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Cybersecurity trust is not a label or a guarantee. It is a judgment based on evidence about a system’s security, the suppliers and software it depends on, and whether digital communications and identities are authentic. Because threats and technology change, organizations need to keep revisiting that judgment rather than treating it as a one-time approval.
What does “knowing what to trust” mean in cybersecurity?
It means asking what evidence supports confidence in a technology or communication, what could happen if that confidence is misplaced, and whether the organization can detect and respond to a problem. This three-part framing connects AI and data security, supply-chain dependencies, and the authenticity of messages and identities. It is a practical way to think about the problem, not a formal definition issued by NIST or ENISA.
No single score can establish that a system is safe in every setting. Security depends on the system’s design and data handling, its deployment conditions, its dependencies, and the organization’s ability to monitor it over time.
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NIST says, “The trustworthiness of AI technologies depends in part on how secure they are.” AI systems face familiar confidentiality, integrity, and availability risks, while their changing capabilities and attack surfaces can introduce threats that existing frameworks may not fully address. AI can also support defenders while giving attackers new capabilities. NIST’s AI security and resilience overview describes security as an active, rapidly changing area.
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For a practical assessment, examine the system in its actual use rather than relying only on a vendor’s general assurance. Consider:
- Data: What information enters the system, how is it handled, and what confidentiality or integrity risks could follow from misuse or compromise?
- Deployment: Where and how is the AI used, who can access it, and what decisions or services depend on its output?
- Evidence: What security information is available about the system and its dependencies, and does it apply to the organization’s specific configuration?
- Response: Can the organization detect an issue, limit its effects, and revisit its assessment when the system or threat environment changes?
These questions do not certify an AI system as trustworthy. They help connect available evidence to the consequences of relying on it.
Why do suppliers and software dependencies matter?
An organization’s exposure extends beyond the technology it builds and operates itself. Software, suppliers, and digital services can introduce risk through dependencies the organization does not directly control. NIST’s foundational SP 800-161r1 guidance centers on identifying, assessing, and mitigating cybersecurity risks across the organization and its supply chain. NIST describes the guidance as covering those activities “throughout all levels of the organization.” Its Cybersecurity Supply Chain Risk Management resources provide that organizational context.
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Supply-chain risk management should therefore be continuous, not limited to procurement or a one-time vendor review. For each important dependency, an organization needs to weigh the potential impact of compromise, the security evidence it can obtain, its ability to monitor the relationship, and its capacity to respond. The assessment should account for dependencies across the lifecycle, not just the direct supplier.
NIST listed SP 1326 and SP 800-18r2 among releases in 2026. Their appearance in that year’s release information signals ongoing guidance activity; it does not by itself establish that any particular organization has implemented the guidance or reduced its risk.
How do deepfakes and disinformation change the trust problem?
Cybersecurity also depends on whether people can judge the authenticity of communications and identities. Social engineering can exploit trust in a person or message, while information manipulation and AI-enabled deepfakes can make deceptive content more convincing. These risks affect decisions and workflows, not only the security of computers and networks.
ENISA’s 2026 Threat Landscape analyzes events observed from 1 January through 31 December 2025. Its threat summary includes information manipulation and interference, social engineering, and supply-chain attacks; it also notes AI-enabled disinformation and deepfakes among the trends. ENISA’s Foresight 2030 list includes software-dependency supply-chain compromise, advanced disinformation or influence operations, and abuse of AI as emerging threat categories. These are EU-focused threat reporting and foresight categories, not a prediction that every organization will face each threat in the same way. ENISA’s cyber-threats page links its threat-landscape material.
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What do current executive surveys say—and what can’t they prove?
PwC’s 2026 Global Digital Trust Insights surveyed 3,887 business and technology executives across 72 countries. The results describe respondents’ reported priorities and perceptions, not measured breach rates or objective security.
| PwC 2026 survey finding | What it measures |
|---|---|
| 60% ranked cyber risk investment among their top three strategic priorities in response to geopolitical uncertainty. | Executives’ reported strategic priorities in that context. |
| Only 6% said their organization was very capable across all vulnerabilities surveyed. | Executives’ reported view of organizational capability across the survey’s vulnerabilities. |
| 53% prioritized AI and machine-learning tools among their top three approaches to cyber talent gaps over the next 12 months. | Reported plans for addressing cyber talent gaps in the coming year. |
PwC also reported that knowledge and skills gaps were the top two barriers to implementing AI for cyber defense over the previous year. The combination points to a practical tension: organizations may look to AI to help address capability gaps while lacking some of the skills needed to implement AI for defense. The survey does not establish that a particular AI tool or service will close those gaps. PwC’s 2026 Global Digital Trust Insights provides the survey context.
PwC further reports that security leaders prioritized agentic AI for the coming year in areas including cloud security, data protection, and cyber defense operations. That is reported organizational intent, not proof that these capabilities have been deployed effectively or improve security outcomes.
A practical framework for deciding what to trust
Use the same core questions for an AI system, supplier, software dependency, or consequential digital communication, then adapt them to the specific risk:
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- Identify what is being trusted. Name the system, service, dependency, data flow, identity, or communication and define its role.
- Assess the consequence of compromise. Determine what could be exposed, disrupted, manipulated, or decided incorrectly if the trust assumption fails.
- Examine the available evidence. Ask what security information supports confidence, whether it applies to the actual use, and where important uncertainties remain.
- Check monitoring and response capacity. Establish whether the organization can notice a change or incident, contain the impact, and recover.
- Revisit the assessment. Update assumptions as technologies, suppliers, deployments, and threats change; a past assessment is not a permanent guarantee.
This approach follows the risk-management logic in NIST’s supply-chain guidance: identify, assess, and mitigate risk across the organization and its dependencies. It also avoids treating trust as something a tool or score can settle by itself.
Why people and operational capacity remain essential
Security controls need people who can interpret evidence, make decisions, and respond when circumstances change. PwC’s reported knowledge and skills barriers underscore that organizational capacity is part of the trust problem. AI tools and specialized managed services may be among the approaches organizations consider, but the cited survey reports priorities and does not prove the effectiveness of any specific product or provider.
Trustworthy cybersecurity practice is therefore not just a matter of adopting new technology. It requires clear responsibility for assessments, sufficient expertise to understand their limits, and operational processes that can act on what the evidence shows.
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