Privacy engineering is often called “building privacy in,” but in practice, it is far more complex and involves dealing with rapidly evolving technology, unclear laws and shifting user expectations. The role of a privacy engineer also varies across organizations, shaping when privacy is incorporated into products and how controls are implemented.
ISACA’s State of Privacy 2026 report signals that privacy programs work with limited resources, uneven technical staffing and growing complexity of regulations. These realities set the context for understanding key challenges in privacy engineering. Within these constraints, privacy engineering works best when it is repeatable, measurable and integrated into the product lifecycle – not treated as a late-stage review step.
I recently worked on a US-based privacy engineering practitioner study, which surfaced several challenges:
Privacy Engineering Challenges
1) Translating regulations into technical requirements remains a bottleneck
One major challenge is turning vague legal requirements into practical and adoptable technical controls. Privacy engineers need to interpret laws, create controls and check them across systems. As the regulatory landscape evolves, this task gets harder and increases delivery risks.
2) Role ambiguity creates execution gaps
Many organizations still find it difficult to define what privacy engineering is responsible for, where it sits and how it differs from adjacent work in security engineering, product compliance, or governance. This ambiguity leads to late engagement, uneven review quality and inconsistent control implementation.
3) Compliance-only mindset undermines privacy by design
Not practicing privacy by design is one of the most common privacy failures, according to ISACA’s State of Privacy 2026. Problems arise when product goals such as fast delivery, growth and short-term results conflict with reducing privacy risks. A compliance-only framing turns privacy into a checkbox rather than an engineering quality attribute. This often leads organizations to focus on paperwork rather than lasting system changes, resulting in repeated problems and avoidable failures.
4) Effective cross-functional collaboration is structurally difficult
Privacy engineering is inherently interdisciplinary. Effective work requires close coordination among engineering, product, legal, compliance and risk functions. When stakeholders are not aligned, privacy engineering becomes reactive, responding to incidents, escalations or launch deadlines rather than proactively shaping system design from the start.
5) Measuring success remains informal
Many privacy engineers describe success as the absence of incidents, meeting SLAs or earning stakeholder trust. These indicators don’t provide a stable basis for setting priorities or planning. Without clear metrics, privacy engineering often loses out to other work, meaning privacy is put off until incidents happen.
Five Strategies that Help Privacy Engineering Scale
1) Define privacy engineering responsibility and scope
Start by removing ambiguity. Clarify what privacy engineering entails and how it integrates with development. Define clear OKRs (Objectives and Key Results) and establish how the team works cross-functionally. When roles and deliverables are explicit, privacy is easier to plan and execute consistently.
2) Develop a repeatable process for translating requirements into technical controls
Maintain a control catalogue linked to common obligations and risks, and pair it with reusable engineering patterns that teams can use. The goal is to reduce reinvention. Teams can apply tested approaches for data minimization defaults, anonymization, retention enforcement, logging and more. Over time, this builds organizational memory and lowers the cost of compliance and risk management.
3) Treat privacy engineering as sociotechnical work
Privacy engineering is not solely a technical implementation; it is also negotiation, influence and stakeholder alignment. Formalize recurring touchpoints, such as product intake gates, and document decision logs for high-risk trade-offs. When collaboration is routine rather than emergency-driven, privacy becomes part of the operating rhythm, and engineering teams tend to engage early.
4) Address skill gaps
Effective privacy engineering depends on more than knowledge of regulations or privacy principles. Technical skills matter, but so do communication and risk management – the ability to explain trade-offs, advocate for controls and align stakeholders. Closing these skill gaps demands deliberate staffing and training, not just assigning privacy responsibilities to whoever is available.
5) Develop metrics that demonstrate value
Track leading indicators such as integration of privacy controls into roadmaps, time-to-close for privacy issues, quality of risk assessments and recurring privacy patterns. Additionally, track lagging indicators such as the number and severity of privacy incidents, repeat findings, and outcomes from audits or reviews. Even modest measurement improves prioritization, helps leadership understand progress and supports resource requests.
Make Privacy a Core Capability
Privacy engineering is evolving into a distinct practice but it faces challenges such as limited resources, skill gaps, complex rules and rapidly changing technology. The hardest problems are not just technical – they include turning broad requirements into system design, dealing with unclear roles, fixing misaligned incentives and working with teams that operate in different ways.
To move forward, organizations should focus on five key actions: clarify privacy engineering roles and expectations, develop and reuse control patterns, integrate privacy into development workflows, invest in the right mix of skills and measure meaningful outcomes for leadership. By making privacy engineering a consistent, proactive process, organizations can better match user expectations, organizational values, and evolving regulatory requirements, transforming privacy from a reactive task into a core capability.
About the author: Nandita Rao Narla is the Head of Technical Privacy and Governance at DoorDash. Previously, she was a founding team member of a data profiling startup and held various leadership roles at EY, where she helped Fortune 500 companies build and mature privacy, cybersecurity, and data governance programs. She is a Senior Fellow at Future of Privacy Forum and serves on the Advisory Boards, technical standards committees, and working groups for ISACA, IAPP, IEEE, Ethical Tech Project, X Reality Safety Initiative, Institute of Operational Privacy Design, and NIST.
