AI sandbox: secure environments for evaluating and protecting Artificial Intelligence models

The dynamic nature of innovative and disruptive technologies is pushing companies to act swiftly to prevent gaps that could compromise security, financial stability, and privacy. But just how imminent are these associated or derivative risks, and what measures should organizations and governments take to proactively mitigate them?

AI has become essential for both critical and non-critical systems, and is present in sectors ranging from finance and healthcare to national security. Moreover, it is deeply embedded in the fabric of socioeconomic and industrial development.

However, its exponential growth and evolution are raising increasing concerns about risks related to Cyber Security, intellectual property, privacy, and others—including the ethical challenges associated with malicious actors.

Dynamic shields against cyber threats, privacy issues and ethical challenges

AI sandboxes have emerged as isolated environments that serve as essential tools to face these challenges, offering a dynamic, controlled, and secure space where AI models can be tested, analyzed, and protected before deployment.

AI sandboxes allow for experimentation without causing repercussions outside the confined environment.

These environments allow us to test AI models and systems against various cyber threats, performance demands, and ethical issues before deploying them in the real world. Unlike traditional software sandboxes—mainly used to analyze code, malware, or vulnerabilities—AI sandboxes are specifically designed to map situational heatmaps and the complexities of AI in both substance and form.

Testing, threats and governance: the strategic role of isolated environments

For developers, these environments make it possible to observe how AI models interact with different datasets, simulate potential cyberattacks, and identify flaws in their decision-making processes. A few weeks ago, I was assessing an AI environment to test a fraud detection system capable of accurately distinguishing between legitimate transactions and sophisticated fraud attempts, triggering compliance or due diligence alerts.

By providing a secure space for experimentation, modeling, and analysis, these environments help ensure models function properly before integration into real-world systems.

It’s important to note that AI-based systems are becoming prime targets for cyberattacks, and AI sandboxes are key to detecting and mitigating these threats by simulating real-world scenarios and evaluating how models respond.

AI under pressure: anticipating, detecting, and withstanding sophisticated attacks

In one of my recent analyses, I identified machine learning (ML) techniques used by malicious actors to manipulate AI models. In that environment, developers exposed models to adversarial inputs—subtle data modifications designed to deceive the system—to evaluate their resilience.

—For example, if an AI-based security system misclassifies a malicious email as legitimate, the sandbox allows the model to be adjusted and its defense reinforced.

■ Detecting these gaps before deployment helps prevent consequences that could jeopardize the organization’s operations.

Strengthening governance and anticipating threats through simulation

Cyberattacks are among the most pressing threats to AI systems. They often involve injecting manipulated data to mislead models into making incorrect decisions. Scenario development is key to anticipating and understanding our environment and its potential changes.

In fields like computer vision, small modifications to an image—imperceptible to humans—can cause an AI system to misclassify objects.

—For example, stickers on a “stop” sign could make an autonomous vehicle interpret it as a speed limit sign, with dangerous consequences.

■ Cyber Security teams in AI environments can generate adversarial models, patterns, and examples, and enhance defenses against cyberattacks. These environments also help address AI hallucinations—cases where Generative AI models produce false or misleading results—by testing and refining responses before actual use and deployment.

AI under scrutiny: regulatory frameworks and compliance testing

Acknowledging these risks, regulators have begun to establish frameworks to ensure the responsible use of AI. The European Union, through its AI Act, and NIST with its AI Risk Management Framework, highlight the importance of testing, transparency, privacy, and security—areas where sandboxes play an essential role.

However, regulatory compliance doesn’t automatically equate to security, though it does help foster a structured environment of best practices for evaluating and mitigating risks. The AI Act, for instance, requires that high-risk systems undergo rigorous testing for bias, safety, ethics, and transparency before being deployed.

Regulations provide appropriate safeguards in line with best practices for control, risk, ethics, and legal aspects.

AI in critical sectors: healthcare, finance and mobility

AI is increasingly integrated into critical applications such as medical diagnostics, financial fraud detection, and autonomous vehicles. Ensuring safe and reliable performance is paramount. Isolated environments allow for rigorous validation before these systems are integrated.

In healthcare, for example, diagnostic imaging models may be biased if training data lacks diversity. However, bias or adversarial attacks could lead to incorrect diagnoses, endangering patients. Testing in sandboxes helps ensure accuracy across demographic groups, minimizing the risk of serious errors.

It’s essential to balance security and usability: overly strict controls hinder innovation, while overly lax ones fail to detect vulnerabilities.

Technical and ethical challenges of AI sandbox environments

Designing effective sandboxes comes with challenges. Cyber threats are constantly evolving, and no environment can simulate every real-world scenario. In these environments, it's vital to balance security and usability: too many restrictions hinder innovation, while too few fail to catch vulnerabilities.

By enhancing security and reliability, AI sandboxes help organizations build trust in AI systems and prevent potentially catastrophic failures.

At the same time, ethical concerns arise when testing AI in these environments. Simulating cyberattacks could inadvertently create gaps that malicious actors exploit. Therefore, responsible and supervised use of these environments is crucial to avoid unintended consequences.

Threat intelligence: from analysis to defense reinforcement

AI environments can enhance and strengthen cyber threat intelligence capabilities by enabling detection and analysis of AI-specific threats. For example, they are used to test malware detection systems and ensure they can identify sophisticated cyberattacks. Improving these capabilities means stronger defenses.

Moreover, Generative AI models—such as large language models (LLMs)—pose specific risks: disinformation, bias, and data leakage. Evaluating them in sandboxes helps detect these risks before they impact real-world environments.

In a testing environment it is essential to assess risks before implementation through detailed, realistic, and objective analysis.

Security, transparency, and the future of responsible AI

In this era of AI enthusiasm, many want to adopt it without putting the right controls in place. The AI Act sets a clear line, and its enforcement regime leaves little room for improvisation. As I often warn: everyone wants AI, but few are ready to manage it as a strategic asset.

We all want to embrace AI, but we fail to prioritize control: we see it as a business asset.

Developers must use these environments to prevent models from generating harmful or misleading content. To ethically evaluate AI models and conduct security testing, it’s critical to establish control mechanisms that mitigate risks linked to technologies like deepfakes, disinformation campaigns, and fraud.

Trust in AI systems relies on transparency and explainability, enabling us to understand and justify their decisions. These environments help organizations test and document how models work, ensuring they operate fairly and transparently.

—For example, AI-based credit scoring models must be explainable to avoid discrimination and protect fundamental rights. Developers fine-tune them before they reach end users.

■ As AI evolves, sandboxes will be fundamental in building a future centered on security, privacy, ethics, governance, and compliance. Organizations that invest in them today will be better prepared to face challenges and complexity in an ever-changing landscape.

Cyber Security

Cyber Risk Quantification

May 28, 2025