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Discover how the seductive promise of AI productivity masks critical risks to your company's future. This report reveals the hidden costs of AI-driven development—from insidious vendor lock-in and novel security threats like "slopsquatting" to unprecedented legal liabilities—equipping leaders to safeguard their technical sovereignty and fi nancial stability in the AI era.
The Productivity Paradox
How AI Could Quietly Rewrite Your Code, Your Security, and Your Bottom Line
Generative AI coding assistants offer a dramatic boost in developer productivity, but this speed comes at a hidden cost. Beneath the surface, subtle biases and automated suggestions are embedding long-term risks into your most critical digital assets. This is the invisible ad rewriting your company's future.
The adoption of AI in development is no longer a trend; it's the new standard. A vast majority of developers now rely on AI for core coding tasks, fundamentally changing how software is built.
of developers use AI for tasks like writing and explaining code.
AI assistants are trained on popular ecosystems, causing them to recommend specific vendors. Developers accept these suggestions due to cognitive biases like 'Authority Bias', creating a powerful feedback loop that erodes technical independence.
1. AI Suggests Vendor-Specific Code
2. Developer Accepts Suggestion (Authority Bias)
3. Dependency on Vendor Ecosystem is Created
4. More Usage Reinforces AI's Bias
While developers feel more productive, AI-generated code often leads to higher "code churn"—code that is quickly deleted or rewritten. This indicates lower quality and creates a long-term maintenance burden.
AI models often "hallucinate" and suggest code that uses plausible but non-existent software packages. Attackers exploit this by registering these fake package names and filling them with malware, creating a predictable and dangerous supply chain attack.
1. AI hallucinates a package name.
2. Attacker registers the fake package.
3. Developer installs the malware.
The rate at which AI models invent dependencies is significant, with open-source models posing a substantially higher risk. Each hallucination is a potential gateway for a slopsquatting attack.
AI models learn from vast amounts of public code, including insecure patterns. A significant portion of AI-generated code contains known security flaws, directly injecting risk into your applications.
Places direct liability on the software "manufacturer" for any vulnerabilities.
or 2.5% of global turnover.
Transmitting proprietary code to third-party AI vendors triggers strict obligations.
or 4% of global turnover.
Regulations like the EU's CRA eliminate plausible deniability. The responsibility for security flaws in AI-generated code now rests solely with the company.
Liability is diffuse, often deflected to open-source projects or covered by EULAs.
Liability is absolute and consolidated with the software manufacturer.
Read the full story here.
That single line of code your AI just generated? It might have just locked your company into a multi-year, five-figure contract you never approved.
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