Solutions · Gen AI Governance
Gen AI Governance & Compliance Services
Secure. Compliant. AI-Ready.
CyNtell designs the acceptable-use policies, governance frameworks, and data controls that let your organization deploy generative AI and LLMs without exposing itself to data-privacy, IP, or regulatory risk.
Governed AI Deployment
Policy-approved · Data-governed · Risk-assessed
Tested against MITRE ATLAS & NIST AI RMF
Policy, data governance, and impact assessment all feed one outcome: AI your organization can actually stand behind.
Gen AI governance is the set of policies, controls, and assessments that let an organization deploy generative AI (GenAI) and large language models (LLMs) without exposing itself to data-privacy, intellectual-property, or regulatory risk. CyNtell builds and runs that program for organizations adopting GenAI faster than their internal policies can keep up, covering acceptable use, governance structure, data controls, and the security testing that verifies it all holds up in practice.
- Category
- GenAI and LLM governance, risk, and compliance consulting
- Core deliverables
- Acceptable use policy, security and privacy awareness training, governance framework, data governance controls, solution impact assessments
- Risks addressed
- Shadow AI, IP and data leakage, unclear accountability, regulatory and audit exposure
- Frameworks referenced
- NIST AI Risk Management Framework, MITRE ATLAS, EU AI Act, and emerging U.S. state AI laws
- Related CyNtell services
- Penetration testing (LLM and prompt-injection testing), gap assessments, Fractional CISO
- Engagement model
- Policy and governance, then assessment and readiness, then data governance and assurance
What does an AI acceptable use policy actually cover?
An AI acceptable use policy sets the ground rules for how employees may use public GenAI tools and LLMs at work: what data can and cannot go into a prompt, which tools are approved, and who signs off before a new AI use case goes live.
CyNtell drafts the policy with legal, IT, privacy, and compliance stakeholders in the room, builds in a mandatory user sign-off step, and maps each rule directly to the compliance obligations your organization already carries, rather than shipping a generic template and hoping it fits.
Does CyNtell train our staff on secure AI use?
Yes. CyNtell's AI Security and Privacy Awareness Training teaches employees to align everyday behavior with the acceptable use policy: what counts as sensitive, confidential, or PII data, why it cannot go into a prompt, and how an ordinary mistake, the AI equivalent of an email sent to the wrong recipient, becomes a real exposure.
The employee is usually the weakest link in any security program, and GenAI adds a new way to make an old mistake. CyNtell delivers this training annually, refreshed against the organization's current AI Acceptable Use Policy so the training tracks what people are actually allowed to do rather than going stale the year a new tool gets approved.
How does CyNtell build an AI governance framework?
CyNtell's AI governance framework gives an organization a strategic plan for AI adoption: documented rules of engagement, measurable ROI metrics and KPIs, named accountability for AI decisions, and a roadmap covering both public AI tools and any private AI or LLM built internally.
Most AI governance failures trace back to nobody being clearly accountable when a tool is adopted informally. CyNtell's framework assigns that ownership explicitly, alongside the roadmap and metrics, so governance survives past the initial rollout.
What is an AI solution impact assessment, and when do I need one?
An AI solution impact assessment reviews a specific AI use case before it launches, covering legal, compliance, security, privacy, and ethical implications, so an organization catches a problem in the proposal instead of after deployment.
CyNtell also runs this backward, as a Shadow AI discovery pass: identifying GenAI tools departments have already adopted without a formal review, then applying the same impact assessment retroactively so nothing already in use stays ungoverned.
How does CyNtell govern AI data once a tool is approved?
CyNtell's AI data governance controls extend an organization's existing data protection program to GenAI and LLM traffic: AI and LLM firewalls, data anonymization and masking, and integration with the data governance stack the organization already runs.
The goal is that approving a GenAI tool does not create a new blind spot next to a data protection program that already works. It is the same controls, extended to a new traffic type, not a second parallel program to maintain.
Does an AI governance engagement include security testing?
Yes. CyNtell extends its existing penetration testing practice to cover LLM and AI-specific attack paths such as prompt injection and data exfiltration, informed by the MITRE ATLAS adversarial-AI framework.
Governance policy and technical testing are usually sold as two separate engagements by two separate vendors. CyNtell runs both under one roof, so the policy you write and the system you actually deployed get checked against the same standard.
Which AI regulations and frameworks does this address?
CyNtell's AI governance work is informed by the NIST AI Risk Management Framework's govern-map-measure-manage structure, the MITRE ATLAS taxonomy of adversarial AI techniques, and the compliance direction set by the EU AI Act and emerging U.S. state AI laws.
None of these automatically apply to every organization. Which ones are in scope depends on your industry, where you operate, and whether you are a regulated federal contractor. CyNtell scopes that applicability as the first step of any engagement rather than assuming it.
The risk of moving without a policy
Shadow AI
Employees already run prompts through public GenAI tools with no visibility into what data leaves the building.
IP and data leakage
A prompt containing proprietary code, client data, or trade secrets can permanently leave your control the moment it is submitted.
No internal guidelines
Without a written policy, every employee is making their own judgment call about what is safe to share with an AI tool.
Regulatory and audit exposure
An auditor or regulator asking "how do you govern AI use?" needs a documented answer, not an assurance that it is handled informally.
Why govern Gen AI through CyNtell?
Governance and testing, one team
The same engagement covers policy, data governance, and MITRE ATLAS-informed AI security testing, not a policy binder handed off to someone else to verify.
Federal-grade credentials
CyNtell holds a GSA Schedule contract, SBA 8(a) and HUBZone certifications, and Authorized C3PAO status, built for organizations that already answer to a compliance framework.
Cybersecurity heritage, not a bolt-on
AI governance sits inside CyNtell's existing cybersecurity and compliance practice, so a policy CyNtell writes reflects how attackers and auditors actually operate.
Gen AI governance: frequently asked questions
What is Gen AI governance?
Gen AI governance is the set of policies, controls, and assessments that let an organization deploy generative AI and large language models responsibly, covering acceptable use, data protection, accountability, and security testing so AI adoption does not outrun the organization's ability to manage its risk.
What is the difference between an AI acceptable use policy and an AI governance framework?
An acceptable use policy is the rulebook employees follow day to day: what is allowed in a prompt, which tools are approved. An AI governance framework is the broader structure around it: rules of engagement, ROI and KPI metrics, named accountability, and a roadmap for both public and private AI adoption.
Do we need an AI solution impact assessment for every new AI tool?
CyNtell recommends one before any AI use case that touches sensitive, regulated, or proprietary data goes live, since the assessment is what catches a legal, security, or ethical problem before deployment rather than after. Lower-risk, general-purpose tool use can often be covered by the acceptable use policy alone.
What is "Shadow AI" and why does it matter?
Shadow AI is GenAI or LLM use inside an organization that was never formally reviewed or approved, an employee running client data through a public chatbot, for example. It matters because that usage carries the same data-privacy and IP risk as sanctioned AI use, just without anyone accountable for it.
Does CyNtell provide AI security and privacy awareness training?
Yes. CyNtell's AI Security and Privacy Awareness Training is delivered annually and teaches employees to treat sensitive, confidential, and PII data carefully around AI tools, aligning their day-to-day habits with the organization's AI acceptable use policy rather than leaving that judgment call to each individual.
Does CyNtell test our AI systems for security vulnerabilities?
Yes. CyNtell extends its penetration testing practice to AI-specific attack paths such as prompt injection and data exfiltration, informed by the MITRE ATLAS adversarial-AI framework, so a governance policy is backed by evidence the underlying system actually holds up.
Which AI frameworks and regulations does CyNtell's work reference?
CyNtell's AI governance work references the NIST AI Risk Management Framework, the MITRE ATLAS adversarial-AI taxonomy, and the compliance direction set by the EU AI Act and emerging U.S. state AI laws, scoped to which ones actually apply to your organization rather than assumed by default.
How long does a Gen AI governance engagement take?
Timeline depends on the number of departments, existing policy maturity, and whether a Shadow AI discovery pass is needed first. CyNtell scopes a specific timeline and cost during the initial consultation rather than quoting a one-size figure upfront.
Bring clarity, control, and compliance to your AI initiatives
CyNtell scopes your Gen AI governance program in an initial consultation, with no obligation to proceed.
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