Navigating the AI Trust, Risk, and Security Landscape

Artificial Intelligence (AI) is no longer confined to the realms of science fiction; it’s now an integral part of our business landscape, transforming the way we work and compete. With the advent of Generative AI, the floodgates of new AI initiatives have burst open, presenting incredible opportunities for innovation. However, the rapid expansion of AI also brings an urgent need to implement robust AI trust, risk, and security management capabilities. In this blog, we delve into why IT leaders must build guardrails to keep AI efforts under control while unlocking AI’s potential.

Seizing the Opportunities

As AI continues to advance, businesses and application stakeholders are increasingly aware of the unique challenges AI presents. They are also supportive of dedicated AI security and risk management projects. Yet, the drive to capitalize on AI’s benefits is strong, and business AI initiatives continue to surge forward. This results in a paradox within organizations, where there’s a growing need for robust control measures while businesses are unwilling to postpone their AI ventures.

Gartner Research highlights that limiting AI security controls to narrow vectors, such as large language model prompts, is insufficient. To adequately manage the risks associated with AI, we need AI trust, risk, and security management (AI TRiSM) that systematically addresses ModelOps, AI-specific security, and risk concerns1.

The good news is that organizations already embracing components of AI TRiSM technology find themselves moving AI models and applications into production more quickly and reliably.

Recommendations for IT Leaders

Gartner provides essential recommendations for IT leaders responsible for AI:

  1. Enhance Security and Risk Management: To make AI usage safer, AI application security and risk management programs must be enhanced and upscaled. These programs should cover the new AI attack and compromise surfaces that come with AI-based solutions.
  2. Stay Current with Controls: As controls for designing, training, and operating AI models and applications mature, IT leaders must keep pace. This includes integrating data protection measures into the design phase, monitoring models and applications in production for inaccuracies, drift, and unintended outcomes.
  3. Address Compliance: Get ahead of compliance issues by deploying AI TRiSM principles across business units to manage technical and organizational requirements. Business imperatives often outpace regulations and accepted industry standards, so being proactive is crucial.

Understanding the Shift in AI Risk

Traditional business solutions equipped with AI capabilities are not equivalent to AI applications. Treating AI in the same way exposes organizations to additional risks. The growing dependence on AI increases the risk and impact of underperforming AI models and applications, which can have serious consequences.

AI models and applications deployed in production must be protected by mechanisms that ensure acceptable use based on predetermined intentions. This not only safeguards the organization but also supports the sustained generation of value.

AI Trust, Risk, and Security Management

The democratization of AI, particularly the impact of technologies like ChatGPT, necessitates the urgent implementation of AI trust, risk, and security management (AI TRiSM). Without appropriate guardrails, AI models can quickly generate compounding negative effects, overshadowing their positive performance and gains.

Users guided by AI services with TRiSM capabilities benefit from more reliable information for decision-making. In contrast, those using AI services without these controls experience a significant increase in inaccurate information affecting their decisions.

AI TRiSM practices are essential to mitigate external AI risks that organizations can’t directly control. They enhance bias control in decisions and promote fairness in AI-driven applications.

Ensuring AI implementations are compliant, fair, and ethical requires constant monitoring of AI models and applications. Risk management tools are vital to identify and eliminate drift and uncontrolled biases.

The collaboration of IT and business leaders is key in managing AI trust, risk, and security. By establishing AI TRiSM programs, organizations can protect AI models, applications, and datasets from adversarial activities, ensuring the reliability of AI outputs and outcomes.

How InterVision Can Help

In this rapidly evolving landscape, InterVision’s AI practice offers indispensable support for building GenAI strategies and enhancing governance. With InterVision, businesses can assess their readiness, modernize their data, and seamlessly implement GenAI solutions. Through this partnership, organizations can confidently embrace the democratization of GenAI and remain at the forefront of this transformative trend.


AI continues to drive innovation and competitiveness in today’s business world. The democratization of AI opens doors to incredible possibilities while introducing new challenges. Implementing robust AI trust, risk, and security management, as recommended by Gartner, is crucial for securing the future of AI. Organizations must act now to build guardrails that keep AI initiatives under control and enable AI’s full potential. With the assistance of experts like InterVision, businesses can navigate this transformative trend with confidence, ensuring that AI remains a powerful force for good.


1. Gartner:Innovation Guide for Generative AI in Trust, Risk and Security Management. November 13, 2023.