The adoption of Artificial Intelligence (AI) in business has entered a new era filled with both opportunity and complexity. AI has quickly transformed from an emerging technology into a key part of business operations. Recent surveys show that 72% of organisations now use AI in their enterprise functions because it increases productivity and improves decision-making. However, this rapid growth brings emerging challenges that need AI-specific trust and safety frameworks. These frameworks are essential for protecting reputation and meeting legal requirements, while also helping to create a resilient and sustainable approach to technology.
understanding the risks posed by AI
AI systems are powerful but not flawless. Generative AI can create misinformation, hallucinate facts, and introduce bias, which affects trust and user loyalty. In a 2024 survey, over 40% of respondents identified explainability in AI models as a significant risk in adoption. Additionally, as deepfakes and impersonation attacks rise, particularly in sectors like finance and healthcare, AI trust and safety is becoming a major concern for leaders across all industries.
Other key risks include:
- Model misalignment: AI models may stray from their intended guidelines based on human values, leading to unethical use.
- Data privacy breaches: Protecting privacy is essential for building trust. Personal or sensitive data can be compromised if it is used to train AI models without proper oversight and safeguards in place.
- Algorithmic bias: Training models on incomplete or unbalanced data can amplify bias.
- Workforce disruption: AI-driven automation can disrupt existing job roles. Businesses must focus on training and responsible deployment to prevent job loss.
- Lack of transparency: The working of many AI models is opaque, complex and challenging to understand, which raises ethical concerns.
Effectively handling these challenges requires a strong and proactive AI risk management strategy that combines technology with social and ethical factors.
why AI-specific trust and safety are business essentials
Ignoring AI-specific trust and safety issues is no longer acceptable. Industry leaders now understand the strong connection between trust, regulatory compliance, and long-term value.
- Safeguarding brand reputation: Mishandling AI-generated content or experiencing a related breach can quickly erode public trust. However, prioritising AI trust and safety fosters customer loyalty and confidence.
- Adapting to legal and regulatory changes: The regulatory landscape is evolving rapidly. In 2024, over 50 new rules or laws focused on AI risk management and safety were introduced worldwide. Non-compliance can lead to fines, sanctions, and restrictions on business activities.
- Maintaining operational efficiency and ESG commitments: By managing AI risks effectively, businesses can identify issues before they escalate, saving time and money while supporting broader environmental, social, and governance goals.
how to keep AI safe: Principles and strategies
To fully harness the potential of AI while managing its risks, businesses must follow key principles of ethical AI development. This means designing systems that support fairness, accountability, and human rights, protecting sensitive data with rigorous privacy and security measures, and reducing bias by training with diverse datasets and inclusive design principles from the start.
Based on these principles, businesses can employ several practical strategies such as:
- Safety by design: Conduct risk assessments and ethical reviews from the outset and continue them throughout the AI development process.
- Continuous model refinement: Regularly update algorithms and test datasets to identify and fix flaws.
- Human intervention in critical decisions: Introduce human involvement in high-stakes AI applications, particularly in fields such as finance and healthcare.
- Alignment with regulatory standards: Monitor international AI regulations and adjust practices accordingly to ensure compliance.
- Collaborative effort across industries: Work with experts, researchers, and advisory groups to shape policies and best practices.
- Governance and transparency: Implement strong AI governance frameworks for full accountability and transparency.
building a trustworthy AI future
To establish credibility now and in the future, AI solutions must include responsible and sustainable practices. This involves creating fair and transparent systems, using inclusive and diverse data, protecting privacy, and selecting energy-efficient infrastructure. By doing this, companies can meet both current risks and future expectations. Responsible AI enhances trust, ensures compliance, and supports business resilience.
how can Infosys BPM help with AI trust and safety
Infosys BPM offers comprehensive services, spanning consulting and transformation, to managed operations, all powered by AI and generative AI. Our expert solutions enable proactive threat detection, fraud prevention, and regulatory compliance, while enhancing brand reputation and user trust. By combining deep industry knowledge with modern digital frameworks, we help businesses streamline processes and boost performance.
Frequently asked questions
General cybersecurity policies address infrastructure and access control — they do not govern model behaviour, output accuracy, or algorithmic bias. AI-specific trust and safety frameworks address risks that are unique to AI systems: hallucinations presenting false outputs as fact, model misalignment from intended human values, and deepfake-enabled impersonation that bypasses identity controls. As 72% of organisations embed AI into enterprise functions, the absence of AI-specific frameworks means these risks operate without governance — directly exposing brand reputation and regulatory standing.
Algorithmic bias — produced when models train on incomplete or unbalanced datasets — generates systematically skewed outputs in credit decisions, hiring, fraud detection, and content moderation. In regulated sectors, biased AI outputs constitute discriminatory practice, triggering enforcement under GDPR, the EU AI Act, and emerging national AI regulations. With over 40% of AI adopters citing explainability as a significant risk, enterprises that cannot audit model outputs for bias face both legal liability and the erosion of user trust that is extremely difficult to recover.
Safety by design means embedding risk assessments, ethical reviews, bias audits, and human oversight checkpoints into the AI development process from the outset — not as post-deployment additions. For high-stakes functions such as loan approvals, medical triage, and fraud detection, this requires human intervention protocols for edge cases, continuous model refinement against updated datasets, and governance frameworks that assign clear accountability for AI outputs. Enterprises that retrofit safety onto deployed AI systems consistently face higher remediation costs and greater regulatory exposure than those that build it in from day one.
The surge to 50+ AI-focused regulations in 2024 means enterprises operating across jurisdictions face overlapping and sometimes conflicting compliance obligations — the EU AI Act, US executive orders, NIST AI RMF, and national frameworks all impose distinct requirements. Effective cross-jurisdictional compliance requires a unified AI governance framework with jurisdiction-specific overlays, continuous regulatory monitoring, and alignment of internal AI policies to the most stringent applicable standard. Enterprises that manage AI compliance jurisdiction by jurisdiction in silos create dangerous gaps and duplicated effort.
The cost of proactive AI trust and safety investment — risk assessments, governance frameworks, bias audits, and continuous model monitoring — is a fraction of post-incident remediation. A single AI-related breach, biased decision at scale, or deepfake-driven fraud event carries compounding costs: regulatory fines, legal liability, reputational damage, and customer attrition that can persist for years. Enterprises that treat AI trust and safety as a business enabler rather than a compliance cost also report stronger customer loyalty, faster regulatory approval for new AI deployments, and reduced operational disruption from model failures.
