System quality attributes
By the end of this module you will be able to:
- Locate security within the ISO 25010:2023 system quality model and distinguish it from related quality characteristics
- Conduct a structured trade-off analysis between security, usability, and performance requirements
- Apply the concept of architectural fitness functions to define measurable security quality gates
- Quantify and communicate security debt to non-technical stakeholders

UK NHS COVID-19 contact tracing app architecture decision, 2020
NHS COVID-19 app: a documented quality trade-off, not a security failure
In 2020, the UK government's contact tracing app design team faced a fundamental quality trade-off. A centralised architecture, uploading proximity data to a government server, would provide richer epidemiological data and more accurate matching. A decentralised architecture, where matching occurred on the user's device, would provide stronger privacy guarantees but with lower data utility and some reduction in detection accuracy.
The NHS chose decentralised. The decision was not a security failure; it was a deliberate, documented quality trade-off that concluded the Security/Confidentiality quality characteristic should be weighted above Functional Suitability for a voluntary app where user trust was essential to adoption.
The design review was published with explicit acknowledgement of the trade-off, quantifying the estimated impact on epidemiological completeness. This is the model for quality trade-off documentation: state the options, quantify the trade-off where possible, document the decision rationale, and record who made the decision.
ISO 25010:2023: security as a quality characteristic
ISO/IEC 25010:2023 (the SQuaRE system and software quality model) defines eight top-level product quality characteristics: Functional Suitability, Performance Efficiency, Compatibility, Interaction Capability, Reliability, Security, Maintainability, and Flexibility. Security has five sub-characteristics:
- Confidentiality: data is accessible only to authorised entities.
- Integrity: the system prevents unauthorised access to or modification of data.
- Non-repudiation: actions can be proven to have taken place.
- Accountability: actions can be traced uniquely to the entity that performed them.
- Authenticity: the identity of a subject can be proven to be the one claimed.
Treating security as a quality characteristic rather than a separate compliance exercise has practical implications: security requirements are managed using the same requirements engineering practices as functional requirements; security quality is measured and tracked alongside reliability and performance; security regressions trigger the same response as performance regressions.
Security NFRs must be specific, measurable, and verifiable. A poor NFR: "The system must be secure." A well-formed NFR: "All customer payment data transmitted between the client application and the backend API must be encrypted using TLS 1.2 or higher. Connections on TLS 1.1 or below must be rejected. Compliance is verified by automated test TC-TLS-001 which connects to all production API endpoints using a TLS 1.1 client and confirms a connection failure."
With an understanding of iso 25010:2023: security as a quality characteristic in place, the discussion can now turn to trade-off analysis and architectural fitness functions, which builds directly on these foundations.
Trade-off analysis and architectural fitness functions
Security frequently creates tension with other ISO 25010:2023 quality characteristics. Documented trade-off analysis is the correct response, not silently de-prioritising one characteristic.
Security versus Interaction Capability (usability): MFA increases Authenticity but adds login friction. Resolved by: contextual MFA (prompt only on new devices or unusual locations), passkeys (FIDO2/WebAuthn replacing both password and OTP friction), and user research identifying the friction level at which adoption drops materially.
Security versus Performance Efficiency: full-disk encryption, field-level encryption, and HTTPS certificate verification all add measurable latency. Resolved by: baseline performance measurement before and after; efficient implementations (AES-GCM hardware acceleration, TLS session resumption); an explicit acceptable latency budget for security overhead.
Architectural fitness functions (Ford, Parsons, Kua: "Building Evolutionary Architectures", O'Reilly 2017) are objective, automated measurements that validate security quality properties continuously in the CI/CD pipeline. Examples: a Trufflehog scan on every pull request failing if a secret pattern is detected (no secrets in source code); a Trivy scan failing the build on any CRITICAL CVE (no vulnerable images promoted); a TLS 1.1 client test confirming connection rejection on all API endpoints; a performance test blocking deployment if p99 latency under security controls exceeds 200ms. Fitness functions are most valuable when automated, continuous, and pipeline-blocking. A fitness function generating warnings rather than failures does not enforce the property it is meant to protect.
With an understanding of trade-off analysis and architectural fitness functions in place, the discussion can now turn to security debt and nist csf 2.0 alignment, which builds directly on these foundations.
“Security is a quality characteristic of a system or software product that encompasses the degree to which the product or system protects information and data so that persons or other products or systems have the degree of data access appropriate to their types and levels of authorisation.”
ISO/IEC 25010:2023, Section 4.2.6: Security
“Security is a quality attribute that must be balanced against other quality requirements. Security controls often impose costs in performance, usability, and maintainability. Architects must make explicit trade-off decisions rather than treating security as an absolute requirement with no cost.”
ISO/IEC 25010:2023, Section 4.1: System and Software Quality Model, Security as a sub-characteristic of reliability
Security debt and NIST CSF 2.0 alignment
Security debt is the accumulation of known security deficiencies not yet remediated: deferred patches beyond their SLA, omitted controls during development, unupdated threat models, and documented but unclosed compliance gaps. A practical measurement approach:
- Inventory: enumerate all open findings from SAST, DAST, penetration tests, vulnerability scans, and compliance assessments.
- Score: assign severity and remediation cost estimate to each item.
- Age: calculate days overdue beyond defined SLA per severity tier.
- Aggregate: total security debt = sum of (overdue items × severity weight × remediation cost estimate).
- Trend: is the debt increasing or decreasing, and at what rate?
For board audiences, translate findings into business risk: "We have three unmitigated CRITICAL vulnerabilities in our payments API. If exploited, they could expose all customer payment records, with ICO fine exposure of up to 4% of global annual turnover and reputational impact comparable to the British Airways £20 million fine." Compare remediation cost to breach cost: "Fixing these three issues requires approximately 15 engineering days. A breach at our scale would likely cost £2 to 10 million in fines, incident response, customer notification, and reputational impact."
NIST CSF 2.0 (published 2024) adds a sixth function to the original five: Govern (GV) for cybersecurity risk strategy, roles, and policy; Identify (ID) for asset management and risk assessment; Protect (PR) for access control and data security; Detect (DE) for monitoring and anomaly detection; Respond (RS) for incident response; and Recover (RC) for recovery planning. The Govern function recognises that security is a governance and quality concern, not only a technical one, aligning with ISO 25010:2023's quality characteristic framing.
Common misconception
“Presenting CVE counts and CVSS severity labels to the board communicates security risk effectively.”
Boards make resource allocation decisions based on business risk, not technical metrics. A dashboard showing '142 HIGH findings' does not communicate urgency to a non-technical audience. CTOs and board members are not equipped to interpret a CVSS score or CWE category. Translate security debt into: what data is at risk, which regulations would be violated in a breach, and what the financial exposure is. The British Airways £20 million ICO fine provides a concrete UK benchmark. Compare the cost of remediation to a realistic breach cost estimate to enable a funding decision.
Common misconception
“Adding security controls to a system never negatively affects its other quality attributes.”
Every security control carries costs in at least one other quality dimension. Encryption adds CPU overhead (performance trade-off). Input validation adds processing latency (performance and usability). Strict authentication requirements reduce user accessibility (usability trade-off). Detailed audit logging increases storage requirements (efficiency trade-off). The goal is not to minimise security controls but to make explicit, documented decisions about which trade-offs are acceptable given the system's risk profile. A system that is maximally secure but unusable fails on a different quality dimension. Architectural decisions must document these trade-offs.
A payments platform feature requires 200 database calls per API request. The security architect recommends field-level encryption for all PII in the database, adding 180ms per call. At 200 calls per request, this adds 36 seconds to response time, making the API unusable. Which ISO 25010:2023 quality trade-off is in play, and what is a principled resolution?
A CISO presents to the board: 87 CRITICAL and HIGH open vulnerabilities; mean time to remediate of 42 days against a 14-day SLA for CRITICAL; three systems on the CISA KEV catalogue still unpatched. How should this be framed as security debt for a board audience that must make a funding decision?
A financial services firm is designing a real-time payment processing API that must: (1) process transactions with under 200ms end-to-end latency at 99th percentile; (2) comply with PCI DSS requiring encryption of all cardholder data in transit and at rest; (3) provide complete audit trails for fraud investigation. The security architect proposes full-payload encryption with AES-256-GCM plus SHA-256 HMAC integrity verification, logging every request and response body. A performance test shows this exceeds the 200ms SLA at P99. Which quality attribute trade-off analysis should guide the resolution?
Key takeaways
- ISO 25010:2023 positions security as one of eight product quality characteristics. Its five sub-characteristics (Confidentiality, Integrity, Non-repudiation, Accountability, Authenticity) provide a vocabulary for writing precise, verifiable security NFRs.
- Security NFRs must be specific, testable, and automated. 'The system must be secure' is not an NFR; 'all API endpoints must reject TLS 1.1 connections, verified by automated test TC-TLS-001' is.
- Quality trade-offs between security and usability or performance are inevitable. They must be documented explicitly: options considered, metrics used to evaluate, decision made, and who made it.
- Architectural fitness functions automate security quality validation as part of the CI/CD pipeline. They enforce quality properties continuously rather than episodically. A fitness function generating warnings rather than failures does not enforce the property.
- Security debt must be translated into business risk language for board audiences. CVE counts are not decision-enabling for non-technical stakeholders; financial exposure estimates and regulatory fine benchmarks are.
You now understand how quality attributes shape security architecture. The final module brings the entire course together: a capstone scenario that tests whether you can apply foundations, applied techniques, and strategic thinking to a realistic organisational challenge. Module 25 is the Professional practice capstone.
Standards and sources cited in this module
ISO/IEC 25010:2023: Systems and Software Quality Requirements and Evaluation (SQuaRE)
Eight product quality characteristics including Security (Section 4.2.6) with five sub-characteristics and their definitions.
ISO/IEC 25019:2023: Quality Measurement
Measurement of security quality sub-characteristics for quantitative evaluation.
Six-function cybersecurity framework including the new Govern (GV) function for security strategy and policy.
Ford, Parsons, Kua: Building Evolutionary Architectures (O'Reilly, 2017)
Chapter 2: Architectural fitness functions concept and implementation examples for continuous quality validation.
NHS COVID-19 app design documentation (NHSX, 2020)
Decentralised architecture decision record: documented trade-off between privacy quality and epidemiological functional utility.

