The STRIDE framework is a structured approach to threat modeling that helps organizations identify and prioritize the most common and impactful cybersecurity threats. Originally developed by Microsoft, STRIDE remains widely used today to assess risks across modern systems, including AI-driven environments.
For organizations pursuing ISO/IEC 42001 compliance, STRIDE framework threat modeling plays an important role in AI risk identification, mitigation planning, and governance alignment. It supports proactive security decision-making while also helping organizations meet overlapping requirements found in other cybersecurity and risk management frameworks.
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STRIDE Threat Modeling and ISO/IEC 42001
The STRIDE framework is a cybersecurity threat modeling methodology developed by Microsoft in the early 2000s and formally introduced between 2005 and 2006. Despite its age, STRIDE remains highly relevant in today’s threat landscape—particularly for systems that incorporate artificial intelligence (AI).
STRIDE focuses on six specific categories of risk: spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege. This structured approach makes STRIDE especially effective for AI risk modeling and mitigation, where threats must be identified, categorized, and addressed systematically to support ISO/IEC 42001 compliance.
In the sections below, we explain how STRIDE framework threat modeling aligns with ISO 42001 and broader security objectives, including:
- What the STRIDE framework is and what each threat category addresses
- How STRIDE can support ISO/IEC 42001 requirements
- How STRIDE applies to other cybersecurity and risk management frameworks beyond AI
- Additional AI security considerations organizations should account for
Because AI governance introduces complex and evolving risks, working with an AI compliance and security specialist is often the most effective way to integrate the STRIDE framework, or any threat modeling methodology, into a long-term AI governance and risk management strategy.
What Is the STRIDE Framework?
The STRIDE framework is a threat modeling methodology developed by Microsoft in 2006 to help organizations identify and categorize the most critical cybersecurity threats. Despite being introduced nearly two decades ago, STRIDE remains widely used today across a range of environments, including modern, AI-enabled systems.
Microsoft continues to apply the STRIDE framework in threat modeling for its proprietary software, reinforcing its ongoing relevance. STRIDE focuses on the ways attackers can compromise systems or abuse legitimate functionality to gain unauthorized access, manipulate data, or disrupt operations. By categorizing threats into defined risk areas, STRIDE helps security teams prioritize mitigation efforts based on potential impact.
As noted above, STRIDE is an acronym representing six categories of threats:
- Spoofing
- Tampering
- Repudiation
- Information disclosure
- Denial of service
- Elevation of privilege
In later sections, we’ll apply Microsoft’s STRIDE guidance to AI systems and ISO/IEC 42001 requirements. First, however, it’s important to understand each threat category in detail to see why this model remains effective for AI-focused threat modeling and continuous monitoring
Spoofing (STRIDE Framework)
Within the STRIDE framework, spoofing refers to attacks in which a threat actor impersonates a legitimate user to gain unauthorized access to systems, applications, or data. This commonly occurs when attackers obtain valid credentials, such as usernames, passwords, API keys, or authentication tokens, through methods like phishing, credential stuffing, or brute-force attacks.
While spoofing can take many forms, STRIDE focuses on the outcome of identity-based compromise: an attacker successfully masquerading as an authorized entity to bypass access controls and perform unauthorized actions.
When assessing spoofing risks using STRIDE framework threat modeling, organizations evaluate:
- Weak or improperly implemented authentication mechanisms
- Vulnerabilities that could enable credential theft or identity impersonation
- Indicators of compromise suggesting unauthorized access has already occurred
Addressing spoofing risks is especially important in AI-enabled systems, where compromised identities can be used to manipulate models, access sensitive training data, or interfere with AI-driven decision-making
Tampering (STRIDE Framework)
In the STRIDE framework, tampering refers to any unauthorized modification of data, code, or system configurations. Under this category, even changes made without malicious intent are considered tampering if they are not explicitly authorized. Ensuring the integrity of sensitive information is critical, as tampering can compromise system reliability, regulatory compliance, and operational trust.
Many regulatory and compliance frameworks, including ISO/IEC 42001, require robust data integrity and change management controls. Assessing tampering risks involves examining processes and systems to ensure that all modifications to sensitive data are tracked, verified, and properly authorized.
Organizations can detect and mitigate tampering threats by implementing:
- System-wide visibility and auditing mechanisms to monitor changes
- Access controls to limit who can modify sensitive information
- Logging and alerting infrastructure to identify unauthorized or suspicious modifications
In AI-enabled systems, tampering poses unique risks, such as manipulating model training data or altering algorithmic outputs, which can undermine the integrity and reliability of AI-driven decisions.
Repudiation (STRIDE Framework)
Within the STRIDE framework, repudiation occurs when an actor is able to deny performing an action in a system or application, leaving no verifiable proof of the event. This creates a risk that malicious activities, intentional or accidental, cannot be traced back to their source. Ensuring non-repudiation is therefore a fundamental aspect of robust cybersecurity and compliance programs.
Assessing repudiation risks involves ensuring complete system visibility across hardware, software, and network components. Organizations can implement strategies such as:
- Audit logs that record all critical user and system actions
- Digital signatures and cryptographic verification to validate actions
- Regular penetration testing to identify weaknesses where malicious actors might conceal activities
For AI-enabled systems, repudiation risks may include actions like unauthorized modifications to training data or manipulation of automated workflows, which can be difficult to detect without proper monitoring. Implementing non-repudiation measures is essential for maintaining ISO/IEC 42001 compliance and safeguarding AI governance integrity.
Information Disclosure (STRIDE Framework)
In the STRIDE framework, information disclosure occurs when sensitive or protected information is exposed to individuals or systems that are not authorized to access it. This can happen in multiple ways:
- An authorized user unintentionally shares data with the wrong recipients
- Data is illegitimately accessed or exfiltrated by a threat actor
- Other STRIDE threats, such as spoofing or tampering, lead to exposure
Regardless of the method, any unauthorized exposure of data is a serious security concern. Detecting and preventing information disclosure is essential for maintaining regulatory compliance and protecting organizational trust.
Assessing information disclosure risks involves monitoring who has access to sensitive data, tracking how it is used, and implementing safeguards such as:
- Data access controls to enforce the principle of least privilege
- Encryption of sensitive information at rest and in transit
- Monitoring and logging systems to detect unauthorized access or sharing
In contexts like HIPAA compliance, the severity of information disclosure can trigger legal obligations, such as breach notifications, depending on the scope and nature of the exposed data. In AI-enabled systems, information disclosure can include unauthorized access to training data or model outputs, which may compromise both privacy and the integrity of AI-driven decisions. Ensuring robust controls for information disclosure is therefore critical for ISO/IEC 42001 compliance and effective AI risk management
Denial of Service (DoS) (STRIDE Framework)
Within the STRIDE framework, denial of service (DoS) refers to attacks that prevent authorized users from accessing systems, applications, or data. One of the most common forms is a distributed denial of service (DDoS) attack, in which attackers overwhelm a server or network with excessive traffic, rendering it unavailable to legitimate users. DoS attacks can take many forms, but their impact is always the disruption of normal operations.
Mitigating DoS risks requires both infrastructure resilience and proactive monitoring. Organizations should:
- Develop network diagrams to understand typical traffic patterns and identify potential bottlenecks
- Maintain bandwidth capacity and redundancy to absorb sudden traffic surges
- Implement real-time monitoring and alerting systems to detect abnormal traffic flows quickly
- Conduct stress tests and simulations to evaluate how infrastructure responds under peak loads
In AI-enabled systems, DoS attacks can disrupt access to models or data pipelines, potentially delaying automated decision-making or affecting critical AI-driven services. Implementing DoS protections is therefore essential for maintaining ISO/IEC 42001 compliance, ensuring business continuity, and preserving trust in AI systems.
Elevation of Privilege (STRIDE Framework)
Within the STRIDE framework, elevation of privilege refers to situations in which a user or process gains access rights beyond what was originally authorized. While it is not always an end goal on its own, privilege escalation is a common objective in infiltration-focused attacks because it allows threat actors to expand their reach within a system.
Attackers may seek limited privilege escalation to quietly access sensitive data while avoiding detection, or they may attempt full administrative control to disrupt operations, alter system behavior, or destroy data environments. The impact of successful privilege escalation can therefore range from targeted data exposure to complete system compromise.
Assessing elevation-of-privilege risks using STRIDE framework threat modeling involves evaluating:
- How attackers could move laterally within systems after initial access
- Weaknesses in role-based access controls or permission boundaries
- The potential actions an attacker could perform at higher privilege levels
As with spoofing, internal penetration testing, especially tests focused on post-compromise mobility and privilege escalation paths, is an effective way to identify and mitigate these vulnerabilities. In AI-enabled environments, elevated privileges can enable unauthorized access to models, training data, or deployment pipelines, making privilege management a critical component of ISO/IEC 42001 compliance and AI governance
Applying STRIDE to ISO/IEC 42001
ISO/IEC 42001 is an international AI regulatory framework jointly developed by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC). It emphasizes AI governance through a formalized AI Management System (AIMS). Organizations operating in Europe and other regions often implement ISO guidance, including 42001, to meet client expectations and demonstrate compliance with recognized standards.
A central component of ISO 42001 is AI risk mitigation. The standard requires organizations to conduct:
- AI-specific risk assessments (requirements 6.1.2 and 8.2)
- AI system impact assessments (requirements 6.1.4 and 8.4)
Unlike some other frameworks, ISO 42001 does not mandate a specific methodology, leaving organizations free to select the most suitable approach for their AI systems.
The STRIDE framework can be effectively applied to ISO 42001 by mapping AI-specific threats and vulnerabilities to the six STRIDE categories:
- Spoofing: How AI tools or systems could lead to credential theft or unauthorized access
- Tampering: Where AI systems might be vulnerable to data or model integrity issues
- Repudiation: Whether monitoring systems can reliably log and verify AI actions
- Information Disclosure: How attackers could exfiltrate or expose sensitive AI data
- Denial of Service (DoS): Where servers and AI infrastructure might be disrupted
- Elevation of Privilege: What attackers could achieve by gaining unauthorized privileges in AI systems
Organizations can also combine STRIDE with other threat modeling tools for a more comprehensive AI security posture. Common complementary approaches include:
- OWASP Vulnerability Analysis: Identifies and prioritizes system weaknesses
- DREAD: A mnemonic that evaluates threats based on Damage potential, Reproducibility, Exploitability, Affected users, and Discoverability
By integrating STRIDE with ISO/IEC 42001 requirements and additional threat modeling methods, organizations can create a robust AI risk management strategy that strengthens both security and compliance
Broader Applications for STRIDE
While the STRIDE framework is a highly effective method for meeting ISO/IEC 42001 AI risk assessment requirements, its utility extends far beyond AI compliance. Organizations can leverage STRIDE to identify and manage cybersecurity risks across diverse systems and regulatory frameworks, making it a versatile tool for comprehensive risk management.
The six STRIDE threat categories, spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege, are applicable to multiple compliance standards:
- HIPAA (Health Insurance Portability and Accountability Act):
Information disclosure is particularly critical under HIPAA, as breach notification requirements apply to protected health information (PHI). STRIDE helps organizations monitor, detect, and mitigate unauthorized data disclosures, ensuring compliance with HIPAA’s strict rules. - PCI DSS (Payment Card Industry Data Security Standard):
STRIDE is equally valuable for protecting cardholder data (CHD). Identifying vulnerabilities related to tampering, spoofing, or unauthorized access allows organizations to safeguard sensitive financial information and maintain PCI DSS compliance.
By applying STRIDE in these contexts, organizations gain a structured approach to risk assessment and prioritization, whether the goal is regulatory compliance or general cybersecurity. As a result, STRIDE can form the foundation of any organization’s risk management program, providing actionable insights for protecting data, systems, and users across industries.
Other AI Risk Management Considerations
One reason the STRIDE framework is particularly effective for AI risk management is its flexibility and adaptability. AI remains a rapidly evolving technology, and global laws and regulations are still emerging. While ISO/IEC 42001 is one of the most widely recognized frameworks for AI governance, future regulatory developments, both in the US and internationally—may introduce more complex compliance requirements.
For example, in the United States, the National Institute of Standards and Technology (NIST) has published an AI Risk Management Framework (AI RMF). Additionally, several US states and local jurisdictions are developing AI-related rules that align with the themes of ISO 42001 and the NIST AI RMF. Although AI mandates are currently limited, organizations that prepare proactively can avoid compliance gaps as regulations evolve.
Implementing robust AI threat modeling and risk mitigation strategies, such as the STRIDE framework, helps organizations maintain long-term compliance and strengthen AI governance. Partnering with an experienced compliance and security specialist ensures that STRIDE is applied effectively, providing a structured approach to managing evolving AI risks while safeguarding data, models, and systems.
Implement the STRIDE Framework Today
The STRIDE framework may not be new, but its applications are evolving, especially in the context of AI risk management and ISO/IEC 42001 compliance. By breaking down threats into six focused categories, spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege, STRIDE enables organizations to identify, prioritize, and mitigate risks in a structured and actionable way.
At RSI Security, we help organizations implement comprehensive threat modeling and risk mitigation strategies tailored to AI systems and broader cybersecurity needs. Our expertise includes:
- Applying STRIDE to meet ISO/IEC 42001 and other AI compliance frameworks
- Aligning threat modeling with emerging AI regulations globally
- Providing actionable guidance to safeguard data, models, and AI-driven systems
By addressing threats proactively, organizations can ensure long-term compliance, strengthen security posture, and unlock the freedom to innovate with confidence in the age of AI.
Contact RSI Security today to learn more about our AI risk management, STRIDE implementation, and cybersecurity services, and take the first step toward a secure and compliant AI environment.
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