By Sanjay Kala, Technology Practice Head, SAS Asia Pacific
AI has made its way into almost every aspect of business, from innovation to operations and even customer engagement. But with great power comes great unpredictability. A recent global study by IDC and SAS found that while organizations increasingly prioritize AI, only 40% are investing in governance, explainability, and ethical safeguards. Yet those who do are 60% more likely to double the ROI of their AI projects. This gap between ambition and accountability is where the real drama unfolds.
Why is this so important? In just the last two years, generative AI has already eclipsed the use of traditional AI. As the market rapidly advances to agentic AI, the impact on decision-making will be pervasive, often concealed behind automation and integration. For the good of society, businesses and employees: trust in AI is imperative. Tangible ROI increases with trust, and the study found that businesses that focus on governance, explainability, and ethical safeguards realize far greater value from AI initiatives.
Let’s explore an imaginary but plausible scenario to understand what happens when AI is deployed without guardrails and how the right governance framework can turn disruption into discipline.
Picture a retail enterprise with an AI-powered customer support chatbot. The chatbot, like most others, is trained on thousands of historical emails, customer FAQs, and internal documents. It does the job of engaging with customers and answering queries until it begins offering discount codes that never existed. It is easy to imagine what would have followed: unhappy customers sharing their woes on social media, with screenshots revealing internal product pricing. What was meant to be an AI-driven efficiency booster can easily become a corporate nightmare, a classic example of AI without security governance.
Where It All Went Wrong
The company’s intention was noble, meant to enhance customer experience through automation. But in the rush to innovate, they skipped the foundational step: embedding governance and security principles from day one. The AI model had been fine-tuned using internal communication logs that were never classified for sensitivity. No one set up access controls for prompt inputs or outputs. The governance team was never consulted on data lineage or model risk assessment. In essence, the organization built a smart system but gave it no boundaries. It’s like a ship without a captain in a sea without borders.
We tend to overly trust more humanlike technology. In spite of evidence that GenAI can be error-prone, organizations often have more faith in this technology than in other types of AI, including traditional machine learning. That misplaced confidence is exactly why guardrails, data classification, and human validation must be embedded from day one.
The Hidden Layer of Risk: What AI Really Brings
When organizations deploy AI, especially Generative AI, they open doors to a spectrum of new risks. These risks broadly fall into four buckets: Security risks, operational risks, privacy risks, and business risks.
Among them, security risks are the most immediate and least forgiving. Let’s look at what went wrong with our fictional scenario of a rogue bot, and what every technology leader needs to consider:
Hallucinations: AI hallucinations are outputs that appear factually correct but are false or misleading. In this scenario, the chatbot “invented” discount codes, a textbook hallucination. Such errors may seem harmless, but in regulated industries like finance or healthcare, they can have legal or ethical consequences.
Filter Bubbles (Echo Chambers): AI models optimize engagement by reinforcing existing patterns. Over time, this creates filter bubbles, where users see only content aligned with their preferences or biases. In business settings, this means your AI could reinforce flawed assumptions or narrow perspectives, leading to suboptimal decision-making.
Deepfakes: Manipulated audio or video generated by AI represents another alarming frontier. Imagine a deepfake video of your CEO announcing a policy change or merger before it’s public. Deepfakes blur the line between truth and fabrication, potentially triggering brand crises or market disruption.
Data Poisoning: AI models learn from data. If that data is intentionally or unintentionally corrupted, the output becomes compromised. A malicious insider or external actor could inject biased or harmful data into your training set, degrading model performance or steering outputs toward unsafe directions. The principle is simple: garbage in, garbage out.
Beyond GenAI: Risks in Traditional AI Systems
Not all AI risks come from generative systems. Predictive or analytical AI systems also carry unique vulnerabilities. There’s a growing myth of AI infallibility, which is the belief that algorithms are always objective and correct. Over-reliance on AI outputs can lead employees to skip critical validation steps, allowing flawed or biased outcomes to slip through unnoticed. These are also deliberate manipulations of AI models by attackers to cause malfunction. Four common forms include:
Model Inversion: extracting sensitive data from the model’s outputs.
Model Extraction: stealing proprietary model parameters or weights.
Model Poisoning: altering model parameters to change behavior.
Model Evasion: bypassing built-in safety filters to misclassify content.
In 2023, San Francisco streets witnessed self-driving cars frozen in place and surprisingly, this was not because of hackers, but activists who placed safety cones on their hoods. It was a striking reminder that intelligence without adaptability remains fragile, and that AI progress must evolve together with public trust and ongoing refinement.
Building AI Security Governance: The Five Pillars
The antidote to all these risks is Security Governance, a structured, proactive framework that integrates seamlessly with your Data Governance ecosystem.
Say hello to DRAMA (the good kind): five foundational pillars that every organization should adopt to ensure AI remains secure, scalable, and trustworthy.
1. Data Governance Integration: This begins with understanding the sensitivity of the data used to train and fine-tune AI models. Organizations must ensure that all data is properly classified, consented, anonymized, and traceable. Without clear lineage and regular audits of data flowing into AI pipelines, even well-intentioned models can become liabilities.
2. Risk Management Optimization: This means rigorously testing models for bias and robustness, simulating adversarial attacks to assess resilience, and maintaining detailed version histories and explainability logs. These practices help organizations stay ahead of threats and ensure that AI decisions remain transparent and defensible.
3. Access and Identity Control: Ensure that only the right people and systems can interact with AI models. Role-based access protocols, secure API usage, and prompt-level data controls are essential. Logging, monitoring, and approval workflows for sensitive operations add an extra layer of accountability and traceability.
4. Monitoring and Incident Response: You need a safety net for AI in production. Continuous tracking of model outputs helps detect hallucinations, data leaks, or misuse in real time. Organizations must be prepared to respond swiftly with rollback mechanisms and retraining processes to minimize impact and restore trust.
5. Advisory Board Establishment: Bring cross-functional oversight into the governance equation. Putting together an AI Governance Council with representatives from security, compliance, data management, and business units ensures that every deployment undergoes thorough risk assessment before going live. This collaborative approach embeds responsibility into innovation.
Governance Isn’t Bureaucracy — It’s Your AI’s Immune System
Governance protects innovation from self-destruction. AI will continue to transform how organizations operate, but trust remains the ultimate KPI. Security governance is not an optional add-on but the foundation for responsible, scalable, and sustainable AI adoption.
Because in the world of AI, a model without governance is like a city without laws: powerful but perilous, and so, we need to build the ‘DRAMA’ to turn disruption into discipline.