Solution · AI Governance & Security
Adopt AI with Governance Boards and Regulators Can Rely On.
The Challenge
AI tools are entering operations faster than policies can follow, and AI-driven threats such as deepfakes and automated phishing are already in use.
The Outcome
- Governed AI adoption
- Protected data and decisions
- Workforce alert to AI-driven threats
Executive Summary
- 01AI governance starts with knowing every AI use, sanctioned or not, and who owns it.
- 02Risk is judged per use case, so scrutiny goes where harm to people, data or decisions is plausible.
- 03Controls attach to data, access and model pipelines, and staff are trained against deepfake and phishing fraud.
The Challenge
AI tools are entering operations faster than policies can follow, and AI‑driven threats such as deepfakes and automated phishing are already in use.
Boards want the benefits of AI without new exposure of data, decisions or reputation.
The DAAKYI Approach
DAAKYI helps define AI governance, assess the risks of AI use cases and apply security controls to the data and systems involved.
Scope is agreed per engagement.
Delivered Through These DAAKYI Services
- 01
AI use-case risk review
- 02
AI governance and policy
- 03
Data and access controls for AI
- 04
Awareness of AI-driven threats
In Depth
How the Solution Comes Together
Inventory Comes Before Any Policy
You cannot govern AI you cannot see. Employees paste documents into public chatbots, business units subscribe to AI features inside existing software, and developers call model APIs from internal code. Each of these is an AI use case with its own data flows. An inventory records the tool, the owner, the model provider, the data classes involved and the decisions the output influences. It also captures shadow use, usually through procurement records, network telemetry and short staff surveys. Without this register, risk reviews cover only what people chose to disclose, and policy rests on guesses.
Risk Depends on the Use Case
The same model carries different risk in different roles. A drafting assistant for marketing copy is low stakes. A model that ranks loan applicants, flags suspicious transactions or summarizes citizen records affects people and obligations. Reviews therefore weigh impact on individuals, sensitivity of data, degree of human oversight, explainability and dependence on a third-party provider. Risk tiers follow from these factors and set how much evidence a team must supply before launch. Tiering keeps low-risk experiments fast and puts scrutiny where harm is plausible. NIST AI RMF 1.0 and ISO 31000 give useful vocabulary for the scoring.
Controls Follow the Data
Most AI exposure is data exposure. Prompts, uploaded files, training sets, retrieval indexes and logs can all hold personal or confidential information. Controls start with classification: which data classes may reach which tools. Enterprise agreements with providers should state retention, training use and breach notification. Access to models and connectors follows least privilege, with identity-based authentication and logging of prompts and outputs where lawful. Encryption protects data in transit and at rest, and retrieval systems must respect the same permissions as the source documents. Otherwise an assistant becomes a path around existing access rules.
You cannot govern AI you cannot see, and you cannot secure AI that has no owner.
Capability Matrix
What the Service Includes
AI Governance
Policies, roles and approval paths.
Use‑Case Risk Review
Risks assessed before deployment.
Data Protection for AI
Controls on data used by AI.
AI Threat Awareness
Deepfakes and automated phishing.
Operating Model
Delivered from the GUIDE · SECURE · OPERATE Pillar
Pillar position
GUIDE · SECURE · OPERATE
Delivered across all three pillars
Engagement model
Advisory Engagements
Fixed-scope assessments, strategy, compliance and testing.
Best for · Defined questions with a clear deliverable
Threat Scenarios
What It Looks Like in Practice
Deepfake Voice Payment Fraud
How It Shows Up
A finance officer receives a call or video message that sounds like an executive and demands an urgent, confidential transfer.
Response
Require out-of-band callback verification and dual approval for payments, and train approvers to treat voice and video as unverified.
Staff Paste Data into Public Chatbots
How It Shows Up
Network logs show regular traffic to consumer AI sites, and staff admit sharing contracts or customer records for summaries.
Response
Publish clear acceptable-use rules, provide an approved enterprise alternative and apply data loss prevention to prompts.
Prompt Injection Through Documents
How It Shows Up
An internal assistant starts disclosing content or taking actions that no user requested after reading an external file.
Response
Treat retrieved content as untrusted, restrict tool permissions and require human confirmation for consequential actions.
Flawed Automated Decision
How It Shows Up
A scoring model produces outcomes that staff cannot explain, or complaints cluster around one customer group.
Response
Require documented testing, human review of adverse outcomes and a monitoring plan that detects drift.
| Scenario | How It Shows Up | Response |
|---|---|---|
| Deepfake Voice Payment Fraud | A finance officer receives a call or video message that sounds like an executive and demands an urgent, confidential transfer. | Require out-of-band callback verification and dual approval for payments, and train approvers to treat voice and video as unverified. |
| Staff Paste Data into Public Chatbots | Network logs show regular traffic to consumer AI sites, and staff admit sharing contracts or customer records for summaries. | Publish clear acceptable-use rules, provide an approved enterprise alternative and apply data loss prevention to prompts. |
| Prompt Injection Through Documents | An internal assistant starts disclosing content or taking actions that no user requested after reading an external file. | Treat retrieved content as untrusted, restrict tool permissions and require human confirmation for consequential actions. |
| Flawed Automated Decision | A scoring model produces outcomes that staff cannot explain, or complaints cluster around one customer group. | Require documented testing, human review of adverse outcomes and a monitoring plan that detects drift. |
Outcomes
What Your Institution Gains
- Governed AI adoption
- Protected data and decisions
- Workforce alert to AI-driven threats
Methodology
The Method, Step by Step
- 1
Discover AI Use
Build the register of sanctioned and unsanctioned AI use. Interview business owners, review procurement and cloud billing, and examine network traffic to model providers. Record purpose, data classes, vendor, users and the decisions affected. Assign a named owner to every entry so that later reviews have someone accountable.
- 2
Tier the Risk
Score each use case against impact on people, data sensitivity, autonomy of the output, regulatory exposure and vendor dependence. Assign a tier. Higher tiers require fuller evidence, such as testing results, privacy assessment and human oversight design. Lower tiers follow a short checklist so teams are not slowed unnecessarily.
- 3
Set Decision Rights
Define who approves what. Write an acceptable-use policy, name an accountable executive, and set an approval path matched to each tier. Establish a review forum that includes security, legal, privacy and the business. Record decisions and exceptions so that the board can see how AI risk is being accepted.
- 4
Apply Technical Controls
Implement the controls the review demands. These include data loss prevention for prompts, restricted connectors, key management, segregation of environments, output filtering and logging. For models built internally, add adversarial testing and supply-chain checks on model files and dependencies. Verify each control works before approving launch.
- 5
Monitor and Refresh
Review live use continuously. Watch for new tools, drift in model behavior, policy exceptions and incidents involving AI. Refresh the register and tiers on a set cycle and after major vendor changes. Train staff on current deepfake and phishing techniques, and rehearse the escalation path for suspected AI-enabled fraud.
How It Works
How AI Governance & Security Comes Together
- 01Inventory
- 02Assess
- 03Govern
- 04Protect
Regulatory & Industry Context
The Obligations This Service Supports
Governance draws on ISO/IEC 27001 and data protection obligations under Act 843 and GDPR.
Frameworks DAAKYI helps clients implement
- ISO/IEC 27001
- Ghana Act 843
- GDPR
Framework references describe client implementation support, not DAAKYI certifications.
Deliverables and Roles
What You Receive, and Who Does What
| Deliverable | Purpose |
|---|---|
| AI Use‑Case Register | Gives the organization one current list of AI tools, owners, data classes and affected decisions. |
| Risk Tiering Model | Sets consistent criteria for deciding how much review each use case needs. |
| AI Acceptable‑Use Policy | Tells staff which tools and data combinations are permitted, restricted or prohibited. |
| Control Requirements by Tier | Specifies the technical and procedural safeguards each risk tier must show before launch. |
| Board Reporting Pack | Summarizes AI exposure, exceptions and incidents in terms directors can act on. |
| Role | Responsibility |
|---|---|
| Accountable Executive (Client) | Owns AI risk acceptance and ensures the governance forum has authority and resources. |
| Use‑Case Owner (Client) | Answers for a specific AI system's purpose, data, outputs and ongoing performance. |
| Data Protection Officer (Client) | Assesses privacy impact and confirms that personal data use meets legal obligations. |
| Security Architect (Provider) | Designs and tests the access, data and pipeline controls that each tier requires. |
Maturity Model
Where Organizations Typically Stand
Stage 1
Unmanaged
AI use is scattered and invisible to security. No policy exists, and data leaves through personal accounts.
Stage 2
Documented
A policy and register exist, but reviews are inconsistent and depend on individual awareness.
Stage 3
Governed
Tiered reviews, named owners and approval paths operate, and technical controls match each tier.
Stage 4
Assured
Monitoring, testing and board reporting run continuously, and the program is audited against recognized standards.
Frameworks and Regulation
The Standards That Shape This Work
NIST AI RMF 1.0
A voluntary framework organized around Govern, Map, Measure and Manage functions. It structures how AI risks are identified, evaluated and treated.
ISO/IEC 42001
A management system standard for AI. It supports policy, roles, risk treatment and continual improvement that an auditor can examine.
NIST CSF 2.0
Its Govern function places cybersecurity risk decisions with leadership. AI controls can be mapped across Identify, Protect, Detect, Respond and Recover.
Buyer Guidance
Choosing the Right Provider
What to look for
- Look for a provider that treats AI governance and AI security as separate disciplines that must be joined.
- Look for risk reviews that differ by use case instead of one generic questionnaire.
- Look for experience applying data protection law to AI prompts, training data and vendor contracts.
- Look for deliverables you can audit, such as registers, tier criteria and recorded decisions.
Questions to ask a provider
- How do you find AI use that the business has not disclosed?
- How do you decide which use cases need deep review?
- Which technical tests do you run on assistants that read internal documents?
- How will our policy be mapped to ISO/IEC 42001 or NIST AI RMF?
Technical Detail
For Architects and Security Leads
Prompt Injection and Retrieval Permissions
Language models cannot reliably separate instructions from data. A retrieved web page, email or document can contain hidden text that tells the model to reveal data or call a tool. Defenses are layered. Treat all retrieved content as untrusted. Limit the tools an agent can invoke and require human confirmation for actions that move money, change records or send external messages. Enforce document-level permissions at retrieval time, not only at indexing. Scope API tokens narrowly and rotate them. Log prompts, tool calls and outputs for investigation. Test with adversarial inputs before launch and after each model or prompt change. OWASP publishes guidance on LLM application risks.
Model Supply Chain and Data Pipeline Integrity
Models are software artifacts with dependencies. Downloaded weights can carry malicious serialized code, and poisoned training or fine-tuning data can shift behavior quietly. Verify the source and checksum of every model file, prefer safe serialization formats, and scan in an isolated environment before use. Keep training data lineage: where it came from, who changed it and under what authority. Separate training, testing and production environments, and restrict write access to datasets. Map these assets to the technological controls in ISO/IEC 27001 Annex A, such as secure development, configuration management and logging, so AI pipelines sit inside the existing control framework.
Glossary
Key Terms
- Shadow AI
- AI tools used for work without approval or visibility from security and governance teams.
- Prompt Injection
- An attack that hides instructions in input or retrieved content to make a model disclose data or take unintended actions.
- Model Drift
- A gradual change in model performance or behavior as data, usage or the underlying model changes.
- Human in the Loop
- A design where a person reviews or approves model output before it triggers a consequential action.
Related
Explore Connected Capabilities
Proof Points
What We Can Stand Behind
- Every stage of DAAKYI's delivery methodology produces documented, auditable deliverables.
FAQ
Questions Leaders Ask
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