No visibility of AI use
Teams adopt AI tools and build AI features without central awareness.
05 / Technology & Engineering
AI Security, Governance and Ethics
As AI moves into core operations, organisations need to answer hard questions: which AI systems are in use, what risks they carry, who is accountable, how they are secured and whether they treat people fairly. We help build governance and security that are practical enough to be followed and strong enough to matter.
At a glance
- Service
- AI Security, Governance and Ethics
- Pillar
- Technology & Engineering
- Typical starting point
- AI inventory and risk review
- Typical first phase
- 4 to 8 weeks for a governance foundation
- Key technologies
- Artificial Intelligence, Large Language Models, Agentic AI
Durations depend on scope, data readiness and integration needs.
What is AI Governance?
Short answer
AI security, governance and ethics is the set of policies, processes, roles, technical controls and review practices an organisation uses to ensure its AI systems are secure, lawful, fair, transparent, reliable and accountable across their lifecycle, from use case approval through development, deployment, monitoring and retirement.
AI governance is sometimes seen as a brake on innovation. Done well, it does the opposite: clear rules about acceptable uses, risk tiers and required controls let teams move faster with confidence, while high-risk uses receive appropriate scrutiny.
Our work combines governance design with technical security. On the governance side we create AI policies, inventories, risk assessment methods, roles and review processes aligned with recognised frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001, and with data protection obligations including India's Digital Personal Data Protection Act. On the technical side we assess and address AI-specific threats such as prompt injection, data leakage, model manipulation and insecure agent tool use.
Business problems
What problems does AI Governance solve?
These are the situations where organisations most often find this service worthwhile.
Unclear accountability
When an AI system causes harm or errors, responsibility is undefined.
AI-specific security threats
Prompt injection, data leakage and model abuse are not covered by existing security practices.
Regulatory uncertainty
Leaders are unsure how data protection laws and emerging AI rules apply.
Fairness and reputational risk
AI decisions may treat groups unfairly or cause public concern.
Customer and partner due diligence
Clients ask for evidence of responsible AI practices during procurement.
Use cases
Where can AI Governance be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| AI policy | Acceptable use policies for employees and principles for AI development. |
| AI inventory and risk tiering | Cataloguing AI systems and classifying them by risk to set required controls. |
| Impact assessments | Structured assessment of AI systems affecting people, including fairness and privacy. |
| Security testing | Red teaming of LLM applications and agents for injection, leakage and misuse. |
| Vendor AI assessment | Evaluating third-party AI products for security, data use and compliance. |
| Standards readiness | Preparing management systems aligned with ISO/IEC 42001 or similar frameworks. |
Who should consider it
- Organisations scaling AI across teams
- Regulated industries using AI in customer-facing decisions
- Technology companies selling AI-enabled products
- Boards and leadership teams responsible for AI risk
How AARHIT approaches it
How we deliver AI Governance
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
AI inventory and discovery
We identify AI systems and tools in use or in development, including third-party and embedded AI.
Output: AI inventory.
Stage 2 of 6
Risk framework
A risk tiering method and required controls per tier are designed, aligned with relevant frameworks and laws.
Output: AI risk framework.
Stage 3 of 6
Policies and roles
Policies, principles, decision rights, review boards and responsibilities are defined.
Output: governance policies and operating model.
Stage 4 of 6
Assessments
Priority systems are assessed for security, privacy, fairness, transparency and reliability.
Output: assessment reports with remediation plans.
Stage 5 of 6
Technical controls and testing
Security controls are implemented and AI systems are tested, including adversarial testing.
Output: implemented controls and test results.
Stage 6 of 6
Monitoring and continuous governance
Ongoing monitoring, periodic reviews, incident processes and training are established.
Output: sustained governance routines.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Frameworks | NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, OWASP Top 10 for LLM Applications |
| Security testing | LLM red teaming tools, prompt injection test suites, adversarial testing |
| Guardrails | Input and output filters, PII detection, content safety services, policy engines |
| Fairness and explainability | Fairlearn, SHAP, model cards, datasheets for datasets |
| Governance tooling | AI registries, risk registers, documentation templates, audit logging |
| Privacy | Data minimisation, anonymisation, consent management aligned with the DPDP Act |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- Visibility and control over AI use across the organisation
- Clear accountability for AI systems
- Reduced security, privacy and fairness risk
- Evidence of responsible practices for customers and regulators
- Faster approval of low-risk AI use cases
How long does implementation generally take?
Typical timeline
A governance foundation, including inventory, risk framework, core policies and assessment of priority systems, typically takes 4 to 8 weeks. Security testing of an individual AI application usually takes 2 to 4 weeks. Governance then continues as an ongoing routine.
What determines complexity?
- Number of AI systems and teams
- Regulatory exposure and sectors
- Use of AI in decisions affecting individuals
- Third-party AI dependence
- Existing risk and security maturity
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Proportionate controls
Applying heavy review to every AI use slows innovation and encourages workarounds. Tier by risk.
Integrate with existing processes
AI governance works best inside existing risk, security, privacy and procurement processes.
Keep up with change
AI regulation and threats evolve quickly. Build periodic review into governance.
Governance needs legal input
Our technical and governance work complements, and does not replace, advice from qualified legal counsel.
| Risk to consider | How we manage it |
|---|---|
| Policies that are ignored | Practical, role-specific guidance, training and approved tools that make compliance easy. |
| Unassessed shadow AI | Discovery, clear approved alternatives and simple registration processes. |
| Novel attacks on AI systems | Regular adversarial testing and monitoring of emerging threat guidance. |
| Harm to individuals | Impact assessments, human oversight for consequential decisions and complaint mechanisms. |
FAQ
Questions about AI Governance
What is AI governance?
AI governance is the framework of policies, roles, processes and controls that ensures an organisation's AI systems are developed and used responsibly, securely and in line with laws and values, with clear accountability.
What is prompt injection?
Prompt injection is an attack where malicious instructions are placed in content an AI system processes, such as a web page, email or document, to make the system ignore its instructions, reveal data or take unintended actions.
How does India's DPDP Act affect AI projects?
The Digital Personal Data Protection Act sets obligations for processing personal data, including lawful purpose, consent or legitimate uses, data minimisation, security safeguards and rights of individuals. AI systems using personal data must comply. We address these requirements in design and assessments alongside your legal advisers.
What is ISO/IEC 42001?
ISO/IEC 42001 is an international standard specifying requirements for an AI management system, helping organisations establish, implement, maintain and improve responsible AI practices.
Does AI governance slow down innovation?
Poorly designed governance can. Risk-tiered governance speeds up approval of low-risk uses, gives teams clear guidance and focuses scrutiny where potential harm is greatest.
Do you provide legal advice on AI regulation?
No. We provide technical, risk and governance expertise and work alongside your legal counsel, who should advise on legal interpretation and obligations.
Next step
Considering AI Governance for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.