AI trust gap: Balancing innovation and security in IT

The AI trust gap refers to the disconnect between AI’s capabilities and the concerns that come with its adoption.

---

Artificial intelligence (AI) has become the cornerstone of modern IT operations, offering groundbreaking advancements that streamline processes, enhance security and revolutionise user experiences. According to a 2024 Capgemini report, 97% of organisations reported experiencing at least one security breach related to generative AI in the past year. While AI promises unprecedented opportunities, it also comes with critical challenges that cannot be ignored.

The rapid adoption of AI in IT departments has sparked debate on the balance between driving innovation and maintaining trust in these technologies. The AI trust gap is emerging as a central issue – how can businesses harness the full potential of AI without jeopardising security, ethics or compliance?

The AI trust gap refers to the disconnect between AI’s capabilities and the concerns that come with its adoption. As organisations increasingly rely on AI to improve operational efficiency, security and service delivery, they must confront a growing set of risks. These include concerns about cyber threats, the ethical implications of AI decision-making and the overall reliability of AI-driven processes.

1. Security and cyber risks

AI-driven tools have undeniably strengthened cyber security measures by detecting threats faster, automating responses and improving predictive capabilities. However, these tools are not impervious to attack. In fact, as AI becomes more integrated into security systems, the very same technology that strengthens defences is also vulnerable to exploitation. Adversarial AI – where cyber criminals manipulate AI models to bypass security systems – has become an increasing threat. A 2025 survey revealed that 80% of bank cyber security executives feel they cannot keep pace with AI-powered cyber criminals, underscoring the urgency of this issue.

Moreover, AI-generated deepfakes and sophisticated phishing schemes have escalated, allowing malicious actors to exploit these technologies to create highly convincing scams that bypass traditional security defences. The rise of AI-facilitated cyber crime poses a stark reminder that while AI can secure systems, it can also be weaponised against organisations.

Case study: In 2023, cyber criminals used AI-powered techniques to bypass e-mail security filters, leading to large-scale phishing breaches that cost millions in damages and significantly damaged reputations.

2. AI bias and ethical considerations

AI models rely heavily on the data they are trained on, and this data is often imperfect. Biased or incomplete data can result in AI-driven decisions that perpetuate harmful stereotypes or unfair treatment. In critical sectors like finance, healthcare and hiring, this becomes an ethical minefield. A report by the World Economic Forum highlighted that AI systems in recruitment have shown tendencies to discriminate against certain demographic groups, often unintentionally.

In regulated industries, such as healthcare and banking, AI errors or biases can lead to serious compliance violations, legal challenges and significant financial repercussions. As AI technologies permeate decision-making processes, the responsibility falls on IT leaders to ensure that AI is used ethically and does not perpetuate discrimination or inequity.

3. Over-reliance on AI and automation

The automation capabilities of AI are transformative, but they also introduce a serious risk – over-reliance on AI systems without sufficient human oversight. AI may be capable of handling routine tasks, but it is not infallible. For example, AI-driven security systems could misidentify a threat, resulting in false positives or false negatives that either flood IT teams with irrelevant alerts or fail to flag a legitimate security issue.

In addition, automating IT service management (ITSM) and other critical business processes without adequate monitoring can lead to operational errors that snowball into larger problems. A complete delegation of decision-making to AI can reduce human judgment and intervention, which could have devastating consequences when the AI makes the wrong call.

Example: Automated security patching systems powered by AI could mistakenly shut down critical systems, impacting business continuity. Similarly, AI-driven ITSM systems might misroute critical service requests, leading to operational chaos.

To ensure that AI technologies fulfil their promise while minimising risks, organisations must adopt strategies to close the AI trust gap. Here’s how businesses can safely navigate the evolving AI landscape:

1. Enhance transparency and explainability: AI systems should be designed to offer transparency. Decision-making processes need to be explainable, and organisations should ensure that AI-generated outcomes are understandable by human teams. This fosters trust and accountability while reducing the "black box" stigma often associated with AI systems.
2. Maintain human-in-the-loop systems: While AI can augment human efforts, it should not replace them entirely. Critical decisions – especially those with ethical, financial or legal implications – should involve human oversight to ensure that AI aligns with organisational values and goals.
3. Establish robust governance and compliance frameworks: Implementing strong governance frameworks for AI is essential. Organisations must develop and enforce policies that ensure AI systems are not biased, that they comply with industry regulations and that they operate within ethical boundaries.
4. Invest in proactive AI security measures: While AI can assist in defending against cyber threats, organisations must also deploy measures to protect AI systems themselves. Proactive security measures such as adversarial AI detection and security monitoring can help identify vulnerabilities in AI models before they are exploited.

The integration of AI within IT is accelerating, and businesses will continue to see innovations that reshape how they operate. Predictive cyber security measures, AI-automated ITSM solutions and real-time compliance tools are just the beginning. As these tools evolve, addressing the AI trust gap will be critical to avoid the potential pitfalls of misuse, security breaches and ethical violations.

However, it’s important to remember that AI is a tool – powerful, but still a tool. When implemented correctly, it can help IT teams deliver faster, more accurate results, reduce operational inefficiencies and enhance user experience. The key to realising these benefits lies in how we manage and secure AI systems.

Think Tank Software Solutions is committed to guiding enterprises through secure AI adoption, balancing innovation with robust security measures. By providing AI solutions that prioritise transparency, ethics and human oversight, Think Tank Software Solutions helps its clients stay ahead in the rapidly evolving digital landscape.

Want to see AI in action? Explore the company's AI solutions and discover how Think Tank Software Solutions can help you navigate the evolving AI landscape securely.