Understanding and managing AI risks

AI is no longer a futuristic concept; it’s a foundational force transforming industries, economies, and society. However, its rapid acceleration has outpaced governance structures, introducing systemic risks that leaders can no longer afford to treat as technical side issues. From algorithmic bias and misinformation to privacy erosion and power concentration, the implications of unchecked AI are real and far-reaching.

As AI becomes more embedded in critical business and societal systems, organisations must go beyond compliance and adopt proactive governance throughout their entire AI lifecycle. This means embedding transparency, ethical principles, and continuous oversight into the design, deployment, and evolution of AI systems. Understanding these risks is foundational.

In the following sections, we’ll break down the most pressing categories of AI risks today: ethical, technical, systemic, and sector-specific, along with proven strategies to manage the risks. These insights have informed the development of our recently launched OneAdvancedAI solution and reflect our commitment to helping organisations like yours navigate and mitigate today’s AI problems.

1. Ethical and social risks

AI systems often reflect and reinforce existing social inequalities, with bias emerging as a critical and persistent challenge. When trained on historical or unbalanced datasets, models can produce discriminatory outcomes, such as unfair hiring processes, inequitable lending practices, and facial recognition failures that disproportionately affect marginalised communities.

Emerging tech like deepfakes and hyper-personalised content algorithms further threaten public trust. They can distort reality, amplify misinformation, and deepen polarisation by reinforcing echo chambers. And as AI begins to mediate human interaction in sectors like healthcare and education, there are growing concerns around the erosion of empathy, nuance, and meaningful connection.

Amanda Grant, Chief Product Officer for OneAdvanced: “Bias is baked into data and systems unless we deliberately work against it. That means sourcing diverse data, continuously testing for unintended impacts, and ensuring human oversight is central, not  optional. Especially as AI mediates sensitive areas like healthcare and hiring, maintaining empathy and nuance becomes critical to preserving trust and dignity.”

2. Technical and operational risks

Advanced AI systems, while powerful, often operate as "black boxes", making decisions that are difficult to interpret or audit. This lack of transparency can create serious technical and operational risks, particularly in regulated, high-stakes sectors like healthcare and finance. When an AI system’s output cannot be clearly explained or justified, it undermines trust, complicates regulatory compliance, and increases the risk of harm or unfair outcomes.

A widely reported case involving UnitedHealth Group’s nH Predict algorithm illustrates how these risks can manifest in practice. The tool was used to forecast the duration of post-acute care for patients, but it was alleged to consistently underestimate patient needs, leading to early discharges and denial of services. Physicians challenging the AI’s recommendations found their clinical input overruled, with no access to the model’s logic or decision-making.

Amanda Grant, Chief Product Officer, OneAdvanced: “Transparency is foundational to trust in AI. If professionals can’t understand or challenge a model’s output, the risk of harm multiplies. Explainability, traceability, and ongoing validation aren’t just nice to have; they are essential pillars to embed in AI development and deployment. This approach respects expert judgment and helps organisations meet both ethical and regulatory standards.”

3. Systemic and strategic risks

When development pathways, datasets, and infrastructure are concentrated in the hands of a few major technology companies and state actors like they are today, it can entrench specific worldviews, embed systemic bias at scale, and reduce the diversity of perspectives informing AI’s evolution. Furthermore, over-reliance on proprietary AI platforms may limit strategic autonomy, stifle local innovation, and create long-term vendor lock-in, challenges that are particularly acute for mission-driven sectors operating under tight budgets and regulatory mandates.

In practice, this can result in AI systems that do not adequately reflect the needs, values, or principles at hand. A pertinent example involves the UK government's AI toolkit, Humphrey, which is being rolled out across civil service departments in England and Wales. While it is enhancing efficiency analysing public consultations, critics have raised concerns about the pace of integration, potential biases in AI outputs, and the absence of comprehensive commercial agreements.

Amanda Grant, Chief Product Officer, OneAdvanced: “Relying on proprietary platforms without oversight risks solutions that don’t reflect local needs or values. Building a healthy AI ecosystem means fostering open innovation, ensuring diverse governance, and creating equitable access to tools and talent, so AI can serve fairly and sustainably.”

4. Security and misuse risks

As AI models become more powerful and accessible, malicious actors are finding new ways to exploit them, posing serious risks to both digital and physical infrastructure. A notable example occurred in 2022, when T-Mobile reported a breach affecting over 37 million customer records. The breach was allegedly facilitated by an AI-assisted API exploit, demonstrating how automated systems can be used to probe and penetrate exposed interfaces far faster than manual methods. This attack highlights the growing complexity of AI in an era where it can be used offensively as well as defensively.

Beyond cybersecurity, the dual-use nature of AI raises broader concerns. The same underlying technology that powers predictive maintenance systems for public housing or assists lawyers forecasting case outcomes, if misapplied, can be used for bioterrorism, autonomous weapons, or pervasive surveillance. These risks underscore the urgent need for safety-first design principles, ongoing threat modelling, and governance frameworks that can evolve alongside the technology.

Amanda Grant, Chief Product Officer, OneAdvanced: “AI’s power to accelerate attacks and uncover vulnerabilities is matched by its potential to safeguard and defend. This dual-use nature demands vigilance: layered security measures, ethical guardrails, and proactive ‘red teaming’ exercises to expose weaknesses before adversaries do. Embedding safety and resilience into AI design isn’t just precautionary—it’s critical for long-term adoption and trust.”

5. Sector-specific risks

Financial services

AI is revolutionising financial decision-making, from real-time risk assessment to personalised investment advice. Yet, the opaque nature of some models can obscure systemic risks, such as feedback loops in automated trading or biased credit scoring that silently exclude certain demographics, threatening both market stability and financial inclusion.

Read: How the finance sector is leading AI adoption in the UK.

Education

AI-driven personalised learning platforms offer unprecedented adaptability but risk reinforcing existing inequalities if not carefully calibrated. For instance, content recommendations based on narrow datasets can limit exposure to diverse perspectives, while automated proctoring tools may disproportionately flag students from underrepresented groups, undermining fairness.

Join our webinar: Smarter Spending, Stronger Institutions: Driving University Transformation with AI

Workplace and employment

While AI-powered automation is streamlining routine tasks, it also reshapes workforce dynamics by redefining skills demand and workplace relationships. The challenge lies in ensuring that automation augments human roles rather than replacing them outright, requiring organisations to design inclusive transition pathways that balance technological efficiency with social responsibility.

Discover how AI acts as an enabler, not a replacement for the workforce.

Managing AI risks strategically

The most successful organisations will find themselves integrating strategic governance, robust controls, and a culture of responsibility into their AI roadmap.

Governance and risk frameworks

Global standards such as the NIST AI Risk Management Framework and ISO/IEC 38507 provide structured approaches to AI governance. These frameworks emphasise transparency, fairness, and accountability across the full AI lifecycle and should serve as foundational references for organisations.

However, internally, companies and institutions must establish strong governance models that define roles, oversight responsibilities, and escalation paths. Dynamic risk assessments, aligned with regulatory and industry expectations, are essential to maintain trust and compliance.

Scenario planning and controls

Building resilience into AI systems means anticipating failure scenarios before they happen.

Key strategies include:

• Red teaming: Simulating adversarial attacks to stress-test systems.
• Audits: Regular reviews of model fairness, explainability, and performance.
• Ethical review boards: Multidisciplinary oversight of sensitive AI use cases.
• Fail-safe protocols: Emergency response mechanisms including human-in-the-loop oversight and automated shutdowns.

Culture, leadership, and collaboration

Sustainable AI adoption depends on an organisational culture rooted in accountability and continuous learning.

This means:

• Cross-functional collaboration between technical, compliance, and business teams.
• Transparent processes for data handling, model validation, and ethical review.
• Workforce development through regular AI literacy, ethics training, and change management.

Conclusion

AI holds extraordinary promise, but its risks are real, systemic, and evolving. Managing these risks is not just a technical challenge, but a leadership responsibility. By embedding strong governance, forward-thinking risk management, and an ethical foundation into every layer of AI adoption, organisations can unlock innovation while protecting what matters most.

Connect with us today to begin your journey toward smarter, safer innovation.

Frequently Asked Questions (FAQs)

How can AI risks be managed effectively?

Through robust governance frameworks, continuous oversight, and strong ethical standards. Organisations should also invest in AI literacy, scenario planning, and collaboration across departments.

What industries are most vulnerable to AI risks?

Finance, healthcare, education, and critical infrastructure are particularly exposed due to their reliance on high-stakes, data-driven decisions.

Are AI risks exaggerated or real?

AI risks are real and rapidly evolving. While some fears may be overstated, ignoring them leaves organisations vulnerable. Proactive management enables safe, strategic AI adoption.

Can regulation keep up with AI development?

Yes, with adaptive, collaborative policymaking. Governments, industry leaders, and researchers must work together to create flexible, principles-based regulations that evolve alongside technology.