AI SecOps Framework v3.0
AISec-as-a-Service

Govern AI.
Secure AI.
Scale AI safely.

Enterprise-grade AI security operations built on our proprietary AI SecOps Framework v3.0. The first framework to simultaneously satisfy the demands of the CTO, CISO, CDO, and Board.

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Built for four enterprise audiences
CTOEngineering-grade evaluation science, agentic design, and serving optimisation
CISOZero-trust infrastructure, eight-class threat model, disinformation defence
CDOModelOps lifecycle governance, AI asset inventory, dataset lifecycle management
BoardFormal governance programme, RACI accountability, regulatory compliance mapping
The Framework

AI SecOps Framework v3.0:
Five-Pillar Enterprise Architecture

Version 3.0 preserves and strengthens every engineering principle from v2.0, adds seven governance upgrades, and introduces two new horizontal disciplines. The result is the definitive enterprise AI SecOps architecture.

Pillar 01
AI Adoption and Crawl-Walk-Run Policy
Formalised autonomy classification for every AI agent deployment. Prompt engineering as a governed discipline with version control, adversarial testing, and regression suites. User behaviour analytics and adoption governance.
Autonomy PolicyPrompt Gov.UBA
Pillar 02
Evaluation Science and AI-as-Judge
Four-layer evaluation model: Functional, Safety, Alignment, and Business. AI-as-judge pipeline enabling 100% production traffic evaluation. Benchmark engineering, hallucination governance, and regulatory benchmark alignment.
AI-as-JudgeBenchmarksHallucination Eng.
Pillar 03
Governance, TRiSM, and ModelOps
AI asset inventory and centralised ModelOps lifecycle. GMAV observability stack with five layers plus zero-trust. Token economics engineering, guardrails architecture via Cyvia.ai, and policy-as-code with regulatory mapping.
TRiSMModelOpsPolicy-as-Code
Pillar 04
Resilience and Business Continuity
Six-class AI failure taxonomy. Loss-of-control containment protocol with automatic agent suspension. Full-stack rollback architecture: model, prompt, RAG index, configuration, and vendor rollback with defined RTO targets.
Failure TaxonomyRollbackBCP
Pillar 05
Model and Data Engineering
Domain-specific model strategy with governance approval gates. Model Adaptation Hierarchy: Prompt to RAG to Fine-tune to Pretrain. RAG pipeline architecture with retrieval quality governance, and dataset lifecycle management.
Domain ModelsRAG Gov.Dataset Lifecycle
Five Horizontal Disciplines

Cross-pillar engineering mandates
that apply to every engagement

These disciplines are architectural requirements, not optional add-ons. Every client engagement and every deliverable must satisfy all five simultaneously.

Latency Engineering and Serving Architecture

API vs. self-hosted decision framework with cost, latency, data sovereignty, and compliance documentation
Intelligent model routing based on task complexity classification, reducing cost without sacrificing quality
Prompt caching structured to maximise KV cache hits, achieving up to 90% cost reduction on cached prefixes
Streaming-first design for all user-facing agents: latency perception engineering as a UX requirement
Production load testing before every go-live deployment

Agentic AI Governance and Zero-Trust Infrastructure

Minimal footprint: agents request only permissions required for each specific task, no standing elevated access
Action reversibility: human confirmation required via autonomy policy for all irreversible agent actions
Execution limits: every agent has hardcoded maximum step count, time limit, and cost ceiling enforced at infrastructure level
Immutable audit logging: every tool call, decision, and output logged in tamper-evident audit trail
Zero-trust enforcement: no implicit trust in any AI infrastructure component

AI Governance Programme Structure and RACI

Monthly AI Governance Function review: KPI scorecards, compliance events, and shadow AI discoveries
Quarterly Board AI Committee briefing: AI health index, maturity score, and regulatory compliance status
Annual full AI governance audit: all models, agents, datasets, and policies reviewed with maturity index update
Ad hoc escalation: loss-of-control events and regulatory breaches trigger immediate board-level escalation
Formal AI governance RACI with defined accountability from AI Ops Lead to CISO to Board

Disinformation Security and Digital Provenance

Disinformation detection pipeline: output scanning for false factual claims and logical inconsistencies before delivery
Digital provenance tagging: all AI-generated content tagged with model version, timestamp, and policy compliance status
Content authenticity verification: external-facing AI outputs include cryptographic provenance markers
Adversarial disinformation testing: red-team exercises designed to induce disinformation generation, tested quarterly
EU AI Act disinformation provisions and SEC AI disclosure guidance mapped to governance controls

AI Threat Model v3.0: Eight Security Classes

Prompt injection and jailbreaking attacks
Data exfiltration and context poisoning
Model inversion and membership inference
Supply chain compromise (third-party models)
Agentic loss-of-control and autonomy policy breach
Disinformation generation and provenance fraud
RAG index poisoning and knowledge base tampering
Infrastructure-level zero-trust violations
AI SecOps Maturity Index

Where does your organisation
sit on the AI maturity curve?

Our proprietary maturity benchmark assesses your organisation across eight dimensions: Evaluation, Governance and TRiSM, ModelOps, Resilience and BCP, Model Engineering, Agentic Governance, Security and TRiSM, and Governance Programme.

Reactive
No formal AI governance. AI tools used ad hoc with no oversight or evaluation framework.
Developing
Basic AI policies in place. Some evaluation happening. ModelOps not yet formalised.
Systematic
Structured governance, evaluation pipeline active. TRiSM partially operationalised. Agentic controls developing.
Managed
Full ModelOps lifecycle, zero-trust AI infrastructure, and formal governance programme. Board-level reporting active.
Intelligent
Full AI SecOps Framework v3.0 implementation. Continuous improvement engine active. Regulatory alignment complete.
Request a Free Maturity Assessment →
Regulatory Alignment

Every governance control
mapped to regulatory requirements

The AI SecOps Framework v3.0 is the only AI governance architecture with all evaluation benchmarks, policy-as-code rules, and governance reporting structures explicitly regulatory-aligned by design.

EU AI Act
Full mapping of governance controls to EU AI Act risk classification, transparency requirements, and disinformation provisions for all high-risk AI applications.
DORA
Digital Operational Resilience Act compliance for AI systems in financial services. Resilience architecture, BCP, and incident reporting aligned to DORA mandates.
HIPAA
HIPAA-compliant AI governance for healthcare and MedTech deployments. PHI handling, audit trails, and access controls built into the zero-trust infrastructure layer.
SOC 2
Security, availability, and confidentiality controls mapped to SOC 2 trust service criteria. Continuous monitoring and audit trail capabilities built into governance reporting.
SEC AI Guidance
AI disclosure obligations for publicly listed companies. Board-level AI governance reporting and material risk event escalation protocol aligned to SEC guidance.
ISO 42001
Alignment with the international standard for AI management systems. Framework governance structure and documentation maps cleanly to ISO 42001 requirements.
NIST AI RMF
Governance, risk, and compliance controls aligned to the NIST AI Risk Management Framework. Covers Govern, Map, Measure, and Manage functions across all five pillars.
Shadow AI Detection
Continuous scanning for unsanctioned AI tool usage across the organisation. All discovered assets are assessed, risk-classified, and added to the governance inventory automatically.
Service Deliverables

What you receive
in an AISec engagement

Every engagement produces documented, auditable deliverables that can be presented to regulators, the board, and engineering leadership.

Assessment and Discovery
AI SecOps Maturity Index scorecard across eight dimensions
Full AI asset inventory: all models, agents, datasets, and third-party integrations
Regulatory gap analysis: EU AI Act, DORA, HIPAA, and SOC 2 mapped to current state
Shadow AI discovery report with risk classification for all unsanctioned tools
Prioritised remediation roadmap with effort and risk weighting
Governance Implementation
Autonomy policy documents for every AI agent deployment
Policy-as-code ruleset with automated enforcement and regression testing
AI governance RACI with board-approved accountability structure
ModelOps lifecycle documentation: model registry, version control, and retirement protocols
Monthly governance KPI scorecard template and board reporting pack
Technical Architecture
Zero-trust AI infrastructure architecture document with implementation guide
Guardrails specification: input, output, system, agentic, and supply chain guardrails
Evaluation stack design: four-layer model, benchmark dataset governance, AI-as-judge pipeline
Rollback architecture runbook covering model, prompt, RAG, configuration, and vendor layers
GMAV observability stack integration plan with five-layer monitoring design
Ongoing Operations
Monthly AI governance function meeting facilitation and KPI review
Quarterly board AI committee briefing pack with maturity index update
Continuous adversarial testing: prompt injection, jailbreaking, and disinformation red-teaming
Incident response support for loss-of-control events and regulatory breach escalation
Annual full governance audit with updated regulatory compliance confirmation
FAQs

Common questions about
AISec-as-a-Service

Have a specific question about your AI security posture? Email info@gmavtech.com and our AISec expert will respond within 4 hours.

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What is AISec-as-a-Service?
AISec-as-a-Service is our managed AI security and governance offering, built on our proprietary AI SecOps Framework v3.0. It covers the full enterprise AI operations lifecycle: AI adoption governance, multi-layer evaluation science, TRiSM lifecycle management, resilience engineering, model and data governance, and five horizontal security disciplines including agentic AI governance and disinformation security.
Who needs AISec-as-a-Service?
Any organisation deploying AI in production needs this, especially in regulated industries such as Financial Services, Healthcare, and Legal. This is also essential for enterprises with board-level AI risk reporting requirements, companies using agentic AI or multi-agent systems, and organisations subject to EU AI Act, DORA, HIPAA, or SOC 2 compliance requirements.
How do you assess our AI maturity level?
We use our proprietary AI SecOps Maturity Index, which scores your organisation across eight dimensions: Evaluation, Governance and TRiSM, ModelOps, Resilience and BCP, Model Engineering, Agentic Governance, Security, and Governance Programme. Levels range from 1 (Reactive) to 5 (Intelligent). The assessment takes 2 to 4 weeks and produces a detailed maturity report with a prioritised remediation roadmap.
Does AISec cover EU AI Act and DORA compliance?
Yes. Framework v3.0 explicitly maps every governance control to applicable regulatory requirements: EU AI Act, DORA, HIPAA, SOC 2, and SEC AI disclosure guidance. All evaluation benchmarks, policy-as-code rules, and governance reporting structures are regulatory-aligned by design. We also provide compliance gap assessments and remediation support for organisations preparing for regulatory review.
What is the difference between v2.0 and v3.0 of the framework?
Version 2.0 delivered a rigorous engineering architecture with five pillars and a four-layer evaluation model. Version 3.0 adds seven enterprise governance upgrades: TRiSM operationalised as a unified cross-pillar discipline; centralised ModelOps lifecycle; formalised agentic autonomy controls; zero-trust AI infrastructure doctrine; domain-specific model strategy; disinformation security as a first-class discipline; and a formal AI governance programme structure with RACI and board reporting.
Industries We Secure

AI security for the sectors
where governance is non-negotiable

Regulated industries face the highest stakes in AI deployment. Our framework maps directly to your sector's compliance requirements, risk models, and threat landscape.

Financial Services
Healthcare & MedTech
SaaS & Tech
Government & Defence
Legal & RegTech
Insurance & InsurTech
Energy & Utilities
Education & EdTech
Manufacturing & Industry
Retail & eCommerce
Media & Entertainment
Your Industry
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