Why the Public Sector Should Use Responsible AI for Data Redaction
As public sector organisations manage ever‑growing volumes of sensitive and classified information, data redaction has become a critical governance and security function, not simply an administrative task. Freedom of Information (FOI) requests, legal disclosure, digital archiving and records transfer to The National Archives (TNA) all demand precision, transparency and accountability.
While Artificial Intelligence (AI) presents clear opportunities to accelerate redaction workflows, not all AI is suitable for secure public sector environments. The answer is not uncontrolled generative AI, but Responsible AI built on ethics, explainability and human oversight.
At Certes IT, we believe that Responsible AI is essential to ensure that automation enhances confidence, compliance and control, rather than introducing new risks.
What Is Responsible AI?
Responsible AI refers to AI systems designed and deployed in a way that is:
- Ethical and lawful
- Transparent and explainable
- Secure by design
- Auditable and accountable
- Supported by meaningful human oversight
This approach closely aligns with the UK Government’s Artificial Intelligence Playbook for the UK Government, which sets out ten principles for AI use, including lawfulness, transparency, security and meaningful human control at appropriate stages.
Responsible AI vs Generative AI for Secure Data Redaction
Generative AI tools are typically trained on large external datasets and designed to produce new content. While valuable in low‑risk environments, they are unsuitable for handling classified, sensitive or mission‑critical public sector data due to:
- Opaque decision‑making (“black box” models)
- Limited auditability
- Risk of data leakage or uncontrolled learning
- Difficulty demonstrating legal or governance compliance
In contrast, Explainable and Ethical AI, such as the systems used within Certes’ managed services, operates within defined rule‑sets, produces traceable decisions, and allows departmental teams to understand, challenge and validate outcomes.
This distinction is critical when working with Official, Secret, Tier 2 or Tier 3 data, where transparency and assurance are not optional but mandatory.
The Human Element: Why People Still Matter in AI‑Driven Redaction
The UK Government’s AI Playbook and Data & AI Ethics Framework are clear: AI must augment, not replace, human judgement in high‑risk use cases.
This is especially relevant for data redaction, where errors can result in:
- Accidental disclosure of personal or classified information
- Breaches of the Data Protection Act 2018 or UK GDPR
- Loss of public trust and reputational damage
Responsible AI introduces a human‑in‑the‑loop model, where:
- AI accelerates the identification of sensitive content
- Security‑cleared specialists validate and approve redactions
- Decisions remain contestable, auditable and defensible
Certes’ Data Redaction Service combines Explainable AI with ex‑military SC and DV cleared personnel, ensuring that automation never removes accountability or professional judgement.
Why Responsible AI Matters for Public Sector Data Redaction
Public sector redaction must comply with multiple statutory and regulatory obligations, including:
- Freedom of Information Act 2000
- Data Protection Act 2018
- UK GDPR
- The National Archives redaction and closure guidance
- ICO expectations on consistent, reviewed redactions
Key Challenges Without Responsible AI
Government reviews and parliamentary reports have highlighted concerns around AI adoption without transparency or assurance, particularly where high‑risk decisions lack explainability.
Without Responsible AI, departments face:
- Inconsistent redaction decisions
- Limited defensibility during audits or appeals
- Over‑redaction (reducing transparency)
- Under‑redaction (data breaches)
Responsible AI mitigates these risks by supporting consistent rule‑based decisions, creating a clear audit trail, and enabling structured human review.
Responsible AI Approach to Data Redaction
Certes’ Data & Records Transformation Service is designed specifically for government and secure environments, delivering AI‑enabled redaction without compromising security or control.
Key Principles in Practice
Drawing directly from Certes’ service model:
- Explainable and Ethical AI Decisions are accurate, transparent, auditable and aligned to policy changes without model retraining.
- Manage‑in‑Place Architecture Data never leaves the client’s environment, significantly reducing DLP risks and supporting Secure‑by‑Design principles.
- Human Oversight by Cleared Professionals Ex‑military SC and DV cleared teams provide expert validation in high‑pressure, high‑risk contexts.
- Compliance by Design Redaction processes align with FOI, GDPR, ICO guidance and TNA directives from the outset.
This approach mirrors the UK Government’s emphasis on secure deployment, transparency and accountability in AI systems.
Building Trust Through Responsible AI
Trust is foundational to public sector digital transformation. The UK Government’s AI assurance initiatives highlight that explainability and auditability are essential to building confidence in AI‑assisted decision-making.
For data redaction, Responsible AI delivers:
- Faster turnaround times without sacrificing accuracy
- Defensible decisions during FOI appeals or legal challenges
- Reduced operational burden on internal teams
- Confidence that sensitive information remains protected
Conclusion: Responsible AI Is Not Optional. It’s Essential
Data redaction is a mission‑critical function within the public sector. When dealing with sensitive or classified information, the question is not whether to use AI, but how to use it responsibly.
Responsible AI, ethical, explainable and human‑led, offers public sector organisations the ability to scale securely, remain compliant, and maintain public trust.
At Certes IT, our Responsible AI‑enabled Data Redaction Services are built to meet the realities of government, defence and secure environments, where control, clarity and confidence matter most.
