OWASP guidelines benefits teams by creating a common baseline for secure coding across projects. Because OWASP is regularly updated to reflect new threat vectors and attack techniques, organizations can use it to stay ahead of emerging risks. By building OWASP’s principles into your workflow, you improve code quality, reduce vulnerabilities, and align with widely accepted industry guidelines.

NIST Secure Software Development Framework

The National Institute of Standards and Technology (NIST) publishes comprehensive secure coding guidance alongside broader cybersecurity frameworks. In addition to providing information on high-level secure software development practices, the NIST Secure Software Development Framework (SSDF) provides a common vocabulary that improves communication on key issues among and between teams at your company. It focuses on outcomes rather than specific techniques, so it's best as a complement to other standards like OWASP or the SEI CERT Coding Standards.

SEI CERT Coding Standards

Developed by the CERT Division at the Software Engineering Institute (SEI), the SEI CERT Coding Standards focus on preventing security vulnerabilities in specific programming languages, including C, C++, Java, and Perl. Each language-specific standard features secure coding rules, detailed explanations, and examples of both compliant and noncompliant code. As the CERT coding standards address the nuances and quirks of specific programming languages, they’re highly valuable and actionable for developers working in those environments.

Microsoft Security Development Lifecycle

Microsoft’s Security Development Lifecycle (SDL) is a set of practices designed to make security an integral part of the software development process. It includes recommendations on 10 important topics, including threat modeling, security training for developers, and securing the software supply chain. Microsoft uses this approach itself, so organizations benefit from a tested process that aligns developers, testers, and security teams.

ISO/IEC 27001

ISO/IEC 27001 is best known as a standard for information security management systems (ISMS), but it also has strong implications for secure coding. While it focuses on establishing an organization-wide ISMS, it includes principles on secure coding. These recommendations provide high level guidance organizations can follow when working to implement secure coding practices.

AI Security Rules

AI coding tools are more handy than ever, but they can do more harm than good if using them doesn’t result in safe, accurate code. Secure Code Warrior’s AI Security Rules — the first of their kind — provide guidance on secure coding best practices to use with AI tools like GitHub Copilot, Cline, Cursor, and Windsurf. These rules keep your AI coding assistants on track, putting guardrails in place that minimize the risk of insecure code.

Learn how to create secure code from the start

Secure coding is more than just a technical requirement — it’s a critical business advantage. When your team writes secure code from the start, you prevent costly vulnerabilities, reduce the risk of data breaches, and deliver software your customers can trust. Mastering secure coding practices, however, can be particularly challenging without structured guidance. Developers need real-world practice, up-to-date knowledge of evolving threats, and a way to apply security principles confidently in every line of code.

Secure Code Warrior’s ISO 27001 certified and SOC 2 compliant agile learning platform gives your team exactly that. With training on language-specific security best practices, realistic coding challenges, and content curated for a variety of roles, it turns security from an afterthought into a natural part of the development process. Developers build the skills to identify and fix vulnerabilities early, align with industry standards, and take full ownership of code security throughout the software development lifecycle. It's no surprise that companies that use Secure Code Warrior cut software vulnerabilities by 53%, realize cost savings of up to $14 million, and see 92% of their developers want additional training.

If you want to see how your team can write safer, stronger code from day one, book a Secure Code Warrior demo today.

At Secure Code Warrior, we’re on a mission to change the way the world builds software, one secure line of code at a time. Join us in celebrating a major milestone:

10,000+ Secure Code Learning Activities — and Counting!

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‍These 10,000+ secure code learning activities represent more than just a number, they reflect real impact across thousands of global teams embedding security into their everyday workflows. We are proud to have helped so many companies improve code quality, accelerate time to market, and reduce vulnerabilities. And we’re just getting started. We’re continuing to expand our learning pathways to help developers reduce vulnerabilities and confidently navigate AI-assisted development. Congratulations and thank you to all who have helped us reach this major milestone! 

Here’s to the next 10,000. Thanks for being part of the journey.

TL;DR - Rewind the Tape: A Decade of Developer Empowerment 

2015–2018: Building the Foundation

• Launched Secure Code Warrior with a developer-first mission and introduced interactive secure coding challenges in real languages and frameworks.
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2019–2020: Expanding the Core

• Surpassed 3,500+ secure code learning activities across 30+ languages.
• Introduced Courses, Missions, and hands-on learning pathways.
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2021–2022: Smarter, Broader Learning

• Rolled out Walkthroughs, certification levels, and SCORM LMS integration.
• Focused on accessibility, inclusivity, and scalable program delivery.

2023–2024: Real-World Relevance

• Launched hands-on Coding Labs in C#, Java, Python, React, Terraform, and more.
• Added deep-dive content on cloud, DevOps, container, and database risks.
• Introduced AI/LLM-focused security topics and labs for broader engineering roles.
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2025: The Age of AI and Developer Risk Management

• Hit 10,000+ secure code learning activities across 60+ languages.
• Expanded AI/LLM learning content for OWASP Top 10 for LLMs, Coding with AI, Non-Human Identities (NHIs), and more.

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