Building Trust in AI: A Guide for Secure and Confident Adoption

November 21, 2025

As organizations rapidly embrace AI technologies, trust emerges as the central challenge to successful adoption. While technical risks are significant, a more tangible and sometimes larger concern is the human element: if teams aren’t confident in AI’s reliability, both business decisions and the company’s reputation could be at stake. Although having an established security program and principles continues to be relevant, AI brings new layers of complexity to risk and trust.

So, What Do We Mean by “AI”?

AI is not monolithic. Rather, it encompasses a range of approaches such as AI-enabled applications which include generative AI, large language models (LLMs), machine learning as well as agentic AI, and endpoint chat solutions. Each of the types mentioned bring distinct risks and trust considerations. Many organizational leaders and teams find it challenging to clearly define what “AI” means within their specific context and for their unique use cases. As with many objectives, different needs require different tools.

To build trust and implement effective security measures, start by clarifying your objectives, environment, and constraints. For example:

• What does your AI security strategy need to cover?
• Which types of AI technologies are you currently deploying or planning to secure?
• What use cases have support and funding right now?

Human Trust: The Real Challenge

The breach of trust in AI can be more about “hacking” humans rather than compromising systems. One possibility is when employees trust AI outputs without fully understanding risks such as data sources leading to accuracy issues, hallucinations, bias or model drift, which can significantly impact the reliability of results.

When underlying training or inference data is flawed, it leads decision-makers to make poor choices, underscoring the importance of building competence in the use of AI tools. Security leaders must ensure AI outputs are accurate and dependable. Recognizing human trust is as critical as technical safeguards in successful AI adoptions is key to building organizational trust.

With these challenges in mind, let’s explore practical steps organizations can take to build this trust.

Building Competence and Awareness with AI Literacy

To build organizational trust in AI, your organization should invest time and resources into upskilling the teams, so they understand the risks, limitations and responsible use of AI technologies (the “why”). Teams who are using AI capabilities should be encouraged to provide feedback and recognize contributions so these efforts are seen as meaningful, not just routine.

Foundational AI literacy should be established through comprehensive training programs, executive briefings and ongoing educational initiatives. These efforts must emphasize teaching employees to critically evaluate all AI outputs and recognize situations when questioning results is necessary. 

Launch Optiv’s free AI literacy and awareness course here.

Safeguarding Integrity and Privacy

As teams become more knowledgeable and discerning in their use of AI, it is equally important to protect the integrity of these systems and the data they rely on. 

Data poisoning, prompt injection and similar attacks pose significant risks to both technical and human trust in AI systems. To counter these threats, organizations should implement robust data governance, enforce strong privacy controls and adhere to established compliance frameworks such as NIST AI RMF, OWASP Top 10 Risks, OWASP MAESTRO, MITRE ATLAS and ISO42001.

Embracing a security-by-design approach ensures that AI solutions are built with privacy and integrity at their core, reinforcing organizational trust in AI capabilities.

Proactive Risk Management with Threat Modeling

To effectively safeguard AI systems, organizations should apply foundational security program fundamentals such as threat modeling, continuous testing and ongoing monitoring. This approach should address new and unique AI attack vectors including adversarial attacks, model theft and vulnerabilities within the AI supply chain.

Leveraging proven security frameworks and strategically adapting them to address AI-specific risks enables organizations to establish a robust and resilient security posture that meets the challenges of an evolving AI landscape while including business and IT professionals in the process. 

Embedding Security Throughout the Lifecycle

Security should be integrated into every phase of the AI SDLC lifecycle, from initial strategy through deployment. This means thoroughly vetting AI tools, rigorously testing, implementing guardrails and operationalizing secure AI programs to ensure ongoing protection. 

Rather than pursuing every "shiny new” technology, organizations should prioritize building resilient and trustworthy programs grounded in governance to foster sustained confidence in their AI solutions.

Managing Exclusive AI Incidents: Hallucinations, Bias, Drift

It is critical for organizations to recognize, prepare for, and build a plan to address incidents unique to AI with the potential to erode trust, such as:

• Hallucinations: where AI generates false or fabricated information that appears credible
• Bias: when AI systems produce unfair or skewed outputs based on biased training data, flawed algorithms or human assumptions
• Model drift: the gradual degradation of model performance over time as data patterns or relationships between inputs and outputs change

One way to mitigate these risks is by building processes for transparency, explainability and incident response. Additionally, to maintain stakeholder confidence, organizations must communicate openly about these risks and the strategies used to mitigate them.

Final Thoughts

Building organizational trust in AI is a journey, one that depends on establishing clarity, developing competence and adhering to security fundamentals. It’s not just about securing AI; it’s about helping people feel confident using it.

To support this goal, organizations should invest in AI literacy, implement robust governance and adopt a security-by-design approach to AI adoption. To learn more about building AI programs that foster organization trust, reach out to our experts.