Cyber Strategy
Cyber Secure
Cyber Operations
Cyber Response
Cyber ResilienceAI has moved from “innovation” to “infrastructure.” GenAI copilots, conversational assistants, retrieval-augmented generation (RAG), automated decisioning, and AI-enabled analytics are now embedded in core business processes, often faster than governance, privacy, and security functions can adapt.
That speed creates a predictable outcome: organisations attempt to manage AI risk in silos.
- Security teams focus on traditional controls (identity, segmentation, vulnerability management) but miss model- and workflow-specific attack paths.
- Privacy and data protection teams focus on lawful basis, minimisation, and retention but struggle to operationalise controls inside AI pipelines and prompts.
- Governance teams publish policy and principles but lack evidence, testing, and technical traceability from requirements to implementation.
The result is not just “risk”, it is operational friction, duplicated effort, and weak assurance. In practice, AI risk becomes manageable only when Cybersecurity, Data Protection, and AI Governance are treated as a single integrated control system with shared artefacts, shared testing, and a shared evidence model.
That is the core thesis of DTS Solution’s S3CURE/AI: a standards-led offering designed to translate AI ambition into defensible controls, measurable assurance, and audit-ready evidence.
The Three Risk Planes You Must Manage Together
1) Cybersecurity: AI Expands the Attack Surface in Non-Obvious Ways
Classic security programs are built around systems with deterministic behaviour. AI systems are different:
- They can be manipulated through inputs (prompts, context, documents, tool calls).
- They can be influenced by data (training, embeddings, retrieval sources, feedback loops).
- They can produce outputs that trigger downstream actions (agents, automations, approvals, communications).
This changes the threat model. AI introduces attack paths that are not fully addressed by conventional application security or network controls alone, particularly where LLMs interact with tools, data stores, and user context.
Two reference points are especially useful here:
- OWASP Top 10 for LLM Applications (e.g., prompt injection, sensitive information disclosure, insecure output handling, supply chain risks, and excessive agency).
- MITRE ATLAS (a structured view of adversarial tactics and techniques targeting AI systems across the lifecycle).
The practical takeaway: AI security is not only “LLM red teaming.” It is workflow threat modelling, architecture review, and continuous assurance across the AI lifecycle.
2) Data Protection: AI Makes “Data Boundaries” Harder to Prove
AI systems often aggregate, transform, and infer. That complicates the controls regulators, and customers ask for:
- Where did the data originate?
- Is personal data involved, directly or indirectly?
- Can the model “remember” sensitive information?
- What happens to prompts, chat logs, embeddings, and generated outputs?
- Can individuals exercise rights (access, deletion, objection) without breaking the system?
Even when an AI use case is based entirely on open/public data (such as a public-facing assistant using government open repositories), privacy and data protection still matter, because risk shifts to areas like security logging, prompt/response retention, third-party processors, and misleading outputs that create harm.
The practical requirement is not more policy; it is provable data governance inside the AI solution design.
3) AI Governance: Principles Must Become Controls and Evidence
AI governance often starts with ethics and high-level commitments. That is necessary, but not sufficient.
Executives, auditors, and regulators ultimately ask for:
- a risk classification approach (including emerging legal requirements such as the EU AI Act),
- defined accountability (roles, approvals, oversight),
- documented controls mapped to standards,
- testing and monitoring,
- an evidence catalogue that demonstrates what is implemented, how it is tested, and how it is operated.
Governance fails when it cannot connect:
policy → requirements → controls → tests → evidence → oversight decisions.
“AI doesn’t fail like traditional systems; it fails at the seams between security, privacy, and governance. S3CURE/AI closes those seams by turning AI risk into practical, standards-based controls and audit-ready evidence.” said Rizwan Tanveer, Principal Consultant Cybersecurity GRC, Data Privacy & AI
Why Siloed Approaches Break Down
Most AI programs stall or backfire due to predictable failure modes:
- Security-only implementations that ignore privacy obligations and governance traceability.
- Privacy-only reviews that identify risks but cannot validate technical controls in the AI stack.
- Governance-only documents that are not tied to real architectures, workflows, and operational monitoring.
- Innovation bottlenecks that push teams toward Shadow AI because safe alternatives are not enabled.
- Assurance gaps where controls exist but cannot be evidenced, tested, or reported in an audit-ready way.
The fix is not another committee. The fix is an integrated delivery model designed for AI systems.
The Integrated Operating Model: One Control System, Three Lenses
A robust approach treats AI as a socio-technical system and aligns three disciplines around a single, shared backbone:
Shared Backbone
- Standards and crosswalks that are credible to auditors and scalable across business units.
- Architecture and workflow threat modelling that captures AI-specific attack paths.
- Data governance embedded in pipelines (inputs, retrieval, storage, outputs).
- Evidence-driven assurance with repeatable test methods and measurable outcomes.
Three Lenses, One Implementation
- Cybersecurity lens: threat modelling, security architecture controls, red teaming, monitoring, and incident readiness.
- Data protection lens: lawful basis, minimisation, retention, DSAR readiness, vendor controls, privacy-by-design.
- AI governance lens: risk classification, oversight and accountability, policy-to-control traceability, ongoing assurance.
This is precisely what S3CURE/AI operationalises.
Introducing DTS Solution’s S3CURE/AI: Standards-Led AI Governance and Security, Made Executable
S3CURE/AI is DTS Solution’s integrated service offering for organisations adopting AI and GenAI. It is designed to help teams enable AI safely, while producing the artefacts that leadership, customers, and regulators expect.
Standards-Based Backbone (Credibility and Auditability)
S3CURE/AI is intentionally standards-led to ensure defensible governance and repeatable delivery, aligned to:
- ETSI AI Security Baseline (cybersecurity requirements for AI models and systems)
- NIST AI Risk Management Framework (AI RMF) (governance, mapping, measuring, and managing AI risk)
- ISO/IEC AI terminology and lifecycle alignment (consistent definitions and management discipline)
- ISO/IEC 42001 alignment, where clients want a management system approach to AI governance
- OWASP Top 10 for LLM Applications and MITRE ATLAS to anchor AI security threats and controls
What clients receive (core artefacts):
- A documented control crosswalk (Standards ↔ DTS controls ↔ client requirements)
- An evidence catalogue (controls mapped to artefacts, tests, operational proofs, and ownership)
- Audit-ready reporting (traceability from requirements to implemented safeguards and monitoring)
This is the difference between “we think we’re safe” and “we can prove we’re safe.”
AI has moved from “innovation” to “infrastructure.” GenAI copilots, conversational assistants, retrieval-augmented generation (RAG), automated decisioning, and AI-enabled analytics are now embedded in core business processes, often faster than governance, privacy, and security functions can adapt.
That speed creates a predictable outcome: organisations attempt to manage AI risk in silos.
- Security teams focus on traditional controls (identity, segmentation, vulnerability management) but miss model- and workflow-specific attack paths.
- Privacy and data protection teams focus on lawful basis, minimisation, and retention but struggle to operationalise controls inside AI pipelines and prompts.
- Governance teams publish policy and principles but lack evidence, testing, and technical traceability from requirements to implementation.
The result is not just “risk”, it is operational friction, duplicated effort, and weak assurance. In practice, AI risk becomes manageable only when Cybersecurity, Data Protection, and AI Governance are treated as a single integrated control system with shared artefacts, shared testing, and a shared evidence model.
That is the core thesis of DTS Solution’s S3CURE/AI: a standards-led offering designed to translate AI ambition into defensible controls, measurable assurance, and audit-ready evidence.
The Three Risk Planes You Must Manage Together
1) Cybersecurity: AI Expands the Attack Surface in Non-Obvious Ways
Classic security programs are built around systems with deterministic behaviour. AI systems are different:
- They can be manipulated through inputs (prompts, context, documents, tool calls).
- They can be influenced by data (training, embeddings, retrieval sources, feedback loops).
- They can produce outputs that trigger downstream actions (agents, automations, approvals, communications).
This changes the threat model. AI introduces attack paths that are not fully addressed by conventional application security or network controls alone, particularly where LLMs interact with tools, data stores, and user context.
Two reference points are especially useful here:
- OWASP Top 10 for LLM Applications (e.g., prompt injection, sensitive information disclosure, insecure output handling, supply chain risks, and excessive agency).
- MITRE ATLAS (a structured view of adversarial tactics and techniques targeting AI systems across the lifecycle).
The practical takeaway: AI security is not only “LLM red teaming.” It is workflow threat modelling, architecture review, and continuous assurance across the AI lifecycle.
2) Data Protection: AI Makes “Data Boundaries” Harder to Prove
AI systems often aggregate, transform, and infer. That complicates the controls regulators, and customers ask for:
- Where did the data originate?
- Is personal data involved, directly or indirectly?
- Can the model “remember” sensitive information?
- What happens to prompts, chat logs, embeddings, and generated outputs?
- Can individuals exercise rights (access, deletion, objection) without breaking the system?
Even when an AI use case is based entirely on open/public data (such as a public-facing assistant using government open repositories), privacy and data protection still matter, because risk shifts to areas like security logging, prompt/response retention, third-party processors, and misleading outputs that create harm.
The practical requirement is not more policy; it is provable data governance inside the AI solution design.
3) AI Governance: Principles Must Become Controls and Evidence
AI governance often starts with ethics and high-level commitments. That is necessary, but not sufficient.
Executives, auditors, and regulators ultimately ask for:
- a risk classification approach (including emerging legal requirements such as the EU AI Act),
- defined accountability (roles, approvals, oversight),
- documented controls mapped to standards,
- testing and monitoring,
- an evidence catalogue that demonstrates what is implemented, how it is tested, and how it is operated.
Governance fails when it cannot connect:
policy → requirements → controls → tests → evidence → oversight decisions.
Why Siloed Approaches Break Down
Most AI programs stall or backfire due to predictable failure modes:
- Security-only implementations that ignore privacy obligations and governance traceability.
- Privacy-only reviews that identify risks but cannot validate technical controls in the AI stack.
- Governance-only documents that are not tied to real architectures, workflows, and operational monitoring.
- Innovation bottlenecks that push teams toward Shadow AI because safe alternatives are not enabled.
- Assurance gaps where controls exist but cannot be evidenced, tested, or reported in an audit-ready way.
The fix is not another committee. The fix is an integrated delivery model designed for AI systems.
The Integrated Operating Model: One Control System, Three Lenses
A robust approach treats AI as a socio-technical system and aligns three disciplines around a single, shared backbone:
Shared Backbone
- Standards and crosswalks that are credible to auditors and scalable across business units.
- Architecture and workflow threat modelling that captures AI-specific attack paths.
- Data governance embedded in pipelines (inputs, retrieval, storage, outputs).
- Evidence-driven assurance with repeatable test methods and measurable outcomes.
Three Lenses, One Implementation
- Cybersecurity lens: threat modelling, security architecture controls, red teaming, monitoring, and incident readiness.
- Data protection lens: lawful basis, minimisation, retention, DSAR readiness, vendor controls, privacy-by-design.
- AI governance lens: risk classification, oversight and accountability, policy-to-control traceability, ongoing assurance.
This is precisely what S3CURE/AI operationalises.
How S3CURE/AI Works: A Practical Three-Tier Delivery Model
To make adoption scalable and executive-friendly, S3CURE/AI is delivered as a structured, phased program. A practical model is:
S1 — Discover & Assess (Baseline Risk and Readiness)
Designed for organisations that need to move quickly but responsibly.
Typical activities
- AI use-case inventory and scope definition (systems, workflows, data, vendors)
- AI risk classification and governance readiness review (including EU AI Act-aligned thinking where relevant)
- Architecture and workflow threat modelling (AI-specific)
- Privacy and data protection impact assessment orientation (where personal data is in-scope)
- Initial control baseline mapped to standards
Deliverables
- AI risk and maturity assessment report
- Prioritised remediation roadmap
- Initial control crosswalk and evidence plan
Outcome
A defensible baseline and an implementation plan that leadership can fund and execute.
S2 — Design & Build (Controls, Guardrails, and Secure Architecture)
Designed for organisations moving from “assessment” to “implementation.”
Typical activities
- Security architecture review and secure-by-design guardrails for AI components (RAG, agents, tool integrations)
- Control implementation guidance for AI-specific threats (prompt injection, data leakage, insecure output handling, etc.)
- Privacy-by-design control embedding (retention, access controls, minimisation, vendor boundaries)
- Governance operating model definition (roles, approvals, escalation, change control)
- Assurance test plan design aligned to OWASP LLM Top 10 / MITRE ATLAS
Deliverables
- AI control design package (technical + governance)
- Threat model and mitigations mapped to implementation tasks
- Updated evidence catalogues and operating procedures
Outcome
Working controls that reduce real-world exploitability and enable confident deployment.
S3 — Assure & Operate (Ongoing Testing, Monitoring, and Evidence)
Designed for organisations that require continuous assurance and audit readiness.
Typical activities
- Validation testing and continuous control monitoring (including LLM/AI security testing programs)
- Evidence collection and reporting cadence (executive dashboards and audit packs)
- Incident readiness integration (AI-specific scenarios, triage, response playbooks)
- Governance refinement and periodic reviews (policy updates, model changes, vendor drift)
Deliverables
- Audit-ready assurance pack and recurring reporting
- Continuous improvement backlog and control effectiveness tracking
- Executive risk summaries and decision support artefacts
Outcome
AI systems remain safe, compliant, and governable as they evolve.
Real-World Use Cases Where the Intersection Matters
Use Case A: Public-Facing AI Assistant Using Open/Public Data
Even if no personal data is used, the system can still create risk through:
- prompt injection and malicious input patterns,
- insecure output handling (unsafe instructions, misleading claims),
- integrity issues in retrieval sources,
- logging and retention practices,
- operational controls (change management, monitoring, incident response).
S3CURE/AI addresses this by combining:
- workflow threat modelling + OWASP LLM Top 10 controls,
- governance guardrails for accuracy, transparency, and accountability,
- evidence-driven assurance so stakeholders can approve go-live with a baseline and a roadmap.
Use Case B: Internal Copilots and Enterprise Knowledge Assistants
Here, the data protection risk increases sharply:
- confidential content exposure,
- accidental personal data inclusion,
- retention and audit obligations,
- vendor and subprocessor boundaries.
S3CURE/AI integrates:
- privacy-by-design data controls with security enforcement,
- governance approvals and monitoring,
- practical privacy and retention strategies where applicable.
Use Case C: AI-Enabled Decision Support (Higher Governance Burden)
Where AI influences decisions (risk scoring, HR screening, eligibility, prioritisation), governance must be stronger:
- accountability and oversight,
- bias and fairness evaluation (where relevant),
- explainability and documentation,
- risk classification aligned with regulatory expectations.
S3CURE/AI brings a structured governance and assurance model so these systems can be defended under scrutiny.
What “Good” Looks Like: Evidence, Not Assumptions
Executives and auditors typically want clarity on five questions:
- What AI systems do we have, and what do they do?
- What data do they use, and what are the privacy implications?
- How could they be attacked or manipulated, and what controls exist?
- Who is accountable for oversight and change?
- Can we prove controls work, repeatedly, over time?
S3CURE/AI is designed to answer those questions with:
- a mapped control baseline,
- a maintained evidence catalogue,
- testable requirements and monitoring,
- audit-ready reporting.
Enabling Innovation Without Losing Control
A recurring pattern in AI adoption is that overly restrictive governance creates Shadow AI; teams use unsanctioned tools because the approved path is too slow or unclear. The right strategy is not to block AI; it is to enable safely:
- sanctioned tools and architectures,
- clear policies and guardrails,
- user education and accountability,
- monitoring and continuous improvement.
S3CURE/AI supports this by providing a practical path to adoption that satisfies security, privacy, and governance requirements without paralysing delivery.
Closing: The Intersection Is the Strategy
Treating Cybersecurity, Data Protection, and AI Governance as separate workstreams is no longer viable. AI systems force convergence because the risks are intertwined:
- Security failures become privacy incidents.
- Privacy gaps become governance failures.
- Governance without technical controls becomes unenforceable.
The organizations that succeed will be the ones that can operationalize AI with:
standards-based controls, evidence-led assurance, and a single integrated operating model.
That is what DTS Solution’s S3CURE/AI is built to deliver.
Introducing DTS Solution’s S3CURE/AI: Standards-Led AI Governance and Security, Made Executable
S3CURE/AI is DTS Solution’s integrated service offering for organisations adopting AI and GenAI. It is designed to help teams enable AI safely, while producing the artefacts that leadership, customers, and regulators expect.
Standards-Based Backbone (Credibility and Auditability)
S3CURE/AI is intentionally standards-led to ensure defensible governance and repeatable delivery, aligned to:
- ETSI AI Security Baseline (cybersecurity requirements for AI models and systems)
- NIST AI Risk Management Framework (AI RMF) (governance, mapping, measuring, and managing AI risk)
- ISO/IEC AI terminology and lifecycle alignment (consistent definitions and management discipline)
- ISO/IEC 42001 alignment, where clients want a management system approach to AI governance
- OWASP Top 10 for LLM Applications and MITRE ATLAS to anchor AI security threats and controls
What clients receive (core artefacts):
- A documented control crosswalk (Standards ↔ DTS controls ↔ client requirements)
- An evidence catalogue (controls mapped to artefacts, tests, operational proofs, and ownership)
- Audit-ready reporting (traceability from requirements to implemented safeguards and monitoring)
This is the difference between “we think we’re safe” and “we can prove we’re safe.”
How S3CURE/AI Works: A Practical Three-Tier Delivery Model
To make adoption scalable and executive-friendly, S3CURE/AI is delivered as a structured, phased program. A practical model is:
S1 — Discover & Assess (Baseline Risk and Readiness)
Designed for organisations that need to move quickly but responsibly.
Typical activities
- AI use-case inventory and scope definition (systems, workflows, data, vendors)
- AI risk classification and governance readiness review (including EU AI Act-aligned thinking where relevant)
- Architecture and workflow threat modelling (AI-specific)
- Privacy and data protection impact assessment orientation (where personal data is in-scope)
- Initial control baseline mapped to standards
Deliverables
- AI risk and maturity assessment report
- Prioritised remediation roadmap
- Initial control crosswalk and evidence plan
Outcome
A defensible baseline and an implementation plan that leadership can fund and execute.
S2 — Design & Build (Controls, Guardrails, and Secure Architecture)
Designed for organisations moving from “assessment” to “implementation.”
Typical activities
- Security architecture review and secure-by-design guardrails for AI components (RAG, agents, tool integrations)
- Control implementation guidance for AI-specific threats (prompt injection, data leakage, insecure output handling, etc.)
- Privacy-by-design control embedding (retention, access controls, minimisation, vendor boundaries)
- Governance operating model definition (roles, approvals, escalation, change control)
- Assurance test plan design aligned to OWASP LLM Top 10 / MITRE ATLAS
Deliverables
- AI control design package (technical + governance)
- Threat model and mitigations mapped to implementation tasks
- Updated evidence catalogues and operating procedures
Outcome
Working controls that reduce real-world exploitability and enable confident deployment.
S3 — Assure & Operate (Ongoing Testing, Monitoring, and Evidence)
Designed for organisations that require continuous assurance and audit readiness.
Typical activities
- Validation testing and continuous control monitoring (including LLM/AI security testing programs)
- Evidence collection and reporting cadence (executive dashboards and audit packs)
- Incident readiness integration (AI-specific scenarios, triage, response playbooks)
- Governance refinement and periodic reviews (policy updates, model changes, vendor drift)
Deliverables
- Audit-ready assurance pack and recurring reporting
- Continuous improvement backlog and control effectiveness tracking
- Executive risk summaries and decision support artefacts
Outcome
AI systems remain safe, compliant, and governable as they evolve.
Real-World Use Cases Where the Intersection Matters
Use Case A: Public-Facing AI Assistant Using Open/Public Data
Even if no personal data is used, the system can still create risk through:
- prompt injection and malicious input patterns,
- insecure output handling (unsafe instructions, misleading claims),
- integrity issues in retrieval sources,
- logging and retention practices,
- operational controls (change management, monitoring, incident response).
S3CURE/AI addresses this by combining:
- workflow threat modelling + OWASP LLM Top 10 controls,
- governance guardrails for accuracy, transparency, and accountability,
- evidence-driven assurance so stakeholders can approve go-live with a baseline and a roadmap.
Use Case B: Internal Copilots and Enterprise Knowledge Assistants
Here, the data protection risk increases sharply:
- confidential content exposure,
- accidental personal data inclusion,
- retention and audit obligations,
- vendor and subprocessor boundaries.
S3CURE/AI integrates:
- privacy-by-design data controls with security enforcement,
- governance approvals and monitoring,
- practical privacy and retention strategies where applicable.
Use Case C: AI-Enabled Decision Support (Higher Governance Burden)
Where AI influences decisions (risk scoring, HR screening, eligibility, prioritisation), governance must be stronger:
- accountability and oversight,
- bias and fairness evaluation (where relevant),
- explainability and documentation,
- risk classification aligned with regulatory expectations.
S3CURE/AI brings a structured governance and assurance model so these systems can be defended under scrutiny.
What “Good” Looks Like: Evidence, Not Assumptions
Executives and auditors typically want clarity on five questions:
- What AI systems do we have, and what do they do?
- What data do they use, and what are the privacy implications?
- How could they be attacked or manipulated, and what controls exist?
- Who is accountable for oversight and change?
- Can we prove controls work, repeatedly, over time?
S3CURE/AI is designed to answer those questions with:
- a mapped control baseline,
- a maintained evidence catalogue,
- testable requirements and monitoring,
- audit-ready reporting.
Enabling Innovation Without Losing Control
A recurring pattern in AI adoption is that overly restrictive governance creates Shadow AI; teams use unsanctioned tools because the approved path is too slow or unclear. The right strategy is not to block AI; it is to enable safely:
- sanctioned tools and architectures,
- clear policies and guardrails,
- user education and accountability,
- monitoring and continuous improvement.
S3CURE/AI supports this by providing a practical path to adoption that satisfies security, privacy, and governance requirements without paralysing delivery.
Closing: The Intersection Is the Strategy
Treating Cybersecurity, Data Protection, and AI Governance as separate workstreams is no longer viable. AI systems force convergence because the risks are intertwined:
- Security failures become privacy incidents.
- Privacy gaps become governance failures.
- Governance without technical controls becomes unenforceable.
The organizations that succeed will be the ones that can operationalize AI with:
standards-based controls, evidence-led assurance, and a single integrated operating model.
That is what DTS Solution’s S3CURE/AI is built to deliver.
See also:
Dubai