AI Model Security for Startups: Real Risks, Practical Solutions and Investor Expectations

AI is quickly becoming the core asset of modern startups. But while startup founders often focus on model performance, features, and speed to market, one critical area is still widely underestimated: AI model security.

In a recent webinar, Marie Paindavoine, Founder and CEO of Skyld AI, Pascal Stürchler, CEO and Co-Founder of Bloomhaus Ventures and Laurent Decrue, Co-CEO of Holycode AG shared a practical and grounded perspective on where the real risks lie, how attackers actually exploit AI systems today, and what investors now expect from startups building with AI. The discussion made one thing very clear: AI security is no longer a niche concern, it is becoming central to both product integrity and company value.

AI security is a multi-layered problem

When people talk about AI security, they often default to prompt injection. While that is indeed a relevant issue, it represents only one part of a much broader attack surface. AI systems introduce risks at multiple levels, each with different implications depending on how the model is built and deployed.

At the supply chain level, risks include poisoned datasets or compromised model architectures. These can introduce hidden behaviors or vulnerabilities long before a model is even deployed. During inference, attackers may attempt to manipulate outputs using adversarial inputs or prompt injections, effectively bypassing safeguards. There are also confidentiality risks, such as model inversion or membership inference, where attackers attempt to recover sensitive training data or infer whether specific data points were included in training.

However, the most underestimated category is often the model itself. Model extraction and distillation attacks directly target the intellectual property behind an AI system. These attacks are particularly relevant for startups whose competitive advantage depends on proprietary models.

Model extraction is easier than most startup founders think

A key insight from the webinar is how accessible model extraction has become, especially for models deployed on-device or on-premise. While these deployment strategies are often chosen to improve data privacy, they introduce a different kind of risk: exposure of the model itself.

In practice, attackers can reverse engineer applications to locate model files. Even if these files are encrypted, the protection is limited. At some point, the model must be decrypted to perform inference, and attackers can intercept it in memory at that exact moment. Because inference engines are frequently open source, identifying where and when to extract the model becomes significantly easier.

The result is that even well-protected systems can be compromised relatively quickly. In the example discussed during the webinar, a major tech company’s model was extracted in under a week. For less sophisticated implementations, the same process can take only hours. This fundamentally challenges the assumption that encryption or basic obfuscation is sufficient to protect AI assets.

What happens after a model is stolen

Once an attacker has access to a model, the consequences extend far beyond simple copying. The most immediate risk is model reuse, where competitors can deploy the same functionality without incurring the cost of research, data collection, or training. This effectively erodes the original company’s competitive advantage.

A more advanced scenario involves model adaptation. Instead of using the model as-is, attackers can fine-tune it for related use cases. Because retraining an existing model requires significantly fewer resources than building one from scratch, this creates a shortcut for competitors to enter adjacent markets.

Perhaps the most concerning outcome, however, is model exploitation. With full access to the model’s architecture and parameters, attackers can design adversarial inputs that systematically cause misclassification. These inputs often involve subtle perturbations that are imperceptible to humans but highly effective at confusing the model. In practice, this can render an AI system unreliable or even unusable.

Adversarial attacks undermine trust at scale

Adversarial attacks highlight a fundamental weakness in AI systems: their sensitivity to carefully crafted inputs. By understanding how a model was trained (particularly its loss function and optimization process) attackers can manipulate inputs to maximize error rates.

In the case of content moderation, this can lead to both false positives and false negatives. Harmless content may be flagged incorrectly, while harmful content may pass through undetected. While the technical mechanism behind these attacks is complex, the business implication is straightforward: loss of trust.

For products that rely on consistent and reliable outputs, such as moderation systems, fraud detection, or safety-critical applications, this kind of failure is not just a technical issue. It directly impacts user confidence and, ultimately, product viability.

Traditional security approaches fall short

One of the more counterintuitive insights from the discussion is that traditional software security methods are not well suited for AI systems. Techniques like encryption and code obfuscation, while effective for protecting conventional software, do not translate cleanly to machine learning models.

The core issue is that models are not compiled code; they are data structures that must remain accessible to inference engines. This makes them inherently more exposed. Even if stored securely, they must eventually be loaded into memory in a usable form, creating an opportunity for extraction.

This distinction means that AI security requires a fundamentally different approach. Applying standard software protections without adapting them to the unique properties of models often results in a false sense of security.

Investors are shifting their expectations

From an investor perspective, the importance of AI security is growing rapidly. While early-stage startups may not be expected to have fully mature security frameworks, there is now a clear expectation that founders understand the risks and have a plan to address them.

Due diligence is evolving accordingly. Investors are no longer evaluating only infrastructure and code; they are also examining the models themselves. Questions about robustness, reliability, and susceptibility to attack are becoming standard. In later stages, particularly when companies have enterprise customers, weak security can directly affect valuation and deal outcomes.

This shift reflects a broader recognition that, in many AI startups, the model is not just part of the product – it is the product.

Security as a strategic advantage

Beyond risk mitigation, security is increasingly becoming a competitive differentiator. In industries where trust and compliance are critical, such as healthcare, legal tech, or finance, strong security practices can influence purchasing decisions.

Customers are beginning to ask not only whether a product works, but whether it is secure, reliable, and resilient under attack. Startups that can provide clear answers to these questions are better positioned to win enterprise deals and build long-term relationships.

In this sense, security is moving from a backend concern to a core component of product strategy.

Practical steps for founders

While there is no universal solution, several practical steps can help startups build a stronger foundation. The first is to create a clear inventory of AI assets, including models, data sources, and external dependencies. Understanding what exists and who has access to it is a prerequisite for any meaningful security effort.

The second step is to conduct a risk assessment tailored to the specific use case. Not all attacks are equally relevant, and prioritization should be based on potential impact rather than theoretical possibilities.

Building security considerations into the development process from the beginning is also critical. This does not mean implementing full compliance frameworks at the MVP stage, but it does require thinking ahead and documenting decisions. Over time, this can evolve into a structured roadmap that aligns with industry standards and regulatory requirements.

Finally, it is important to recognize that speed alone is not a defense. Iterating quickly does not prevent model extraction or exploitation. If a vulnerability exists, it can be exploited repeatedly, regardless of how fast new versions are released.

Final thoughts

AI security is still an emerging field, and many challenges remain unsolved. From prompt injection to adversarial attacks and model extraction, the landscape is evolving rapidly. What is clear, however, is that ignoring these risks is no longer an option.

For startups, the key takeaway is simple but significant: the AI model is a core asset, and protecting it requires intentional effort. Those who treat security as an afterthought may find their advantage eroded quickly. Those who address it early, on the other hand, are more likely to build resilient products and sustainable businesses.

Access the full webinar replay in the Swiss Startup Association Education Library, free for members. Not a member yet? Join the community and get access to practical sessions that help you protect your business before something goes wrong. 

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