Although they represent great potential to automate tasks, reduce costs, and increase productivity, Artificial Intelligence (AI) tools can also pose risks. Especially in regulated markets, this technology can affect governance, compliance, and even company reputation.
With AI, the ability to process information, predict trends, and automate complex decisions has become a non-negotiable competitive differentiator. However, this race for innovation brings with it a new and complex layer of responsibility.
The origin of these problems often lies in the speed of technological adoption in corporations, outpacing the maturity of AI governance structures designed to control it.
In other words, organizations around the world are integrating algorithms into critical processes, often without proper shielding against failures, biases, or security vulnerabilities.
According to McKinsey, the adoption of AI in companies jumped to more than 72% globally in 2024. However, the same study reveals that only 18% of these organizations have established a formal board or committee dedicated to AI governance in the past year.
At the same time, a survey by the consulting firm Deloitte shows that regulatory compliance has become the main barrier to the adoption of Artificial Intelligence.
In this new scenario, executives and managers need to understand that using AI without “safety rails” (that is, without standardized processes and robust governance) transforms technology from a strategic asset to a regulatory danger.
Read on and find out how to reshape Artificial Intelligence risk management into a sustainability and compliance pillar for your company.
What are the risks of AI for regulated markets?
The adoption of Artificial Intelligence in the corporate environment is no longer a matter of “if”, but of “how” and “at what speed”. However, the speed of innovation has generated a trail of vulnerabilities.
Stanford University points out that the number of ethical incidents and reported security failures involving AI jumped by 56.4% in 2024 compared to the previous year. And this number tends to continue rising as the unstructured adoption of technology proportionally amplifies the scale of errors.
This is because the operation of Artificial Intelligence is fundamentally different from other tools. Unlike traditional software, which operates under deterministic rules (if X, then Y), AI models—especially generative and deep learning ones—introduce probabilistic variables that challenge conventional methods of control.
Therefore, ignoring the nuances of these new risk vectors is the greatest danger for a manager focused on compliance and governance. Below, we detail the four critical risks that require immediate corporate governance attention.
1. Algorithmic biases and reputational risk
AI learns from historical data. Therefore, if this data contains structural biases and/or biases (whether in hiring processes, credit granting, or risk analysis), it can lead the algorithm to make wrong decisions. In these cases, the risk is not only ethical, but also legal.
That’s why, according to Stanford, global trust that AI systems are unbiased and free from discrimination is on the decline: only 47% of people believe AI companies protect their data and act ethically.
In the case of regulated companies, this transcends the ethical debate and becomes a legal and image liability. Automated discriminatory decisions can result in severe penalties and irreparable damage to brand trust in the market.
To minimize this risk, it is essential to have Responsible Artificial Intelligence (RAI) algorithm models that comply with standards such as ISO 27001 and ISO 42001.
In this way, the technology includes concepts such as:
- privacy,
- data governance,
- impartiality,
- transparency,
- explainability.
Thus, your company is less likely to end up using AI under some prejudiced bias.
2. Information Security and the “Shadow AI” Phenomenon
The democratization of generative Artificial Intelligence tools has created an invisible challenge: Shadow AI. Employees, in the search for productivity, may inadvertently feed public models with confidential data, business strategies, or information protected by industrial secrecy.
Without a clear policy and monitoring tools, the organization runs the risk of expropriation of intellectual property and violation of data protection legislation. After all, once the information is entered into these platforms, it often becomes part of the external model’s training domain.
This represents a double-edged sword for corporations operating in highly regulated environments:
- at the same time, according to a report by the consulting firm IBM, the global average cost of a data breach reached US$ 4.4 million…
- …using AI for security can save companies that have built-in governance up to $1.9 million.
To ensure that your company can reap the rewards of Artificial Intelligence without running the security risks, adopt measures such as:
- create an AI governance policy and establish an AI committee (involving areas such as CISO, Chief Data Officer, Legal, and Business) for approvals, risks, and metrics involving this technology.
- institute specific prompt injection & abuse protections, such as prompt sanitization, segregated windows context use, and guardrails and response filters that detect exfiltration requests.
- organize training and train employees on shadow AI risks (defining what is allowed and how to report unauthorized tools, for example).
- perform AI-generated phishing simulations to train employees on how to use the same technology in favor of the company’s defense.
3. Hallucinations and failures in data quality
The eloquence of Artificial Intelligence should not be confused with the ability to speak the truth and never make mistakes. Language models are susceptible to “hallucinations”; That is, they can generate false information presented with complete confidence. That’s why inaccuracy is the biggest concern for 76% of consumers, according to Forbes.
In environments where accuracy is mandatory, blindly relying on unverified insights can lead to serious operational errors. For example, in sectors such as financial services https://www.softexpert.com/pt-BR/industrias/servicos-financeiros/and pharmaceuticals, a documentary “hallucination” can result in non-compliance with regulatory standards, generating heavy fines.
So, remember that the integrity of the output data is just as crucial as that of the input data. Without validation, AI becomes a strategic noise generator.
4. The lack of traceability
Perhaps the most critical point for compliance is traceability. Many advanced AI models operate like “black boxes”: we know what went in and what went out, but the internal decision-making process is unfathomable. As the Foundation Model Transparency Index shows, the average score for AI companies was 40% in 2025 (down from 58% in 2024).
In addition, for audits and regulatory bodies, knowing only the result of a process or decision-making is not enough. It is necessary to prove the path to this result — and track each step.
The inability to track and justify how an automated decision was made creates an unacceptable audit gap for ISO certifications and strict industry regulations.
How to create internal controls to mitigate AI risks?
Effective mitigation of these dangers requires that Artificial Intelligence tools stop being treated as an “isolated IT project” and start to be managed from the perspective of Integrated Management Systems. The recent publication of the ISO/IEC 42001:2023 standard — the first international standard for AI management systems — marked the beginning of a new era of maturity and corporate requirements and brings structure to build the governance of this technology.
The first step is to understand that implementing internal controls is not just bureaucracy. This action is the only way to transform algorithmic randomness into business predictability.
Here’s how to do it in your company.
1. Stipulate a protection structure
AI governance doesn’t have to (and shouldn’t) be invented from scratch. There are robust frameworks that already offer a roadmap to compliance. The main ones are:
- ISO/IEC 42001: This standard focuses on the organizational process, establishing requirements to assess impacts, address risks, and monitor the ongoing performance of AI systems.
- AI TRiSM (Trust, Risk, and Security Management): Gartner points to this concept as a set of practices for model and application transparency, content anomaly detection, AI data protection, model and application monitoring and operations, resistance to adversarial attacks, and AI application security.
2. Adopt supervision as a quality filter
Artificial Intelligence should operate as a copilot, not as a stand-alone replacement in critical processes. The concept of Human-in-the-loop always inserts an expert’s validation before the AI decision is executed.
Therefore, identify all the vital processes and decisions of your operation that use an AI tool and make sure that these steps will have the supervision of a qualified human to point out corrections, changes, and verifications of what was produced by the technology.
3. Standardize the lifecycle of AI tools
Just as manufacturing has quality-controlled assembly lines, AI requires ModelOps. This ensures that the model is not just “launched” but continuously audited for its performance. This way, you avoid data drift, when the AI loses accuracy over time.
Since technology vendors (such as OpenAI, Google, DeepSeek, and others) are not always fully transparent, it is up to your company to create internal testing controls. These control points act in the validation of results before they impact the end customer.
4. Manage documentation and ensure traceability
In Compliance audits, the absence of registration is synonymous with non-conformities. To avoid this situation, it is essential to create internal controls that ensure that each automated decision is traceable to a specific dataset and an approved version of the model.
If your company does not adopt these measures, it may not only suffer losses in quality in the operation but also face sanctions from regulatory bodies. The EU AI Act, a global regulatory benchmark in the area, stipulates fines that can reach 7% of global turnover (or 35 million euros) for companies that adopt AI practices considered prohibited.
How do integrated systems mitigate the risks of Artificial Intelligence?
For the executive of a regulated sector, excellence lies not only in the intention to comply, but in the evidential capacity of it. In this sense, AI risk management fails when it is fragmented into isolated spreadsheets or niche tools.
Real mitigation occurs only when governance is orchestrated by Integrated Management Systems, such as a GRC. Governance automation is the only scalable answer to the complexity of new AI models.
Centralization with a “single source of truth”
Artificial Intelligence is only as reliable as the data that powers it. The dispersion of information is the breeding ground for inconsistencies and “hallucinations.” Integrated systems (such as SoftExpert Suite) ensure that models consume clean, approved, and unique data, eliminating information silos.
Approval and traceability workflows
To combat “Shadow AI“, an integrated system imposes automated quality barriers. With this type of solution, no model goes into production without going through a workflow that requires evidence of bias testing and approval from the compliance department.
Continuous monitoring via AI TRiSM
The risks of AI are dynamic, so a model that is safe today may deviate tomorrow. Integrated management allows you to connect technical performance indicators from AI directly to corporate risk KPIs (ERM), triggering automatic alerts to those responsible in case of anomalies.
Conclusion
Artificial Intelligence undeniably represents the greatest productivity lever of this decade. However, for organizations that sustain the global economy, such as energy, finance, healthcare, and manufacturing, unchecked innovation is an invitation to collapse.
The good news is that you are now able to identify the risks of AI with its biases, hallucinations, and opacity. In addition, it is important to remember that these are not just technical problems, but governance challenges that require a structural response.
The adoption of frameworks such as ISO 42001, combined with human supervision (Human-in-the-loop) and supported by robust management platforms, forms the tripod of “Responsible AI”.
In this new scenario, the role of the modern leader is not to stop progress, but to build safe tracks through which it must pass. Technology needs to accelerate your business, but it’s built-in governance that ensures it stays on the right road, protected from regulatory liabilities and reputational damage.
Looking for more efficiency and compliance in your operations? Our experts can help identify the best strategies for your company with SoftExpert solutions. Contact us today!
Frequently asked questions about risks of AI
Unlike traditional software that operates with fixed rules, AI works with probabilistic variables that can generate unforeseen results. Accelerated adoption without mature governance can lead to security breaches, compliance breaches, and reputational damage, especially in sensitive industries like finance, healthcare, and energy.
Shadow AI occurs when employees use generative AI tools not authorized by the company to increase productivity. The primary risk is feeding public models with sensitive data or trade secrets, which can result in violation of data protection laws and loss of intellectual property.
Hallucinations happen when AI models generate false or inaccurate information but present it with complete confidence. According to Forbes data, inaccuracy is the biggest concern of 76% of consumers. In regulated industries, relying on this information without verification can lead to serious operational errors and hefty fines.
Organizations can utilize robust frameworks to mitigate risks, such as:
– ISO/IEC 42001:2023: The first international standard focused specifically on AI management systems.
– AI TRiSM (Gartner): A set of practices focused on trust, risk, and security management.
– ISO 27001: Focused on information security management.
It is the practice of maintaining human oversight in critical processes carried out by AI. It means that technology acts as a “co-pilot”, but vital decisions must pass the validation of a qualified human expert before they can be executed, ensuring a filter of ethics and quality.
Many AI models work like “black boxes,” where it is not known how the algorithm came to a conclusion. For auditors and regulatory bodies, it is necessary to prove the path of the decision. The inability to justify an automated decision creates gaps that prevent obtaining ISO certifications and complying with industry rules.
Regulations such as the EU AI Act (a global benchmark in the field) stipulate severe fines for prohibited practices or lack of compliance, which can reach 7% of the company’s global turnover or 35 million euros.
Integrated systems automate governance by:
– Centralize data: Creating a “single source of truth” that avoids inconsistent information.
– Create Approval Workflows: Preventing models from going into production without bias testing – and compliance approval.
– Continuous monitoring: Connecting AI’s technical performance to enterprise risk (ERM) KPIs to trigger automatic alerts in case of anomalies.
