Is AI For Citizen Developers A Security Risk?

Introduction

Yes, AI for citizen developers does present significant security risks, but these risks can be effectively managed through proper governance, security frameworks, and training programs.

The integration of AI into citizen development platforms has fundamentally transformed how organizations approach application development, but it has also introduced a complex array of security challenges that require careful consideration and proactive management.

The Core Security Risks

Inadequate Security Awareness in AI-Generated Code

One of the most significant risks stems from the fact that AI systems excel at generating functionally correct code but often lack the security awareness that experienced developers possess. Traditional software development incorporates security considerations implicitly through developers’ experience with real-world failures, but generative AI lacks this depth of experience and focuses narrowly on the task at hand1. This results in incomplete or inadequate security measures in AI-generated applications.

Pattern Replication and Vulnerability Inheritance

AI coding assistants function by predicting code sequences based on training data, which creates several unique security challenges. These systems tend to replicate patterns from their training data, including insecure ones, making common vulnerabilities in open source code become templates that AI reproduces without understanding their security implications. Research shows that almost half of code snippets produced by AI models contain bugs that could potentially lead to malicious exploitation.

The Comprehension Gap

A critical concern is the growing “comprehension gap” between what’s deployed and what development teams actually understand. Developers increasingly implement AI-suggested code they don’t fully understand, which increases the likelihood that vulnerabilities will go undetected during code reviews and testing phases.

Specific Vulnerabilities in AI-Enabled Citizen Development

Critical Infrastructure Vulnerabilities

Recent research has identified numerous critical vulnerabilities in AI and machine learning tools commonly used in citizen development platforms. The Protect AI bug bounty program has uncovered 32 security defects, including critical-severity issues that could lead to information disclosure, access to restricted resources, privilege escalation, and complete server takeover. Notable examples include:

• CVE-2024-22476 in Intel Neural Compressor software with a CVSS score of 10, allowing remote privilege escalation

• Critical vulnerabilities in popular platforms like H2O-3, MLflow, and Ray that lack authentication by default

• Authorization bypass vulnerabilities in AI development platforms that allow unauthorized access to organizational resources

Shadow IT and Governance Challenges

Citizen development naturally creates shadow IT environments where applications are built without proper IT oversight. This democratization of development fundamentally alters the traditional application security attack surface, introducing new vulnerabilities that often fall outside the purview of traditional IT controls.

Key shadow IT risks include:

• Unmanaged and potentially insecure systems that bypass established security controls

• Data leakage through misconfigured integrations between citizen-developed applications and enterprise systems

• Insecure applications built by citizen developers who may inadvertently introduce vulnerabilities such as improper access controls or flawed business logic

The OWASP Framework for Low-Code/No-Code Security

The Open Web Application Security Project (OWASP) has developed a comprehensive framework specifically addressing security risks in low-code/no-code development environments. The OWASP Low-Code/No-Code Top 10 identifies critical vulnerabilities:

1. Account Impersonation – Attackers impersonating legitimate users

2. Authorization Misuse – Incorrect permission assignments to end users

3. Data Leakage and Unexpected Consequences – Unintended data exposure through poor application design

4. Authentication and Secure Communication Failures – Weak authentication and insecure configurations

5. Security Misconfiguration – Default settings and inadequate security configurations

Mitigation Strategies and Best Practices

Comprehensive Security Governance Framework

Organizations must implement a robust security governance framework that addresses both technical and procedural aspects of AI-enabled citizen development. This framework should include:

• Structured governance policies that define boundaries and expectations for citizen developers, including standards for data security, privacy, compliance, and application lifecycle management.
• Continuous monitoring and risk assessment capabilities that provide visibility into all citizen-developed applications, automations, and integrations.

Security Training and Awareness Programs

Specialized training programs for citizen developers are essential to address the security knowledge gap. The Canadian Centre for Cyber Security has developed comprehensive training that covers:

• Secure coding principles and common vulnerabilities

• Data encryption and digital signing techniques

• Threat recognition and countermeasures specific to citizen development environments

• Vulnerability management approaches tailored to low-code/no-code platforms

Technical Security Controls

Organizations should implement multiple layers of technical security controls:

• Access controls and authentication using multi-factor authentication, role-based access controls, and automated access reviews.
• Data protection measures including encryption, input validation, and bias detection to secure AI training data and maintain model integrity.
• Continuous monitoring and testing with AI-specific security testing tools that can detect vulnerabilities like data poisoning and model extraction.

Platform-Level Security Measures

Modern low-code/no-code platforms increasingly incorporate built-in security features that act as “guardrails” for citizen developers. These platforms provide:

• Pre-built security components developed by professional software engineers rather than citizen developers
• Integrated governance and reporting capabilities that enable IT departments to monitor compliance, security, and maintainability
• Automated security scanning and validation that can detect common vulnerabilities before applications are deployed

Risk-Benefit Analysis and Organizational Considerations

Uneven Risk Distribution

The security risks associated with AI-enabled citizen development will not be evenly distributed across organizations. Larger, more well-resourced organizations will have an advantage over organizations that face cost and workforce constraints. This creates a significant disparity in security posture across different types of organizations.

Balancing Innovation and Security

The key to successful AI-enabled citizen development lies in balancing empowerment with security. Organizations must create environments where innovation can flourish within the boundaries of robust security measures. This requires a proactive, multifaceted approach that includes strict authorization structures, API and data access protocols, and comprehensive monitoring capabilities.

Statistical Context

Recent data underscores the urgency of addressing these security concerns. AI-related incidents have risen by 690% between 2017 and 2023, while 93% of organizations experienced security breaches in the past year, with nearly half reporting estimated losses exceeding $50 million. These statistics highlight that AI security practices are no longer optional but essential for organizational survival.

Conclusion

While AI for citizen developers does present significant security risks, these risks are not insurmountable. The key lies in implementing comprehensive security governance frameworks, providing adequate training and support, and leveraging built-in platform security features. Organizations that proactively address these challenges can harness the benefits of AI-enabled citizen development while maintaining robust security postures.

The future success of AI-enabled citizen development programs depends on organizations’ ability to establish proper governance, implement technical safeguards, and foster a security-aware culture among citizen developers. With proper planning and execution, the security risks can be effectively managed, allowing organizations to realize the significant productivity and innovation benefits that AI-enabled citizen development platforms offer.