AI Model Selection: A Pivotal Step in Every Implementation

As AI systems become more deeply embedded in business operations, product design, and decision-making workflows, the pressure to “get it right” has never been higher. Yet amid the focus on data pipelines, infrastructure, and user interfaces, one foundational element often escapes early scrutiny: the AI model itself. 

What Is an AI Model 

The AI model itself is the computational engine of any AI system, and its capabilities play a critical role in shaping the security of AI applications. Whether using proprietary models (like GPT, Gemini, Claude), open-source options (like Llama, Gemma, Mistral), or ML models trained in house, companies must assess the trustworthiness, robustness, and alignment of the models they integrate. 

Different use cases demand different capabilities, and not all models are created and maintained with enterprise-level scrutiny. Risks emerge from uncertain origin, undisclosed updates, embedded vulnerabilities, and misalignment with users’ goals and safety standards. Models can also behave unpredictably under adversarial pressure or perform inconsistently when combined with proprietary data or exposed to unregimented user inputs. Below, we’ll outline the key risks organizations should consider when selecting, deploying, and maintaining AI models in production environments. 

Use of Unverified or Untrusted Models 

AI models obtained from unvetted or unofficial sources pose significant risks to enterprise security and reliability. While the open-source ecosystem has played a vital role in democratizing access to powerful machine learning tools, it also creates a wider attack surface for adversaries.  

Models can be deliberately manipulated to include hidden backdoors: logic that remains dormant until activated by a specific input. In one case, attackers embedded malicious behaviors into the architecture of a model, allowing them to trigger unexpected actions when a particular token sequence was used. This is especially important for agentic architectures, as we gradually give AI agents more and more autonomy for executing tasks and decisions. Such vulnerabilities, like AI data security risks, are extremely difficult to detect through casual inspection or standard validation workflows, especially when the models are large and opaque by design. 

What makes this risk particularly pressing is that it often flies under the radar during fast-moving development or prototyping phases. Teams under pressure to innovate or ship proof-of-concept systems may reach for a pre-trained model without thoroughly evaluating its origin, license, or security implications. However, once these models are embedded in customer-facing products or connected to sensitive internal systems, their behavior becomes an operational risk. The risk is even more evident in regulated industries, like pharmaceutical, where regulations like GxP heavily influence the design and security standards of AI solutions. Without clear documentation, version tracking, or long-term support, organizations may find themselves locked into brittle systems that cannot be patched or audited effectively. 

Even without malicious intent, AI model development simply is difficult to do, and an organization that doesn’t have enough expertise or reputation might not be up to the task. That’s why I recommend avoiding that risk and going with one of the best AI models built by industry leaders. 

Using models from reputable providers (e.g., Microsoft, Anthropic, Google, OpenAI) helps ensure better oversight, patching, documentation, and predictability in behavior and performance. 

AI Model Bias and Misalignment 

Even the most meticulously trained models can harbor hidden biases or yield outputs that drift from an organization’s core values, legal obligations, or ethical commitments. These issues often arise not from malicious intent, but from the historical data models are trained on: data which may encode unequal treatment, or biased decisions. 

AI systems reflect both the strengths and failings of their creators’ data. Bias and misalignment are rarely overt. They hide in proxies, historical precedents, and design assumptions that seem benign at first glance. Yet when such systems are used to screen candidates, determine loan eligibility, assign medical care, or influence public perception, their impact is anything but benign. How to train AI models in a way that mitigates this risk? The organization needs to eliminate prejudice and bias from the training data sets. But this is much easier said than done, which is why this has been such a pressing issue in the AI area. 

In the era of AI agents and ready-made models, the problem of alignment has been resolved. Despite the efforts of vendors building and selling these models, biased behaviors can still be detected, especially in less common use cases.  

Addressing these risks requires proactive governance: fairness audits, robust monitoring, and well-designed human-in-the-loop systems. It also demands an organizational mindset that treats alignment not as a one-time checkbox, but as a sustained operational imperative. Only then can companies ensure their AI supports, and not undermines, the equitable and responsible achievement of business goals. 

Model Robustness and Output Stability 

Large Language Models (LLMs) often produce different outputs for the exact same prompt, a behavior rooted in their architecture. In addition, even small changes in whitespaces or punctuation can affect the final result.  

This issue isn’t academic. An empirical study of ChatGPT’s code generation performance revealed stark instability: across hundreds of prompts, only a small share returned identical code outputs each time. This issue is relevant to all outputs, not only code generation. Such inconsistency undermines confidence: in critical applications like legal summarization, financial reporting, or medical diagnostics. Variance from run to run isn’t just inconvenient; it can be dangerous.  

Use cases involving regulated environments, legal exposure, human health, or customer trust demand output consistency from AI. Choosing the right AI model for your use case won’t solve all consistency issues, but it’ll certainly help mitigating those risks.  

Resistance to AI Model Extraction and Theft 

Even without having direct access to a model’s code, attackers can effectively reconstruct proprietary AI models through repeated API queries; a process known as model extraction or model distillation. Model inversion attacks take this threat further by letting attackers infer sensitive attributes from model outputs, potentially exposing private data like personal health records or proprietary images. This risk is especially important when exposing AI application to the public.  

Even when model code isn’t exposed, predictive APIs can leak underlying logic and confidential data, letting malicious actors bypass licensing restrictions or spin up competing versions of your model. The consequences of such attacks go beyond intellectual property loss. Once stolen, attackers can fine-tune clones with malicious payloads or use them to mount further adversarial threats, all while evading detection or attribution. 

Companies can mitigate those risks via rate limiting and API restrictions, watermarking, output monitoring and anomaly detection, as well as randomization and obfuscation. While these techniques can’t make their models 100% safe, they’ll significantly raise the cost and complexity of model theft.   

Model Drift and Lifecycle Management 

As model providers roll out updates, improving performance, patching vulnerabilities, or tweaking behavior, organizations without strict version control can find themselves facing silent shifts in AI output. These shifts, sometimes subtle, can disrupt workflows, undo hard-won fixes, or unexpectedly reintroduce bias. 

Mitigating version drift starts with pinning model versions and implementing strict lifecycle management. In mature MLOps practices, organizations employ CI/CD pipelines that treat models like code: versioned, tested, and promoted through environments only after passing robust validation gates. 

Ultimately, robust lifecycle management treats AI models as evolving assets rather than static services. Without version pinning, documentation, controlled rollout, and drift monitoring, AI systems can shift unexpectedly, disrupting compliance, user experience, and trust. By contrast, organizations that bake lifecycle discipline into their AI governance can confidently navigate updates while maintaining consistent, reliable behavior. 

API Security and Exposure Risks 

When models are exposed via APIs, whether hosted publicly or integrated through third-party endpoints, they inherit all the vulnerabilities of web services, amplified by the unique ways LLMs process language. Attackers can exploit these endpoints through unauthorized access, rate-limiting bypass, crafted input, or even novel methods like prompt injection to manipulate model outputs or extract sensitive information. 

One recent high-profile example involved the Chinese model DeepSeek R1, which yielded a 100% success attack rate in safety tests by Cisco and the University of Pennsylvania. Attackers were able to bypass all 50 malicious prompts, allowing the model to produce forbidden content like bomb-making instructions, demonstrating how inadequate guardrails at the API level translate directly into practical threats. 

Similarly, OpenAI’s ChatGPT search tool was shown to be susceptible to prompt injection attacks via hidden webpage text. Researchers found that maliciously hidden content could manipulate ChatGPT’s responses, for example, turning negative product reviews into glowing endorsements. Prompt injection leverages the LLM’s inability to distinguish between system, user, or external instructions, leading to manipulated outputs. 

These risks can directly threaten data privacy, regulatory compliance, and brand reputation. By implementing strong API security, layered input/output controls, restricted privileges, and continuous adversarial testing, organizations can significantly reduce the attack surface and safeguard model integrity. 

The Mismatch Between General-Purpose Models and Task-Specific Expectations 

Foundation models are trained on broad, diverse datasets intended to support general language understanding, not specialized tasks. In real-world applications, however, organizations often expect AI systems to perform precise, domain-specific functions, such as interpreting legal language, referencing technical documentation, or assisting in domain-specific tasks.  

This mismatch can lead to incorrect or outdated responses, particularly when the model relies on pretraining data that conflicts with or overrides newer, domain-specific information, which can result in serious errors and reduced trust in system outputs.  

Mitigating this risk requires grounding the model in verified, up-to-date knowledge. One effective strategy is retrieval-augmented generation (RAG), where the model is paired with a search or database layer that dynamically feeds it relevant context at inference time. This allows the model to “think with the docs,” improving both factual accuracy and contextual awareness. For deeper alignment, companies employ the fine-tuning or domain adaptation techniques, adjusting the model’s weights using curated datasets from a given industry or organization. 

Guidelines for Model Management in AI Deployments 

The models are at the heart of any AI application – but selecting and managing them is far from a one-time technical task. Mitigating risks associated with AI models requires organizations to treat model selection, deployment, and evolution as critical governance responsibilities. 

A sound model strategy begins with selecting trustworthy, well-documented models from reliable providers, supported by transparent benchmarks and an understanding of performance boundaries. From there, deployment must be tailored to specific applications, accounting for the model’s intended tasks, exposure to proprietary data, and potential failure modes. Regular testing, performance evaluation, and version control are essential; particularly in environments where even small changes in model behavior can have downstream business or compliance implications. The roadmap below outlines actionable practices for selecting, deploying, and managing AI models with security, reliability, and accountability at the forefront. 

1. Select Safe, Maintained, and Audited Models 

The starting point is model trustworthiness. Organizations should prioritize models that are actively maintained, publicly benchmarked, and, where applicable, open to external evaluation. Model selection must not be based on hype or convenience alone. Instead, AI leaders must adopt a clear auditing policy that governs which models can be exposed to broader user bases. 

2. Treat Model Selection as Use Case–Specific 

AI is not monolithic: the right model depends on what you’re trying to do. Common enterprise use cases such as document Q&A, summarization, translation, ideation, code or image generation, presentation drafting, or Text-to-SQL querying all have different safety and performance profiles. Organizations must assess each model within the context of the specific task it supports, not just based on its general capabilities. 

3. Go Beyond Public Benchmarks 

While public benchmarks (e.g. MMLU, HELM, MT-Bench) are useful starting points, they often fail to reflect real-world, proprietary data and workflows. Applications that ingest internal documentation or provide user-specific assistance need custom evaluation pipelines. Safety assessments must be contextualized and go beyond what public tests alone can reveal. 

4. Leverage LLM Safety Benchmarks Where Applicable 

For general-purpose assistants or user-facing chat tools, models should also be tested against dedicated safety benchmarks such as AdvBench, PoisonBench, or RobustBench to evaluate robustness against adversarial prompts, hallucination, and misalignment. These evaluations can inform risk assessments before production rollout. 

5. Model Change as a Controlled Process 

The AI ecosystem evolves rapidly, and switching to newer, faster, or cheaper models is tempting, but model replacement must be treated as a change process. Especially when models power critical features (e.g., search, recommendation, legal drafting), validate that new versions don’t introduce regressions in task performance, particularly where knowledge base grounding or in-context demonstrations are involved. 

6. Monitor Model Versions and Behavior Continuously 

Many API providers change model behavior silently: e.g., OpenAI’s gpt-3.5-turbo version of late 2022 vs. late 2023. If your application depends on consistency or regulatory traceability, such opaque versioning is risky. Prefer deployments that offer explicit versioning and change logs, and implement monitoring for behavioral drift over time. 

7. Prefer Self-Hosted or Exclusively Hosted Models Where Possible 

Shared-hosting setups, while economical and suitable for proofs of concept, lack tenant isolation, increase risk exposure, and offer limited control. When data sensitivity or performance predictability matters, organizations should opt for self-hosted or dedicated cloud instances, especially for fine-tuned or instruction-aligned models. 

8. Examine Plugin and Third-Party Model Policies Carefully 

Integrating AI plugins or third-party tools (e.g., copilots, CRM assistants, browser extensions) adds another layer of risk. Ensure that data submitted through such tools is not used to retrain or fine-tune the underlying models, unless explicitly authorized. Review privacy terms, data retention policies, and opt-out mechanisms to stay compliant and protect proprietary information. 

Summary: Getting AI Models Right is a Strategic Decision 

AI models sit at the core of modern intelligent systems, but they are not interchangeable black boxes. As organizations move from experimentation to real-world deployment, the characteristics of the models they choose become critical variables in their risk profile. From using unverified models that may contain hidden vulnerabilities, to deploying general-purpose models for specialized tasks, each choice carries implications for security, performance, and trust. 

Managing model risks isn’t just a technical challenge, it’s a strategic one. Choosing the right model involves understanding its lineage, behavior, deployment mechanisms, and alignment with organizational goals. It also means investing in governance, monitoring, and lifecycle controls that evolve with the model. 

If you want to learn more about planning your AI initiatives, read our compendium on safe AI deployments in enterprise organizations.