Bridging the Gap Between AI Productivity and Secure Development

Artificial intelligence can improve efficiency by reducing the time required to complete many development tasks, but it is not foolproof. One of the things it is used for nowadays is coding. The issue is that research - including findings from Veracode (2025) - indicates that nearly half of AI-generated code contains security flaws. Common issues include inadequate input validation, poor encryption practices, and insufficient protection against injection-based attacks.

AI coding models do not inherently prioritize security in the same way experienced developers typically do. Instead, they generate code based on patterns learned from training data rather than applying threat modeling or secure design principles. Additionally, if the AI model isn’t directly prompted to perform things such as security checks, it can result in critical safeguards being omitted and increase the likelihood of vulnerabilities.

According to a 2025 Veracode report, almost half of all AI-generated code introduced vulnerabilities that led to a range of security weaknesses. In addition, academic studies have identified numerous security weaknesses associated with common CWEs (Common Weakness Enumeration). Developers may also become overconfident in AI-generated output, leading to insufficient review and oversight.

Static analysis tools such as SonarQube, CodeQL, or Veracode Static Analysis can scan code for known vulnerabilities before deployment. These tools can identify insecure coding patterns, including buffer overflows, injection risks, and exposed credentials. Security-focused testing should also be performed in addition to functional testing. (This includes testing for invalid inputs, authentication failures, boundary misuse, and known exploit scenarios). When generating code with AI, it is essential to define expected security requirements explicitly. If prompts do not include security goals, the AI model is unlikely to include them. When AI suggests external packages or libraries, treat them like third-party code and check their CVE history and update frequency.

AI can save you time, but it doesn’t automatically save you from security risks. It relies on learned patterns rather than explicitly evaluating the security risks or attack scenarios. With the right practices - such as static analysis, testing, and code reviews - you can use AI for productivity while still keeping your code secure.