ISO/IEC 42001 — Artificial Intelligence Management System (AIMS)
Provides requirements and guidance for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System.
AI is becoming part of business applications, employee workflows, customer experiences, and critical decision processes. We help organizations understand where AI is being used, establish practical governance, identify security risks, and implement appropriate safeguards for models, agents, data, APIs, and connected systems.
Governance defines the rules. Establish ownership, acceptable-use requirements, risk processes, data-handling expectations, accountability, and organizational guardrails for AI.
Security tests the technology. Evaluate models, applications, agents, data flows, integrations, permissions, and AI-specific attack surfaces to identify technical security weaknesses.
Artificial intelligence is rapidly moving from experimentation into everyday business operations. Organizations are using large language models, AI agents, machine-learning systems, retrieval-augmented applications, and AI-enabled software to automate tasks, process information, support employees, and improve decision-making.
As AI becomes connected to sensitive information, internal applications, APIs, databases, business workflows, and third-party services, organizations need more than an AI policy. They need visibility into their AI environment, clear accountability, appropriate security controls, and a practical way to continuously manage AI-related risk.
Responsible AI adoption requires both governance and security. Governance establishes how AI should be used and who is accountable. Security evaluates whether AI systems, integrations, data flows, and technical controls can withstand realistic threats.
AI Governance vs. AI Security
AI governance and AI security are closely related, but they address different questions. Governance focuses primarily on organizational decision-making, accountability, risk management, and acceptable use. AI security focuses on protecting the technology and its surrounding environment from security threats.
01 / GOVERN
AI Governance Consulting
Helps organizations establish the rules, ownership, processes, and accountability required to adopt and manage AI responsibly.
02 / SECURE
AI Security Consulting
Helps organizations identify and reduce technical security risks affecting AI applications, models, agents, data, integrations, and supporting infrastructure.
Who Is Responsible for What?
Consulting does not replace organizational ownership. The organization remains responsible for business decisions, regulatory obligations, risk acceptance, and implementation of controls. A consulting engagement provides assessment, expertise, recommendations, testing, and implementation assistance according to the agreed scope.
| Area | CyberSafeHaven | Client Organization |
|---|---|---|
| AI Governance | Assess current governance and recommend policies, processes, roles, and controls. | Approve policies, assign ownership, make business decisions, and accept or treat risk. |
| AI Discovery | Identify AI assets, applications, models, vendors, integrations, and relevant data flows within the agreed scope. | Provide access, business context, system information, and validation of identified assets. |
| Risk Assessment | Assess risks and provide documented findings and recommendations. | Determine risk acceptance, prioritization, and remediation ownership. |
| Security Testing | Perform authorized testing within the agreed scope and document findings. | Authorize testing, provide required access, and own remediation of identified weaknesses. |
| Security Controls | Recommend or assist with implementation where included in the engagement. | Own production implementation, operational maintenance, and ongoing effectiveness. |
| Risk Acceptance | Provide objective risk information and recommendations. | Retain final responsibility for accepting or treating organizational risk. |
Emerging AI Security Risks
AI systems can introduce security considerations that extend beyond traditional application and infrastructure security. The relevant risks depend on the architecture, data, model, permissions, integrations, and business purpose of each system.
01
Prompt Injection
Malicious or untrusted instructions may attempt to influence
model behavior or bypass intended instructions and security
boundaries.
02
Sensitive Data Exposure
Confidential or personal information may be exposed through
prompts, outputs, logs, retrieval systems, integrations,
or connected data sources.
03
Excessive AI Permissions
AI agents with excessive access to applications, databases,
APIs, or tools can increase the impact of compromised or
manipulated interactions.
04
Insecure AI Integrations
APIs, plugins, tools, external services, and application
integrations can introduce additional attack paths.
05
Data Poisoning
Manipulated training, fine-tuning, retrieval, or supporting
datasets can affect model integrity and behavior.
06
Unauthorized AI Usage
Unapproved AI services can create unknown data flows,
third-party dependencies, and governance gaps.
07
Model & Supply-Chain Risk
Models, datasets, libraries, APIs, plugins, and external AI
providers create dependencies that should be identified
and evaluated.
08
Unsafe AI Actions
AI agents may perform downstream actions that create security
or operational consequences if authorization, validation,
and control mechanisms are insufficient.
CyberSafeHaven AI Governance & Security Services
Our consulting approach can be tailored to an organization's AI maturity, business objectives, regulatory environment, and technical architecture. Services can be delivered as individual assessments or as part of a broader AI governance and security program.
01 / DISCOVER
AI Asset & Data-Flow Discovery
Identify AI applications, models, agents, datasets, APIs, vendors, integrations, and supporting services. Where appropriate, map how sensitive and business critical information moves through AI systems.
02 / GOVERN
AI Governance Framework & Policies
Establish practical governance structures defining acceptable AI use, ownership, accountability, approval requirements, data handling, risk classification, and review processes.
03 / ASSESS
AI Risk Assessment
Evaluate AI systems based on their business purpose, data sensitivity, permissions, architecture, integrations, threat exposure, and potential business impact.
04 / PROTECT
AI Security Guardrails
Recommend technical and organizational controls that help protect AI applications, models, data, agents, APIs, and connected tools.
05 / TEST
AI Security Testing & Adversarial Simulation
Conduct authorized security testing to evaluate how AI systems respond to realistic malicious or unexpected inputs and interactions.
06 / IMPROVE
Remediation Roadmap & Security Improvement
Translate assessment findings into prioritized recommendations covering governance, architecture, controls, processes, monitoring, and future assessments.
When Should AI Governance or AI Security Be Required?
Not every AI use case requires the same level of governance or technical assessment. The appropriate level should depend on factors such as the sensitivity of the data, business impact, system permissions, external exposure, regulatory requirements, and the actions the AI system can perform.
AI GOVERNANCE
Governance should be considered when:
- AI adoption is becoming widespread across the organization.
- Employees use AI with company or customer data.
- AI systems influence important business decisions.
- The organization needs formal AI ownership and accountability.
- Regulatory, contractual, privacy, or industry requirements apply.
- Multiple AI vendors or applications are being used.
AI SECURITY
Security assessment should be considered when:
- AI systems process sensitive or confidential data.
- AI applications are externally accessible.
- AI agents can access tools, APIs, databases, or business systems.
- AI outputs can trigger business or operational actions.
- Custom models, RAG systems, or AI applications are being developed.
- Material architecture, model, data, or integration changes occur.
AI Governance & Security Frameworks
Organizations adopting artificial intelligence should consider recognized standards, risk-management frameworks, application security guidance, government cybersecurity guidance, and applicable AI regulations. These resources can provide structure for managing AI risk, defining controls, establishing accountability, and improving security throughout the AI lifecycle.
NIST AI Risk Management Framework (AI RMF)
Provides a voluntary framework for organizations to manage risks associated with artificial intelligence and incorporate trustworthy and responsible practices into AI design, development, deployment, and use.
OWASP AI Security Guidance
Provides security guidance for AI and generative AI applications, including risks such as prompt injection, insecure output handling, sensitive information disclosure, excessive agency, and other AI-specific security concerns.
NCSC — UK
The UK National Cyber Security Centre provides secure-by-design guidance for AI systems, covering security throughout the AI lifecycle, including design, development, deployment, operation, maintenance, security testing, supply-chain considerations, and monitoring.
CERT-In — India
India's Computer Emergency Response Team provides cybersecurity guidance and advisories relevant to organizations managing AI-enabled systems, cybersecurity risks, incident preparedness, monitoring, and protection of digital environments.
CISA — United States
The U.S. Cybersecurity and Infrastructure Security Agency provides guidance supporting secure-by-design practices, AI security, critical infrastructure protection, and management of emerging cybersecurity risks associated with AI-enabled technologies.
ENISA — European Union
The European Union Agency for Cybersecurity publishes guidance and research addressing cybersecurity risks associated with artificial intelligence, machine learning, AI supply chains, and secure AI adoption.
EU AI Act — European Union
The EU AI Act establishes a risk-based regulatory framework for artificial intelligence and introduces obligations that vary according to the type and risk level of an AI system, including requirements relating to governance, transparency, security, documentation, and risk management.
MITRE ATLAS
MITRE ATLAS provides a knowledge base of adversary tactics and techniques relevant to machine-learning and AI-enabled systems and can support threat modeling, security testing, and adversarial risk assessment.
NIST Cybersecurity Framework (CSF)
Provides a broader cybersecurity risk-management structure that can complement AI-specific frameworks by helping organizations identify, protect, detect, respond to, and recover from cybersecurity risks affecting AI-enabled environments.
OECD AI Principles
Provides internationally recognized principles for responsible AI, including considerations around trustworthy AI, transparency, robustness, security, safety, accountability, and human-centered values.
AI Governance & Security Framework Crosswalks
Crosswalk approaches can help organizations map AI governance and security practices to multiple frameworks, standards, controls, and regulatory expectations rather than managing each requirement in isolation.
A Practical AI Governance & Security Roadmap
AI security is not a one-time exercise. A practical program should evolve as AI systems, data, integrations, business requirements, and threats change.
01
Discover
02
Assess
03
Govern
04
Protect
05
Test
06
Monitor
07
Improve
01
Discover — Know What You Have
Identify AI applications, models, agents, datasets, APIs, vendors, integrations, and relevant data flows within the agreed scope.
02
Assess — Understand the Risk
Evaluate AI systems based on business purpose, information sensitivity, architecture, permissions, integrations, threat exposure, and potential impact.
03
Govern — Establish Accountability
Define AI policies, ownership, approval processes, risk classification, acceptable-use requirements, and accountability.
04
Protect — Apply Appropriate Controls
Apply security and governance safeguards covering identity, access, data, integrations, AI actions, monitoring, and technical guardrails.
05
Test — Challenge the Defenses
Perform authorized security testing against relevant AI attack surfaces including prompt injection, information disclosure, agent interactions, and model-related weaknesses.
06
Monitor — Watch for Change
Reassess AI systems when models, data, permissions, vendors, integrations, or functionality materially change.
07
Improve — Evolve With AI
Use findings, testing results, incidents, changes, and evolving requirements to continuously improve AI governance and security controls.
Scope, Responsibilities & Limitations
Clear engagement boundaries are important for AI security consulting. The exact services, systems, environments, testing methods, and deliverables should be defined in the engagement scope before work begins.
CyberSafeHaven's Role
Depending on the agreed engagement, CyberSafeHaven may assess AI environments, identify risks, perform authorized security testing, develop governance frameworks, recommend controls, provide remediation guidance, and support implementation activities.
Client's Responsibility
The client retains responsibility for providing authorization and access, validating business context, implementing agreed controls, operating production systems, maintaining policies, and making final decisions regarding risk acceptance and AI usage.
Engagement Boundaries
Testing is performed only against systems and environments explicitly authorized and included in the agreed scope. Production-impacting activities should be separately authorized and controlled.
Ongoing Operations
Unless specifically contracted as a managed service, consulting does not automatically include continuous monitoring, 24x7 incident response, production administration, or ongoing operation of security controls.
Responsible AI Without Slowing Innovation
Effective AI governance should provide teams with a clear path to responsible adoption rather than creating unnecessary restrictions.
When employees understand approved AI practices, developers know security expectations, security teams have visibility into AI dependencies, and leadership understands organizational risk, AI adoption can become more structured and informed.
The objective is not to eliminate AI risk. It is to understand the risks that matter, establish appropriate accountability and safeguards, test whether those safeguards work, and improve them as AI capabilities and business use cases evolve.
Frequently Asked Questions
What is AI Governance Consulting?
AI Governance Consulting helps organizations establish policies, ownership, accountability, risk-management processes, approval requirements, and responsible-use practices for AI systems.
What is AI Security Consulting?
AI Security Consulting evaluates the technical security of AI applications, models, agents, data flows, integrations, permissions, and supporting infrastructure.
What is the difference between AI governance and AI security?
Governance primarily establishes how AI should be used, who is accountable, and how AI-related risk should be managed. Security focuses on identifying and reducing technical vulnerabilities and attack paths affecting AI systems and their surrounding environment.
Can AI security testing identify prompt injection risks?
Authorized security testing can evaluate how an AI application responds to adversarial or untrusted instructions and whether its controls appropriately protect system instructions, data, tools, and connected resources.
Does AI Governance Consulting make an organization compliant?
Governance consulting can help establish policies, processes, controls, documentation, and accountability relevant to applicable requirements. Compliance depends on the organization's specific obligations and how those requirements are implemented and maintained.
Does CyberSafeHaven operate our AI security controls?
Consulting engagements generally focus on assessment, advisory, testing, and implementation assistance within the agreed scope. Continuous monitoring, operational administration, or managed security operations would need to be explicitly included as separate services.
When should an AI security assessment be performed?
Security assessment should be considered before deploying higher-risk AI systems and repeated when there are material changes to models, data, permissions, integrations, vendors, or system functionality.
Who is responsible for fixing identified AI security issues?
The client organization normally retains ownership of remediation and production changes. CyberSafeHaven can provide remediation recommendations and, where included in the engagement, assist with implementation.
Build AI With Governance. Secure AI With Confidence.
AI adoption does not have to mean choosing between innovation and security. Organizations can establish clear governance, understand their AI environment, identify meaningful risks, and implement safeguards that evolve with the technology.
CyberSafeHaven helps organizations discover AI assets and data flows, establish practical governance, assess AI security risks, implement appropriate guardrails, and test AI-powered systems against relevant threats.
Whether your organization is introducing its first enterprise AI application or scaling AI agents across business operations, a structured governance and security approach can provide the visibility and accountability needed for responsible adoption.
CYBERSAFEHAVEN · GOVERN AI · SECURE AI