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AI Is Already Writing Code
AI code assistants are no longer experimental. They’re embedded directly into developer workflows, often without the security team’s knowledge or approval. Developers rely on coding agents to write code, fix bugs, and accelerate delivery. Beyond developers, infrastructure and security teams are also writing code and using coding agents, but with even less focus on secure code or security tools.
The use of AI coding agents is growing faster than organizations can track, leaving security teams with limited visibility into how code is generated, what data is shared with AI systems, and whether it introduces risk.
The code is visible; how it is generated isn’t. That’s where control breaks down.
The New Risk Surface
AI-assisted code development introduces new security challenges and risks:
Vulnerable Code Introduced at Scale
AI code assistants generate code that looks right but isn’t necessarily secure. They often introduce vulnerabilities and repeat outdated practices, and these risks increase as more AI-generated code is produced.
Prompt Attacks on IDEs & Coding Agents
AI coding agents can read files, execute commands, and act autonomously within development environments. That makes them vulnerable to prompt injection – malicious instructions embedded in code, documentation, dependencies, connectors, and MCP servers – which can manipulate the agent into performing unintended actions. This isn’t theoretical. OWASP ranks prompt injection as the top risk for LLM applications.
Sensitive Data Exposure Through Prompts
AI tools are embedded in daily workflows, often involving more data sharing than intended, such as API keys, passwords, internal system details, and other sensitive data. What seems like a simple prompt can expose this data. Most users don’t think about it, but security teams have to.
Vulnerable Dependencies Introduced Via AI-Generated Code
AI code assistants frequently suggest open-source packages to complete the code. These suggestions aren’t validated in real time and may include vulnerable libraries or even non-existent packages that attackers can exploit through supply chain attacks.
Manipulated AI-Generated Code
AI-generated code can be manipulated by malicious prompts. Developers working under time pressure tend to trust these outputs and move on, making manipulated logic hard to catch, as both automated scans and human PR reviews often miss it.
Why Existing Security Tools Fall Short
Security teams rely on tools like SAST and SCA to inspect code after it’s written. These tools can identify known vulnerabilities, but they lack visibility into how the code is created. They can flag a vulnerable dependency, but not whether sensitive data was shared with an AI tool or whether the code was influenced by malicious prompts. They also don’t capture the prompts used to generate the code.
As AI-assisted development grows, code volume increases significantly. Security reviews can’t keep up with this volume, leaving risks unaddressed. Existing tools were built for developer-written code, not for AI-generated code at scale.
Compliance Risks in AI-Generated Code
AI-generated code introduces new compliance risks. Regulations like the EU AI Act, GDPR, and HIPAA don’t regulate code directly; they govern how systems behave, how data is handled, and what risks are controlled. These requirements apply regardless of how the code is created.
Frameworks like NIST AI RMF and the OWASP Top 10 for LLMs define these risks clearly, including prompt injection, data leakage, and lack of traceability. But organizations have no practical way to implement these recommendations. If you can’t trace how the code was created – what data was used and what controls were applied – you can’t prove it’s compliant.
What Control Actually Looks Like
Control in AI-assisted development doesn’t mean restricting teams. It means having visibility and enforcement as code is being generated.
Control requires the following capabilities:
- Identifying which AI tools, connectors, and MCP servers are in use
- Monitoring prompts and AI-generated code in real time
- Preventing sensitive data from being shared with AI
- Enforcing policies before code is committed
This is a shift from post-development security to in-process control, addressing risks as code is created, not after it’s written.
Enable Fast Development Without Introducing Risk
AI is now part of the software development lifecycle. That’s not going to change. Organizations need guardrails that operate during AI usage without slowing developers down. Security needs to keep up, since blocking AI tools is not a viable strategy.
The right approach is to apply security controls that integrate seamlessly into existing workflows. This means:
- Defining clear boundaries for AI usage
- Preventing sensitive data exposure
- Detecting and stopping prompt-based attacks in real time
- Ensuring AI-generated code meets security and compliance standards before it enters the pipeline
The question isn’t whether to use AI in development, it’s whether you can control it.
FAQ
AI code assistants aren’t the security risk; the risk lies in the lack of visibility into how they use your tools, what data gets shared, and what ends up in your code.
Not if implemented correctly. Controls that integrate into existing workflows reduce risk without slowing down development.
Regulations like GDPR and HIPAA govern how systems behave and how data is handled, regardless of how the code was created. Without traceability into how code was generated, you can't prove it's compliant.
No. Even with approved tools, organizations lack control over how they’re used or what data is shared.
Map actual AI usage first: which coding assistants are in use, by whom, and what data is shared in prompts. Without this, security teams can’t assess risk or enforce controls.
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