AI is creating developers faster than cybersecurity is creating secure developers.

Artificial intelligence is dramatically reducing the technical barrier to building software.

A person who previously needed programmers, designers, database engineers and technical consultants can now describe an idea to an AI development platform and potentially produce a working application within hours or days.

That transformation represents an extraordinary opportunity for entrepreneurship. But it is also creating a cybersecurity challenge that deserves considerably more attention.

The next generation of creators will not only create videos, articles, graphics and websites. They will increasingly create applications, AI agents, automation systems, marketplaces, customer portals, payment workflows and business-critical software.

OWASP's 2026 security work for agentic applications reflects this changing threat environment. Its framework focuses specifically on risks affecting autonomous AI systems that can plan, use tools and take actions across digital environments. NIST is likewise developing work around the identity, authorization and accountability of AI and software agents.

Understand what has changed

The traditional creator economy was primarily concerned with producing information.

The difference is critical.

A video can influence a viewer. A software application can access their identity, location, documents, financial information, passwords, business records or private communications.

Recognise the speed mismatch

AI has compressed the software-development cycle.

But cybersecurity cannot safely be compressed in exactly the same way.

The faster the first number grows compared with the second, the larger the security gap becomes.

ENISA's European threat-landscape work continues to document a highly active cyber-threat environment, analysing thousands of cybersecurity incidents across Europe. AI-generated applications are therefore entering an environment in which attackers are already highly active and continuously adapting their techniques.

Meet the new attack surface: the AI agent

Traditional applications often followed a relatively predictable model.

Agentic systems can create a significantly more complicated chain.

Every additional connection can introduce another trust relationship.

OWASP's AI Agent Security guidance identifies threats including direct and indirect prompt injection, tool abuse, privilege escalation and data exfiltration.

OWASP describes excessive agency as a condition in which an LLM-based application is granted functionality, permissions or autonomy that allows damaging actions to occur following unexpected or manipulated model outputs.

The Privacy Burst

The cybersecurity problem may eventually become an equally serious privacy problem.

Consider a creator who builds a simple AI recruitment tool.

AI Privacy Debt and AI Security Debt

Technical debt is already a familiar concept in software engineering. Organizations build systems quickly and later discover that shortcuts have created maintenance problems.

The AI economy may generate two additional forms of debt.

These problems may remain invisible while an application is small.

They become serious when adoption suddenly accelerates.

From Human Trust to Machine Trust

One of the most important future security questions may be:

NIST's 2026 AI Agent Standards Initiative explicitly focuses on the need for secure and interoperable agentic systems. NIST is also examining standards-based approaches to identifying agents, managing their authorization and controlling the actions agents are permitted to take.

For such an ecosystem to operate safely, machines will increasingly need mechanisms similar to the trust mechanisms humans use today.

Secure AI Creation by Design

Stopping people from creating with AI is neither realistic nor desirable.

The better solution is to make secure development part of the AI creation process itself.

The 8-Layer AI Creator Security Framework

These recommendations align with OWASP guidance emphasizing defenses against prompt injection, excessive permissions, unsafe tool access and data leakage in agentic AI environments.

What organizations should start doing now

Organizations do not need to wait for a major incident before adapting. A relatively simple preparation programme can substantially improve their position.

A simple rule before launching any AI-generated tool

Cybersecurity must evolve from application security to agent governance

The cybersecurity industry spent decades learning how to authenticate people, protect networks and secure applications.

The AI era introduces another identity into that ecosystem:

AI agents will increasingly operate on behalf of employees, customers, businesses and potentially other AI systems.

Security architecture therefore needs to answer new questions:

• Which human does this agent represent?
• What authority was delegated?
• For how long?
• Which information can it access?
• Which systems can it modify?
• Can it delegate authority to another agent?
• Can its permissions be instantly revoked?
• Can every important action be independently audited?

NIST's recent work on software and AI-agent identity and authorization shows that these questions are becoming part of mainstream cybersecurity standards discussion rather than remaining purely theoretical research.