Every new frontier brings both pioneers and peril. Artificial intelligence is no different. Its rapid democratization is allowing this profound, paradigm-shifting technology to be used by a wider audience well beyond the data scientists and trained coders.
As AI tools trickle down into more untrained hands, large language models (LLMs) now guide untrained individuals through complex coding processes at blinding speed, ushering in the age of the citizen developer. However, these Python-slinging developers create a lawless cloud zone of half-baked or abandoned coding projects. Unchecked, these projects dramatically increase the number of available attack vectors and expose corporations to a new host of vandals who can ride in and hold corporate data hostage.
Reports describe this growing data governance problem and how it has already increased cyberattacks: 13% of organizations have reported breaches of AI models or applications, and among those breached, 97% lacked proper AI access controls. What’s even more concerning is that 63% of breached organizations either don’t have an AI governance policy or are in the process of developing one. All this is unfolding as cybercrime is projected to cost businesses up to $10.5 trillion by 2025.
The Expanding Attack Surface
The rise of citizen developers, non-engineers using AI-powered tools to build applications and conduct analyses, is spreading organizational data across previously controlled boundaries. From a cybersecurity perspective, this new trend widens the attack surface area.
AI tools require data; without it, they are ineffective. When business users feed sensitive datasets into unsecured AI environments, they will unknowingly increase the organization’s overall attack surface area by lowering the walls around their fort, leaving their organization vulnerable beyond traditional IT perimeters. The result is a two-tier problem: rapid AI development and inadequate AI governance.
The Governance Imperative
Before allowing AI-based analytics access to any dataset, organizations must first ask:
- Do we have the correct security in place to process data in this manner?
- Has the data been appropriately cleansed for the specific analyses being conducted?
These questions are not mere formalities but the foundation of responsible AI deployment. Any outliers must be evaluated for their potential usefulness in modeling rather than automatically discarded. Each of these decisions has implications for the integrity of AI outputs and underlying data security.
The framework connecting exploitability, vulnerability, and probability of exposure becomes critical when considering AI workflows. Threat actors more readily breach systems where sensitive data flows freely, and even more enticing are citizen developer environments that lack the security controls present in traditional IT systems. Increased access points and inadequate governance surrounding valuable data are exactly the combination of factors that get the attention of cybercriminals.
Building a Security-First AI Strategy
Security-first goes beyond maintaining compliance. Effective AI deployment requires key operating principles. These principles must be executed, managed, and tracked against compliance requirements and organizational policies. A formal management program is key to this approach; the program must be well-governed, systematically tracked and built for managing exceptions. In this manner, organizations can maintain visibility into where their data flows through AI, who has access to it, and how it’s being used.
The challenge extends beyond access control. There is a risk when retaining data and allowing for necessary data replication through citizen developer programs. Frameworks must be implemented that appropriately protect data for confidentiality and compliance while allowing business-user AI tool access.
Success requires a combination of security engineering and data governance disciplines anchored in clearly defined risk tolerance, transparency, and shared responsibility guidelines. Security teams must work in tandem with the organization’s data stewards, and citizen developers cannot operate without understanding the implications of their data usage.
The Path Forward
The democratization of coding is AI’s manifest destiny. However, this migration comes with risks, and organizations must implement robust data governance frameworks. Otherwise, they may find themselves open to hostile attacks perpetrated by the weaponization of their coding projects. The fact remains, data becomes ammunition for threat actors exploiting inadequately secured AI-based projects.
The solution does not restrict innovation; instead, it installs guardrails that enable secure AI deployment at scale. Governance policies must clearly define ownership, accountability, and escalation paths. Training programs must also teach citizen developers the fundamentals of security. This establishes clear policies for AI governance and implements technical controls that enforce data protection requirements without stifling creativity.
The data in AI pipelines must be checked for accuracy, compliance and biases, as well as for lawful processing rights, cleansing integrity, and proper outlier treatment. Organizations should implement automated tools, combined with manual oversight, for proper vulnerability assessments. Code reviews should examine both algorithms and software for potential security flaws.
As untrained coders leverage AI on a journey to application boomtown, the question is: How do you mitigate risk while cultivating enthusiasm? Those organizations that succeed will be the ones that see data governance as the sheriff of AI security. Without established guidelines, democratized AI will become the lawless wild west ruled by black-hatted hackers.
