Large Language Models (LLMs) and freelance developers have transformed how companies build software. Faster delivery, lower upfront costs, and on-demand expertise make them highly attractive, especially for startups and growing businesses.

But when it comes to security-critical software, these approaches also introduce hidden risks that are often underestimated. From intellectual property exposure to compliance gaps and inconsistent security practices, relying solely on LLMs or freelancers can quietly increase a company’s financial and operational risk.

Intellectual Property and Data Leakage

One of the biggest risks when using LLMs is data exposure. Developers frequently paste source code, configuration files, error messages, or even credentials into AI tools to get faster answers.

While many LLM providers claim strong safeguards, companies often lose visibility and control over what data is shared and how it is stored or reused. In regulated industries, this can easily violate internal policies, customer contracts, or data protection laws.

Freelancers introduce a similar challenge. Source code may be stored on personal devices, shared across multiple clients, or reused unintentionally. Even with NDAs in place, enforcement is difficult, especially across borders.

Once sensitive logic or infrastructure details leave your controlled environment, the financial consequences of a leak can be significant.

Inconsistent Security Standards

LLMs generate code based on patterns found in public repositories. This means:

• Vulnerable examples may be repeated
• Outdated libraries can be suggested
• Security best practices are not guaranteed

The result is often code that works, but is not hardened against real-world threats like injection attacks, insecure authentication, or misconfigurations.

Freelancers face a similar issue. Each developer brings their own habits, tooling, and security awareness. Without strong internal standards and continuous oversight, security becomes inconsistent across your codebase, making it harder to maintain and audit over time.

Lack of Accountability and Long-Term Ownership

When a vulnerability appears months after release, accountability matters. With LLM-generated code, responsibility always falls back on the company. There is no vendor liability, no SLA, and no guarantee that generated logic aligns with your architecture or threat model.

Freelancers may no longer be available when issues arise. Fixing security flaws later often costs significantly more than addressing them early, both in development time and potential business impact.

This lack of long-term ownership increases the financial risk associated with security incidents, downtime, and emergency remediation.

Compliance and Audit Challenges

Many organizations must comply with standards such as ISO 27001, SOC 2, or internal security frameworks. These require:

• Traceability of changes
• Evidence of secure development practices
• Regular security testing

LLMs do not produce audit trails. Freelancers may not follow required documentation processes unless explicitly enforced. As a result, companies often struggle to prove compliance - even if no breach has occurred.

Failing an audit or delaying certification can directly impact sales cycles, partnerships, and investor confidence.

Why Security Scanning Still Matters

LLMs and freelancers are tools, not security strategies. To reduce risk, organizations need continuous, automated visibility into their applications.

This is where application security scanning becomes critical. By systematically analyzing applications for vulnerabilities, misconfigurations, and exposed components, companies can:

• Detect issues introduced by AI-generated or third-party code
• Reduce remediation costs by fixing problems early
• Support compliance and audit readiness
• Lower the financial impact of breaches

Security scanners act as a neutral control layer, independent of who, or what, wrote the code.

Conclusion

LLMs and freelancers can dramatically improve development speed, but speed without control increases risk. Intellectual property exposure, inconsistent security practices, compliance gaps, and unclear accountability all translate into real financial consequences.

Organizations that combine modern development approaches with continuous security scanning gain the best of both worlds: innovation without blind spots. In today’s threat landscape, visibility and consistency are no longer optional—they are essential for protecting both software and business value.