Trust and security in Artificial Intelligence (AI) are fundamentally different frameworks that often produce contrasting design decisions, evaluation methods, and architectural choices.
Security primarily focuses on preventing direct harm from an AI system through internal technical controls and safeguards. It asks: Does the model make accurate predictions? Does it resist adversarial attacks? Are data and infrastructure secure from external threats? Security is largely an intrinsic property, which can be measured through controlled test environments and technical benchmarks.
In contrast, trust is a more inclusive concept that encompasses fairness, explainability, privacy, robustness, governance, and social impact across the board.
A system may be technically secure yet be highly unreliable, and conversely, internal security verification provides minimal assurance to external stakeholders.
the great ideological divide
This distinction matters because satisfying internal benchmarks and technical security standards is necessary but insufficient to build trust. Organizations can create systems that pass all security tests, achieve high accuracy on the data held and implement robust infrastructure, yet deploy tools that may systematically harm certain populations or make decisions that users cannot understand or verify.
The same dimensions of AI design – explainability, performance, fairness, privacy, and robustness – are understood and implemented completely differently depending on whether the overarching goal is security or trust.
1. Explainability (debugging vs understanding)
In the security paradigm, interpretability is primarily an engineering tool; Developers generate explanations for model predictions to debug failures, identify weak points in the decision boundary, and diagnose where the system breaks down.
Whereas, in the belief model, explanations serve a fundamentally different purpose. They enable end-users and domain experts to understand decisions in their own conceptual language and verify the logic against their expertise.
For example, a medical diagnostic model designed for security can explain itself to engineers through feature importance scores and attention maps.
The same model designed for confidence will explain to physicians using clinical language, highlighting which symptoms and test results contributed to the diagnosis that physicians can verify against their medical knowledge. These are not identical interpretations as they reflect different audiences and different purposes.
2. Performance and Benchmarks (Internal Testing vs. Verifiable Proof)
Security treats performance as something that is intrinsically valid. Does the model achieve 95% accuracy on the test set? Does it withstand adversarial attacks in a laboratory setting?
These are engineering questions answered through controlled benchmarking.
However, trust requires external validation and verifiable proof.
To make AI trustworthy, it demands third-party verification: independent auditors, preferably with access to proprietary models through privacy-preserving mechanisms such as trusted execution environments, that certify performance and bias characteristics.
The EU AI Act and the emerging regulatory framework clearly require this distinction.
3. Fairness and Bias (Discrimination vs. Equality)
Protection-oriented fairness focuses on preventing the system from explicitly using protected characteristics (race, gender, age) in decision making.
This leads to designs that exclude sensitive characteristics or enforce “fairness through anonymity” – the assumption that removing demographic data will prevent discriminatory outcomes.
This approach repeatedly fails because algorithms may detect proxy variables that encode protected characteristics – a gender-blind credit scoring system could discriminate against women if the model finds that certain phone types or app choices are related to female identity, even if gender is excluded from the input.
The algorithm’s optimization objective – predicting loan default – is agnostic to the developer’s intentions; If the training data contains historical gender bias, the model will find shortcuts to that pattern. Trust-oriented fairness addresses this by continuously auditing across demographic groups, testing for both direct and indirect discrimination, and confirming that performance generalizes equally.
4. Privacy (Data Security vs. User Control)
Security-oriented privacy emphasizes protecting data from unauthorized access: encryption, access controls, secure storage, and preventing data breaches. The focus is on privacy – ensuring that no one can access or misuse personal information.
Trust-oriented privacy centers on user agency and informed consent: Do users know what data is being collected and why? How is it used? Can they access, correct or delete their information? Do they have any meaningful choice about what is stored?
Privacy by design in the trust paradigm means minimizing data collection, obtaining explicit consent for each use, implementing transparent data-handling practices, and respecting user rights regardless of technical feasibility.
5. strength
Security-oriented robustness focuses on adversarial attacks. For example, a vision system robust against adversarial attacks can withstand small perturbations designed by attackers to lead to misclassification.
Confidence-oriented robustness asks whether the system generalizes reliably across real-world situations and diverse populations.
A diagnostic AI system may be trained robustly against adversarial examples, but it silently fails when deployed in a different hospital with different patient demographics, equipment, or clinical workflows.
Recent research clearly shows this difference: models adversarially trained to be robust against one type of perturbation (for example, specific image distortions) often become less robust against other, unexpected perturbations.
6. Ethics and Accountability
Security defines ethics as compliance: Does the system meet ethical guidelines? Have we done a risk assessment? Do we document bias mitigation strategies? These are important but mainly process-oriented.
Trust defines ethics as relational integrity: Is the system really fair to all the groups it affects? Do stakeholders feel that their values are respected? Is responsibility clear when the system causes harm? Does the governance reflect the interests of affected parties, not just developers?
This requires ongoing engagement with diverse stakeholders, monitoring of real-world impacts, and adapting governance as ethical issues arise.
Algorithms designed for hiring may claim ethical compliance by documenting bias-mitigation techniques and internal fairness audits.
It earns trust by demonstrating that hiring outcomes are consistent across demographics, that candidates understand how they are evaluated, that recruiters maintain meaningful human judgments, and that the company adjusts the system based on feedback about fairness concerns.
Designing AI for security focuses on preventing exploitation and system failure through rigorous internal testing, adversarial robustness and threat modeling, strong security controls (access management, encryption, monitoring), disciplined technical documentation of specifications and risks, and penetration testing.
On the other hand, designing for trust moves forward by ensuring that the system credibly meets stakeholders’ needs in context, which requires involving stakeholders throughout the lifecycle, building transparency into the process (auditability, model provenance and training-data documentation), providing friendly interpretation to multiple audiences (end-users, domain experts, regulators), externally validating performance through third-party review, maintaining meaningful human oversight rather than box-ticking, supporting diverse populations. And situations include monitoring real-world outcomes and maintaining governance. Feedback loops that adapt when there is evidence of ethical or performance issues.
investment required for trust
Obviously construction tools for trust often come with a price. Yet these investments are essential when trust matters – in health care, finance, criminal justice, hiring, lending and any area where AI decisions directly impact human well-being.
Current regulatory frameworks are increasingly recognizing this gap.
The EU AI Act classifies certain applications as “high risk” and calls for extensive documentation, external auditing and transparency for those systems because security alone is insufficient.
NIST’s AI risk management framework explicitly addresses multiple dimensions of reliability, not just security or accuracy.
Emerging standards, such as the ISO/IEC framework, are developing criteria for verifiable reliability based on objectivity, interpretability, and robustness.
As AI becomes increasingly involved in decision making that affects millions of people, the difference has become fundamental, not just academic. Organizations that recognize and invest in differences are more likely to create systems worthy of genuine trust.
Security is an essential foundation. There is no doubt about it. But trust is a broader social and relational asset. After all, no one wants to trust a system they know is insecure or unreliable.
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(Views expressed are personal)
About the author: Professor Balaraman Raveendran heads the Department of Data Science and Artificial Intelligence (DSAI), Wadhwani School of Data Science and Artificial Intelligence (WSAI), Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI) and Center for Responsible AI (CeRAI) at IIT Madras. His research interests focus on learning from and through interactions and span the areas of geometric deep learning and reinforcement learning. Additionally, he is associated with the Center for Responsible AI (CERAI), where his work aims to promote the responsible development and deployment of AI technologies across various domains, as well as ensuring that they are transparent, fair, and aligned with social values.
