Secure AI-Powered Development: Why Speed Without Security Is a Business Risk

Coding with AI assistance has fundamentally transformed software development. Applications that once took weeks to build can now be generated in hours. When ChatGPT first emerged in late 2022, it mostly just helped developers understand and refactor code, but the actual hyped up benefits seemed few and far between. But now, less than two years later, AI coding agents have made developers significantly more productive, with some developers reporting 10x productivity improvements on specific tasks. Developers who aren’t using these tools have quickly become the minority.

According to Stack Overflow’s 2024 developer survey, 76% of developers are using or plan to use AI tools this year, with 62% already working with them daily. GitHub’s research demonstrates that developers using AI assistants can complete programming tasks dramatically faster than those without.

Now, nearly anyone can build applications with thousands of lines of code in minutes with simple chat prompts and clicks. But here’s the critical business question: how do we balance moving fast while maintaining the security standards our customers and regulators demand?

The Security Reality of AI-Generated Code

AI assistants are remarkably proficient at generating functional code quickly. According to Qodo.ai’s 2025 State of AI Code Quality report, 78% of developers report productivity gains (with 17% claiming a 10× increase).

But there’s a catch. Research shows that AI tools are increasing the amount of insecure code needing to be fixed. This means that when developers can generate code 10x faster, they can also introduce vulnerabilities 10x faster. 

Although things have moved extremely quickly in 2025 in this space, Forrester’s 2024 predictions warned that this speed-security gap could lead to significant breaches if teams don’t adapt their practices. Specifically, Forrester predicted that “at least three data breaches will be publicly blamed on insecure AI-generated code” in 2024.

The core business challenges include:

• Operational risk. AI suggests code that works, but ignores security best practices, creating vulnerabilities that may only surface in production.
• Supply chain risk. Dependency recommendations may include vulnerable or malicious packages, which can expand your attack surface without proper vetting.
• Knowledge risk. Common anti-patterns get replicated across codebases at scale, institutionalizing vulnerabilities rather than best practices.
• Compliance risk. Traditional security reviews can’t keep pace with the rapid development of AI, potentially creating gaps in regulatory compliance documentation and controls.

Four Strategic Approaches to Secure AI Development

So, how do you bridge this gap? The solution isn’t to slow down development or ban AI code assistants—it’s to build security directly into your AI-accelerated workflows. Here are four strategic approaches that forward-thinking organizations are implementing:

Strategy 1: Configure AI Tools for Security-First Development

The easiest wins come from configuring your AI assistant to think about security by default. Most organizations deploy AI coding tools with default settings, which is a critical missed opportunity, and arguably, the easiest way to get basic security coverage.

Most platforms can set up universal rules or additional context that can guide the AI toward secure patterns.

This means establishing security rules that guide AI recommendations toward compliant, secure patterns. For highly regulated industries like healthcare or financial services, this includes sector-specific requirements like HIPAA compliance or PCI-DSS standards built into the AI’s decision-making process. 

However, it’s important to note that AI configuration alone won’t achieve full compliance. You’ll still need proper processes, human oversight, and regular audits as an essential piece of the AI-enhanced SDLC.

The business impact is significant: instead of discovering compliance issues during audits or after incidents, security requirements become automatic, reducing both risk and remediation costs. By implementing rules, you make specific requirements automatic, rather than relying on developers to remember them during code reviews.

Strategy 2: Build Security Testing Into Your AI Workflow

Developers already outnumber security teams, and AI makes that challenge exponentially worse. The solution is to implement automated security testing that runs as fast as your AI generates code. Configuration helps, but it’s not enough.

Traditional security reviews often occur too late. By the time a manual review or penetration test happens, AI-generated code has already been deployed or integrated into larger features. Static application security testing (SAST) also struggles to keep up. It can’t identify vulnerabilities that only emerge in running applications—like broken authorization, business logic flaws, or data exposure issues—and it frequently overwhelms teams with false positives. As AI accelerates development speed, the flood of noisy SAST alerts only increases, making it harder to focus on real problems.

This is why dynamic application security testing (DAST) changes the equation. Instead of application security testing being a separate, later step, DAST becomes part of your development flow. DAST complements SAST by evaluating how applications behave in real-world conditions, catching issues that static scans miss and filtering out unnecessary noise.

The key business benefit is that security issues are caught and fixed during development, not discovered in production, where they’re exponentially more expensive to address.

Strategy 3: Monitor Production Applications in Real-Time

Implementing monitoring systems that can detect and respond to security issues at AI speed is also critical for any vulnerabilities that make their way through to production. AI enables you to ship code faster, but that also means potential security issues reach production more quickly. This is where monitoring comes into play, ensuring that anything that does make it through and is being exploited is caught before it can do much damage.

The math is simple: if AI helps you deploy 10x more code, you need monitoring that can catch issues 10x faster. You can’t wait for the next scheduled security review, if it even exists, to discover that your AI-generated authentication flow has a bypass vulnerability. Most applications require multiple layers of monitoring coverage.

Organizations need monitoring systems that can detect and respond to security issues at the same pace that AI can introduce them. This involves monitoring not just traditional security metrics, but also patterns specific to AI-generated code: unusual API usage, authentication anomalies, and configuration changes that might affect security posture. While these attack surfaces aren’t unique to AI-generated code, the AI context increases the rate at which they can emerge in production.

Building incident response for AI-generated code requires accounting for the unique characteristics of AI development. Much of the time, this learning can then be filtered back into your pre-production testing and potentially into your AI configuration. When security incidents occur with AI-generated code, the response should investigate whether the issue lies in AI configuration or training, and review similar patterns across the application to prevent recurrence.

Strategy 4: Ensure Developers are Trained in Secure Coding Techniques

Not all solutions come neatly wrapped in config files, tools, or AI-powered features. To elevate security in the AI era, organizations must still invest in developer training. AI may handle more of the coding, but that doesn’t mean humans can forget about security. If anything, the opposite is true.

First, developers and reviewers still need a solid grounding in secure coding practices. Security awareness enables them to write better prompts, recognize when AI suggestions miss critical protections, and make informed decisions about whether to accept or reject generated code. Speed without security knowledge only magnifies risk.

Second, training must extend beyond just writing code. Developers should learn how to configure AI platforms with security in mind, whether that means enabling compliance-focused rules, applying sector-specific requirements, or staying current with evolving security best practices built into these tools.

Finally, teams need to understand the common flaws introduced by AI-generated code and how to spot them quickly during reviews. Many of these vulnerabilities aren’t new—authorization gaps, weak input validation, or dependency risks—but AI can reproduce them at scale. Training reviewers to recognize these patterns helps dispel the false assumption that AI-generated code is inherently secure just because it runs.

The goal isn’t to make every developer a security expert. Instead, it’s to ensure they have enough knowledge to guide AI tools toward secure outputs, identify obvious risks, and keep skills current through ongoing updates on new threats, incidents, and AI code security best practices.

The Future of Secure AI Development

The strategies we’ve covered—smart AI configuration, automated security testing, real-time monitoring, and ongoing developer education—work best together. Configure your AI tools for security by default, but back that up with automated testing. Monitor your applications in production, but ensure that your teams understand how to identify issues that automated systems might miss.

Organizations face a strategic choice: adapt security practices to match AI development speed or accept increasing risk as the gap widens. The companies that master secure AI development will build better software faster while maintaining customer trust and regulatory compliance. Those that don’t will find themselves managing an increasing number of security incidents and potential breaches.

The time to act is now. The future belongs to teams that master the balance between AI speed and security rigor—and that starts with testing. If you’re building with AI-powered workflows, StackHawk makes it simple to test applications for vulnerabilities as quickly as you generate them. You can sign up for a free trial account, or if you’re using AI coding assistants like Cursor or Claude Code, try our $5/month single-user plan, Vibe, to find and fix vulnerabilities 100% in natural language.