AI security is getting a lot of attention — and rightly so. Attacks against models, infrastructure and data can cause immediate harm. But security is only one slice of what makes an AI system fit for real-world use. Trustworthy AI is broader: it bundles security with fairness, explainability, robustness, privacy, governance and social impact. Ignoring the “trustworthy” half risks producing systems that are technically secure but unsafe, biased, opaque, or harmful in everyday use.

Below I walk through the differences, give concrete real-world examples, and point to programs and standards (like NIST’s ARIA and the OWASP AI Testing Guide) that are working to create standardized, repeatable ways to evaluate AI systems for both security and trustworthiness. (NIST)

Short definitions — what each term emphasizes

Secure AI
Focus: protect models, data, and infrastructure from threats.
Key goals: confidentiality (no data leaks), integrity (no tampering), availability (services stay up). Defenses include hardened deployment, access control, adversarial-robust training, monitoring for prompt-injection and model extraction attacks.

Trustworthy AI
Focus: produce AI systems that people and institutions can rely on for correct, fair, and ethically defensible outcomes.
Key goals: fairness (no unjust bias), transparency/explainability, accountability (who’s responsible), robustness across contexts, privacy-preserving design, and socio-technical impact assessment.

Think of security as protecting the house and trustworthiness as making sure the house is safe, accessible, habitable, and fair for everyone who lives there.

Real-world examples that show the difference

1. A model that’s secured but still untrustworthy: biased hiring tools

A company hardens model access, encrypts data, runs penetration tests (security ✅). But if the hiring model systematically downgrades resumes from certain demographic groups because of historical bias in the training data, the product remains untrustworthy and legally/ethically dangerous. Security measures don’t solve bias, explainability, or accountability.

Why this matters: biased decisions lead to harms (lost opportunities, discrimination), regulatory risk, and reputational damage — harms that are not prevented by cybersecurity alone.

2. Secure model, unsafe behavior: instruction-following LLMs and jailbreaks

Securing the infrastructure (rate limits, auth, secure logging) helps stop attackers from exfiltrating data — but users or attackers can still craft prompts that make a model produce harmful or misleading outputs (jailbreaks). Testing for those hazards requires targeted safety benchmarks and red-teaming beyond standard IT security checks.

Why this matters: content safety, hallucinations, and misuse are failures of trustworthiness that need their own tests and controls. Benchmarks like MLCommons’ AILuminate help measure those failure modes. (MLCommons)

3. A “safe” medical AI that isn’t trustworthy: fragile accuracy across populations

A diagnostic model tested on a clean hospital dataset performs well in lab tests (availability, secure deployment), but when used in a different hospital with different patient demographics it fails silently. That’s a trustworthiness failure — robustness, datasets, and governance were not addressed adequately.

Why this matters: patient safety, malpractice risk, and loss of trust — again, not solved by security alone.

Where standardized evaluation helps: programs & projects you should know

There’s an expanding ecosystem working to produce repeatable, interoperable evaluation methods that cover both security and trustworthiness.

  • NIST — ARIA (Assessing Risks and Impacts of AI): ARIA is an evaluation-driven NIST program focused on measuring sociotechnical risks and impacts of AI systems to help organizations determine whether an AI system is valid, reliable, safe, secure, private, and fair in deployment. It treats evaluation as a broad, practical activity — not just a model-run metric. (NIST)
  • OWASP — AI Testing Guide: practitioner-centric, community-driven testing methods for AI systems that go beyond classic IT security to include trustworthiness testing: adversarial tests, privacy checks, fairness probes and more. This guide is explicitly designed to make testing repeatable and actionable. (OWASP Foundation)
  • MLCommons — AILuminate: a public benchmark suite designed to evaluate generative AI safety across many hazard categories (jailbreaks, safety, disallowed content, privacy risks). Useful for repeatable safety scoring of LLMs and comparing models on concrete harms. (MLCommons)
  • ISO/IEC JTC 1/SC 42 (Trustworthiness work) and IEEE P7000 series: international standards work and ethics-by-design processes that define vocabulary, conformance, and design practices for trustworthy AI. These bodies aim to make evaluation and certification tractable across industries and borders. (ISO)
  • Regulatory frameworks (EU AI Act, OECD Principles, national testbeds): increasingly, laws and policy frameworks demand documentation, risk assessments, and conformity testing for high-risk systems. These forces push organizations to adopt standardized evaluation methods. (Artificial Intelligence Act)

Why trustworthiness should get at least as much attention as security

  1. Harm types differ and multiply — security prevents exfiltration and hijacks; trustworthiness prevents harms such as discrimination, opaque decision-making, and socio-economic impacts. A single attack or silent bias can cause long-term societal harm that security controls alone don’t address.
  2. Regulatory & legal footprint — regulators increasingly require evidence of fairness, documentation, and risk management (e.g., EU AI Act). A secure system without documentation, impact assessments and governance can still be non-compliant. (Artificial Intelligence Act)
  3. Business continuity & trust — customers, clinicians, regulators and the public withdraw trust faster from opaque or biased systems than they forgive isolated security incidents. Trustworthiness builds sustainable adoption.
  4. Interlocking requirements — many trustworthiness attributes are socio-technical: they require people, processes, and organizational governance in addition to technical controls. Security teams alone often don’t own these concerns.

A practical evaluation checklist (short) — to combine security + trustworthiness

Use a combined evaluation plan that includes both technical tests and sociotechnical assessments.

  1. Threat & risk mapping
    • Security: adversary models, data-exfiltration surfaces.
    • Trustworthy: who can be harmed, how, under what contexts? (stakeholder harm mapping)
  2. Standardized test battery
    • Security: red-teaming, adversarial inputs, pen tests.
    • Trustworthy: fairness tests, explainability checks, robustness (OOD), policy compliance tests (use ARIA/OWASP/AILuminate artifacts).
  3. Data and model provenance
    • Document datasets, curation processes, and training pipelines (audit trail).
  4. Monitoring & incident response
    • Expand beyond security logs: monitor for biased outcomes, drift, unexplained behavior.
  5. Governance & accountability
    • Assign clear owners, review boards, and deployment approvals; include domain experts and impacted-community review.
  6. Third-party evaluation & transparency
    • Use independent benchmarks (AILuminate) and publish summaries/attestations when possible to build public confidence.

What organizations — and practitioners — can do next

  • Start not with a checklist, but with expected uses and impacts: who will use the system, which decisions will it influence, and what harms must be prevented? Mapping use → risk → test plan is the most practical route.
  • Adopt or adapt existing standardized artifacts: ARIA guidance for impact-oriented evaluation, OWASP AI Testing Guide for practical test cases, AILuminate for model safety benchmarks. These are not silver bullets but they give you repeatable methods and community-tested test suites. (NIST)
  • Bake governance into the lifecycle: testing isn’t a one-time activity. Evaluate pre-deployment, during pilots, and continuously in production.
  • Involve multidisciplinary reviewers: technical teams, domain subject matter experts, ethicists, and representatives of potentially impacted groups.

Conclusion — security is necessary, trustworthiness is essential

Security is critical — protect your models, your data and your infrastructure. But trustworthiness is the bridge to responsible, long-term deployment. It ensures that the AI system behaves fairly, explains itself when it matters, stays robust across contexts, and is governed responsibly. The fastest route to safe, reliable, and socially acceptable AI is to evaluate both dimensions with standardized, practical methods — and to adopt the growing set of programs and benchmarks (NIST ARIA, OWASP AI Testing Guide, MLCommons AILuminate, ISO/IEEE work and regulatory frameworks) that are building those methods today. (NIST)