01 · Visibility
Unknown or unmanaged AI use
AI tools can enter the organisation through individual users, SaaS products and supplier platforms, creating blind spots before governance teams are involved.
2-sec AI practice / AI Governance
Govern AI With Confidence - Without Slowing Innovation
Turn AI adoption into a controlled business capability. 2-sec helps you establish clear accountability, practical policies, proportionate controls and ongoing assurance so your organisation can use AI with greater confidence.
Practical governance for leadership, security, risk, compliance and technology teams.
Business-firstGovernance built around how AI is actually used
Risk-basedControls proportionate to impact and exposure
Framework-awareDesigned to support recognised standards and obligations
OperationalBuilt to work after the policy document is signed
The governance gap
AI is moving faster than most organisations’ controls
Teams are adopting copilots, generative AI, embedded AI features and third-party AI services faster than traditional security and governance processes were designed to handle. The challenge is not simply whether AI is allowed. It is knowing what is being used, what data is involved, who owns the risk, and what evidence demonstrates appropriate oversight.
02 · Accountability
Unclear ownership
Security, privacy, legal, compliance, procurement and business teams may all own part of the risk without a clear decision model connecting them.
03 · Control
Policies without enforcement
A policy alone does not govern AI. Effective governance connects rules to intake, risk assessment, technical controls, approvals, monitoring and evidence.
04 · Data
Sensitive information exposure
Prompts, uploads, model outputs and integrations can introduce confidentiality, privacy, intellectual property and data-handling concerns.
05 · Third parties
Supplier and model risk
Organisations need a repeatable way to evaluate AI-enabled vendors, platform changes, data terms, security posture and dependency risk.
06 · Assurance
Difficulty proving control
Customers, leadership and assurance stakeholders increasingly want evidence that AI risks are being identified, evaluated and managed consistently.
What is AI Governance?
AI governance is the system an organisation uses to direct, control and oversee how AI is selected, developed, procured, deployed and used. It connects policies, decision rights, risk assessment, technical and procedural controls, monitoring and evidence so people can make consistent decisions throughout the AI lifecycle.
Accountability
Defines who owns AI decisions, who approves higher-risk uses and who is responsible when controls, performance or outcomes need to be challenged or escalated.
Risk and Control
Identifies material AI risks and applies proportionate safeguards around data use, model behaviour, human oversight, security, suppliers and operational impact.
Compliance and Alignment
Connects AI use with applicable law, regulation, contractual obligations, internal policies and organisational principles so governance decisions are consistent and defensible.
Transparency and Evidence
Maintains the records, rationale, provenance and review evidence needed to explain how AI is used, how decisions are governed and whether controls are working.
What AI Governance Covers
Put a working governance system around the way your organisation uses AI
2-sec helps you establish or strengthen the governance layer around AI adoption, connecting the decisions people make with the policies, ownership, risk controls and evidence needed to support them.
- AI discovery and use-case inventory — identify where AI is used, by whom, for what purpose and with what data.
- AI governance gap assessment — assess current controls, ownership, policies and decision processes.
- AI policy and acceptable-use guidance — set clear, practical boundaries for employees and teams.
- Risk classification and assessment — apply proportionate review based on use case, data, autonomy and business impact.
- Roles, ownership and approval paths — define who decides, who reviews and who accepts risk.
- Third-party AI assurance — strengthen procurement and supplier review for AI-enabled services.
- Control and evidence mapping — connect governance requirements to security, privacy, compliance and assurance evidence.
- Ongoing governance support — keep controls useful as AI adoption, platforms and expectations change.
What you receive
A governance model your teams can actually operate
Agree the outputs at the start of the engagement. The exact package reflects your AI footprint, existing controls and priorities, but the aim is the same: turn governance principles into practical mechanisms people can use.
Operating model
AI governance framework
A practical structure defining how AI decisions are made, governed, reviewed and escalated across the organisation.
Accountability
Roles and decision rights
Clear ownership for approvals, risk acceptance, policy, technical assurance, supplier review and ongoing oversight.
Working rules
Policies and guidance
Governance requirements translated into usable rules for approved AI use, information handling, human review and exceptions.
Risk model
Assessment and classification approach
A repeatable way to determine which AI uses need deeper review, stronger controls or leadership attention.
Roadmap
Prioritised governance actions
A clear view of gaps, dependencies and next steps so governance can mature without trying to solve everything at once.
A practical lifecycle
Govern the full AI lifecycle, not just the policy
Effective AI governance is a repeatable decision system. We structure the work around five connected stages so governance remains useful as new tools and use cases appear.
Discover
Identify AI tools, use cases, data flows, suppliers, owners and business dependencies.
Define
Set governance principles, roles, policies, risk criteria, approval thresholds and evidence requirements.
Control
Translate policy into practical technical, procedural, contractual and people controls.
Assure
Review higher-risk use cases, suppliers and controls, then capture defensible evidence of oversight.
Improve
Monitor changes, exceptions, incidents and new use cases so governance evolves with adoption.
Connected to the controls around it
Make governance part of the way your organisation already manages risk
AI governance works best when it strengthens existing security, privacy, procurement, assurance and operational processes rather than creating a separate bureaucracy beside them.
Security
Protect systems and access
Connect AI approvals with identity, permissions, integrations, data access, misuse scenarios and resilience controls.
Information
Set usable data boundaries
Align AI use with privacy, confidentiality, data classification and information-handling requirements.
Suppliers
Govern third-party AI
Build AI-specific questions into procurement, due diligence, contract review and ongoing supplier assurance.
Assurance
Test where evidence matters
Use risk assessment and technical testing when governance decisions depend on evidence about actual AI behaviour or control effectiveness.
Oversight
Review change over time
Track new use cases, exceptions, incidents, supplier changes and control performance so governance stays relevant as AI adoption evolves.
The wider AI practice
Use governance to connect risk, policy, assurance and certification readiness
AI Governance is the organising layer of 2-sec’s AI practice. It gives your organisation the responsibilities, decision routes and oversight needed to act on risk findings, turn policy into working rules and decide where stronger evidence or a formal management system is needed.
See the exposure
AI Risk Assessment
Identify where AI introduces material risk to systems, data and business activity, then prioritise what needs attention.
Set the rules
AI Policy Development
Translate governance decisions into clear guidance around approved tools, data handling, human review and responsibilities.
Formalise the system
ISO 42001 Readiness
Build and evidence an AI management system that can support independent ISO 42001 certification within an agreed scope.
Test the boundaries
LLM and Prompt Testing
Challenge AI applications with realistic manipulation attempts and bring observed technical evidence back into governance decisions.
Questions before you start
Know what to expect from AI governance
What is AI governance?
AI governance is the system of policies, roles, decision rules, risk assessments, controls and assurance activities used to guide how an organisation selects, develops, buys, deploys and uses AI.
Do we need AI governance if we only use tools such as Microsoft Copilot or ChatGPT?
Yes. Even when you are not building your own models, AI use can affect confidential information, personal data, intellectual property, supplier risk, decision quality and customer expectations. Governance should be proportionate to how the tools are used.
Can AI governance fit with our existing security and compliance programmes?
It should. A practical approach reuses existing security, privacy, procurement, risk and assurance processes wherever possible, then adds AI-specific decision points where the existing controls are not sufficient.
Which AI governance frameworks can 2-sec support?
The right reference points depend on your organisation, markets and objectives. Work can be aligned to recognised AI governance and risk-management approaches, as well as your existing security, privacy and compliance framework obligations.
Where should we start?
Start with visibility. Establish what AI is already being used, which business processes and data are involved, and where current policies or controls do not match actual behaviour. From there, governance can be prioritised around the highest-value and highest-risk gaps.
Start with the governance gap
Make AI adoption easier to defend—and easier to scale.
Speak with 2-sec about your current AI use, governance maturity and risk priorities. We’ll help you identify where practical controls can create the greatest confidence without adding unnecessary friction.