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AI Compliances

Compliance consulting for vibe-coded products and AI-built applications.

We are AI orchestrating engineers. We bring the knowledge and tools vibe coders need so products built with AI can meet compliance frameworks and AI regulations, not only ship features fast.

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For vibe coders Ship fast with AI, then harden the product with clear compliance mapping and practical controls.
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SecureAI tool Our SecureAI tool maps scanner evidence to CIS, OWASP, NIST CSF, ISO 27001, SOC 2, and HIPAA themes for reporting, prioritization, and compliance conversations.
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SecureAI is our compliance mapping system for AI-built apps. Explore what you get: scanner evidence mapped to major frameworks, clearer priorities, and reporting your customers can understand.

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Compliance map

CIS Benchmarks

Center for Internet Security secure configuration standards for servers, databases, containers, cloud, and networking.

CIS Benchmarks are detailed recommendations for configuring infrastructure securely. Think of them as a security checklist for your stack: user accounts, SSH, firewalls, logging, file permissions, and services. SecureAI maps scanner evidence to CIS-style hardening themes so misconfigurations show up clearly in reporting.

SecureAI mapped themes

  • Authentication hardening
  • Least privilege
  • Network exposure
  • TLS
  • Audit logging

Why CIS matters for AI systems

When you run AI on your own infrastructure, CIS becomes an AI data-security concern. An AI SaaS stack may hold customer data, RAG documents, embeddings, API keys, conversations, prompts, uploaded files, and database credentials. A weak server, container, or database config can turn into an AI data breach.

What is at risk
  • Customer & personal data
  • RAG documents & embeddings
  • API keys & credentials
  • Conversations & prompts
  • Uploaded files & internal APIs

Security in layers

For an AI SaaS, CIS works best as layered hardening: OS/server first, then containers, then orchestration, then cloud. Each layer has its own CIS Benchmark family.

Typical layers
  • Layer 1: OS / server (e.g. Ubuntu, Debian)
  • Layer 2: Docker & containers
  • Layer 3: Kubernetes (EKS, AKS, GKE)
  • Layer 4: Cloud provider & services
  • Plus databases, apps, and APIs

CIS + AI Security Auditor

CIS provides the security baseline. SecureAI evaluates infrastructure evidence against those themes. AI can then explain findings, prioritize risk, and guide remediation, instead of forcing teams to read a 300-page checklist manually.

Auditor flow
  • CIS rules as the baseline
  • Server / stack scanning
  • Risk analysis & scoring
  • Clear report for teams
  • Guided remediation

CIS Benchmark is not a certification

Following a CIS Benchmark means you configure technology to a recognized security baseline. It does not mean your company is "CIS certified." SecureAI mapping supports reporting and prioritization; it does not replace CIS-CAT assessments, audits, or formal certification.

Compliance map

OWASP

Open Worldwide Application Security Project: securing applications, APIs, websites, and increasingly AI systems.

If CIS asks "is the server configured securely?", OWASP asks "is the application itself secure?" SecureAI maps findings to OWASP-oriented application and API themes so teams can prioritize issues that lead to abuse, data leakage, or broken access paths.

SecureAI mapped themes

  • Injection
  • Broken access control
  • Auth abuse / rate limits
  • Data exposure
  • Resource abuse
  • Database TLS

OWASP Top 10 (application risks)

The OWASP Top 10 lists major web application security risks. For a multi-tenant Laravel SaaS, Broken Access Control is especially critical: a request like GET /customers/125 must never let Company A retrieve Company B data by changing an ID.

Top risk examples
  • Broken access control
  • Cryptographic failures
  • Injection (e.g. SQL)
  • Insecure design
  • Security misconfiguration
  • Vulnerable components
  • Authentication failures
  • Integrity & logging failures
  • SSRF

OWASP for AI and LLMs

OWASP also publishes LLM / GenAI guidance for AI applications. Prompt injection, sensitive information disclosure, and excessive agency matter when your AI can read data, call tools, create bookings, send messages, or modify customer records.

AI-specific risks
  • Prompt injection
  • Sensitive information disclosure
  • Excessive agency / tool abuse
  • RAG and private data leakage
  • Unsafe model outputs

CIS + OWASP together

A complete AI SaaS strategy uses both baselines: CIS for infrastructure (server, Docker, database, cloud) and OWASP for the application (Laravel, APIs, auth, sessions, uploads) plus OWASP LLM guidance for RAG, prompts, agents, and tool calls.

Three baselines
  • CIS: harden VPS, Linux, Docker, DB, cloud
  • OWASP ASVS / Top 10: app & API security
  • OWASP LLM/GenAI: RAG, agents, tool calls
  • Tenant isolation & audit logs
  • Compliance-ready reporting

OWASP is guidance, not a certificate

OWASP publishes community-driven security projects and risk lists. Mapping findings to OWASP themes helps reporting and prioritization. It does not mean an application is "OWASP certified."

Identity & access

SAML

Security Assertion Markup Language: enterprise SSO and identity for AI platforms.

SAML is an authentication and identity standard, not an AI technology. It becomes critical in AI engineering when enterprise platforms connect employees, AI agents, RAG, documents, and tools to a company Identity Provider such as Microsoft Entra ID.

Where SAML helps AI systems

  • Employee SSO into the AI platform
  • Identity for RAG document access
  • Role limits for AI agents and tools
  • Department or role based reports
  • Multi-tenant IdP per customer
  • Audit logs tied to real users

Enterprise AI login flow

A company employee authenticates with the company IdP. SAML asserts identity to your AI platform so users do not need a separate password. The assertion can include name, email, department, and role for authorization decisions.

Typical flow
  • Employee
  • Company IdP (e.g. Microsoft Entra ID)
  • SAML assertion
  • AI platform
  • Agents / RAG / tools under that identity

SAML + AI agents

An agent that searches documents, queries CRM, creates reports, or sends email must run inside the user's authorized context. Identity and roles from SAML feed the orchestrator and policy engine before tools and RAG are invoked.

Secure agent path
  • SAML authentication
  • Identity + roles
  • AI orchestrator
  • Permission / policy engine
  • Agent tools, APIs, and RAG

SAML vs OAuth vs OIDC

SAML is enterprise authentication and SSO, traditionally XML-based. OAuth 2.0 handles delegated authorization. OpenID Connect (OIDC) is authentication on OAuth 2.0. RBAC and ABAC decide what a user or agent may do after login.

Modern AI SaaS stack
  • SAML / OIDC for authentication
  • RBAC / ABAC for authorization
  • AI orchestrator
  • Agents + RAG + APIs
  • Audit and governance

SAML is identity, not an AI or compliance certificate

SAML belongs under enterprise identity, security, and AI platform architecture. It does not certify AI compliance. Pair it with RBAC/ABAC and your compliance maps for full enterprise AI governance.

Compliance map

NIST CSF

NIST Cybersecurity Framework: manage cybersecurity risk across the whole organization (CSF 2.0).

CIS hardens configuration. OWASP secures applications. NIST CSF manages cybersecurity risk as a program. It is not mainly a checklist of technical settings. It asks what you have, what can go wrong, how you protect, detect, respond, recover, and improve. SecureAI maps scanner evidence into NIST themes so technical findings connect to that broader program view.

SecureAI mapped themes

  • Access control
  • Data security
  • Continuous monitoring
  • Asset inventory

CSF 2.0 functions

NIST CSF 2.0 organizes cybersecurity outcomes into six functions. Govern is a major emphasis in 2.0: strategy, policies, responsibilities, and acceptable risk. Identify covers assets. Protect puts safeguards in place. Detect spots suspicious activity. Respond handles incidents. Recover restores operations and improves.

Six functions
  • Govern: strategy, policy, ownership
  • Identify: assets and risks
  • Protect: MFA, encryption, backups, isolation
  • Detect: failed logins, unusual API/AI activity
  • Respond: contain, preserve logs, notify
  • Recover: restore, patch, review, improve

How it differs from CIS and OWASP

CIS Benchmarks = secure configuration (harden infrastructure). OWASP = application and AI security. NIST CSF = cybersecurity risk management for the whole program. They work together: NIST provides the structure; CIS and OWASP provide technical guidance underneath.

Together for AI SaaS
  • NIST CSF: overall security program
  • CIS: server, Docker, DB, cloud hardening
  • OWASP: Laravel, APIs, auth, LLM risks
  • Customers, AI/RAG, and infrastructure covered

Example: AI customer-support SaaS

Govern/Identify may flag that customer knowledge bases hold confidential business data and that one tenant must never retrieve another tenant's documents. OWASP then addresses access control, prompt injection, and data disclosure. CIS hardens Linux, Docker, databases, SSH, firewalls, and logging.

What customers understand
  • Documented risk management
  • Clear policies and ownership
  • Asset and data awareness
  • Incident response and recovery
  • Controls mapped for audits and sales

NIST CSF is a framework, not a certificate

CSF 2.0 helps organizations communicate and manage cybersecurity risk without prescribing one implementation. Mapping findings to NIST themes supports reporting and prioritization. It does not mean a product is "NIST certified."

Compliance map

ISO 27001

ISO/IEC 27001: a formal Information Security Management System (ISMS) that an organization can be audited and certified against.

CIS asks if infrastructure is securely configured. OWASP asks if the application and AI layer are secure. NIST CSF asks if cybersecurity risks are managed properly. ISO 27001 asks whether you have a systematic, documented, and auditable security management system. SecureAI maps findings to Annex A-style themes so scanner results can be discussed in ISMS control language.

SecureAI mapped themes

  • Privileged access
  • Cryptography
  • Logging
  • Configuration management
  • Secure development
  • Confidentiality
  • Asset inventory

What is an ISMS?

An Information Security Management System is not just software. It combines people, processes, policies, technology, risk management, documentation, monitoring, and continuous improvement. A policy such as "production customer data is only for authorized personnel" must also define how it is implemented, who owns it, how it is monitored, what happens when it fails, and how you improve it.

ISMS building blocks
  • People and responsibilities
  • Policies and procedures
  • Technology controls
  • Risk management
  • Evidence and monitoring
  • Continuous improvement

ISO 27001 for AI SaaS

AI SaaS adds assets and risks beyond a classic website: customer data, conversations, RAG documents, vector stores, LLM providers, API keys, tool integrations, and tenant isolation. ISO 27001 forces systematic questions about assets, risks, controls, and demonstrable evidence. That evidence trail is what enterprises and auditors expect.

AI-relevant questions
  • What data goes to external AI providers?
  • Who can access AI conversations?
  • How are prompts and outputs logged?
  • Can one tenant reach another tenant's RAG?
  • What can an agent do without approval?
  • How is retention and deletion handled?

Certification and ISO 42001

An organization can implement an ISMS and pursue independent ISO/IEC 27001 certification for a defined scope, such as development and operation of its SaaS platforms. That is commercially stronger than "we have a firewall." For AI specifically, ISO/IEC 42001 covers an AI Management System (AIMS). Many AI companies combine ISO 27001 + ISO 42001, then use NIST CSF, OWASP, and CIS underneath.

How the stack fits
  • ISO 27001: formal ISMS + auditability
  • ISO 42001: responsible AI management
  • NIST CSF: risk-management structure
  • OWASP: application and AI security
  • CIS: infrastructure hardening

Mapping is not certification

SecureAI maps scanner findings to ISO 27001-style control themes for reporting and prioritization. That does not mean an organization is ISO 27001 certified. Certification requires a scoped ISMS and an independent audit.

Compliance map

SOC 2

System and Organization Controls 2: evaluate how a company protects customer data and operates its systems.

SOC 2 is especially important for SaaS, cloud, AI, hosting, and software companies that store or process customer information. Where CIS hardens configuration, OWASP secures apps, NIST manages risk, and ISO 27001 builds an ISMS, SOC 2 asks: can you demonstrate that your security controls are designed and operating effectively? SecureAI maps scanner evidence to Trust Services Criteria themes to support those conversations.

SecureAI mapped themes

  • Logical access
  • Encryption in transit
  • Confidential data exposure
  • Monitoring
  • Change / config management
  • Application security events

Five Trust Services Criteria

SOC 2 is built on five Trust Services Criteria. Security is mandatory for a SOC 2 examination. Availability, Confidentiality, Processing Integrity, and Privacy are selected based on the organization's services and risks.

The five criteria
  • Security: unauthorized access and attacks (required)
  • Availability: systems reliable when needed
  • Confidentiality: sensitive data protected
  • Processing Integrity: accurate, complete processing
  • Privacy: personal data lifecycle controls

Type I vs Type II

Type I evaluates whether controls are properly designed at a point in time. Type II evaluates whether those controls operated effectively over a period of time. For a SaaS or AI platform, Type II is generally the stronger target for enterprise customers.

Typical AI SaaS controls
  • MFA and role-based access
  • Encryption, backups, and recovery
  • Logging, monitoring, and audit trails
  • Vulnerability and change management
  • Incident response and vendor risk
  • Secure development and access reviews

How SOC 2 fits the stack

CIS answers how systems should be configured. OWASP answers how applications should be secured. NIST CSF answers how cybersecurity risk is managed. ISO 27001 answers how an ISMS is established. SOC 2 answers whether you can prove controls are designed and operating effectively. A practical progression: CIS + OWASP, then NIST/ISO 27001 controls, then SOC 2 Type II readiness, then audit.

Complementary roles
  • CIS: secure configuration
  • OWASP: application and AI security
  • NIST CSF: risk management
  • ISO 27001: formal ISMS
  • SOC 2: demonstrated control effectiveness

Mapping is not a SOC 2 report

SecureAI maps findings to SOC 2 Trust Services themes for reporting and prioritization. That does not produce a SOC 2 Type I or Type II report. A SOC 2 examination requires an independent auditor and scoped evidence over the relevant period.

Compliance map

HIPAA

U.S. federal law establishing requirements for protecting health information and how it is handled.

HIPAA matters when a SaaS, AI platform, app, or infrastructure stores, processes, or transmits U.S. patients' protected health information (PHI). SecureAI maps findings to HIPAA Security Rule themes relevant to systems that may handle electronic PHI (ePHI), focusing on access, auditability, integrity, and transmission security.

SecureAI mapped themes

  • Access control
  • Audit controls
  • Integrity
  • Transmission security
  • Encryption / confidentiality
  • Risk analysis for network exposure

What HIPAA protects

The key concept is PHI (Protected Health Information): patient names and contacts, medical records, diagnoses, treatment and prescription details, insurance and appointment data, and related identifiers when linked to health information. Main areas include the Privacy Rule, Security Rule (ePHI safeguards), Breach Notification Rule, and Enforcement Rule.

Main HIPAA areas
  • Privacy Rule: use and disclosure of PHI
  • Security Rule: safeguards for ePHI
  • Breach Notification Rule: report certain breaches
  • Enforcement Rule: penalties and enforcement

AI/SaaS and Business Associate Agreements

If a clinic or hospital sends patient information through your AI SaaS and that information is PHI, you may need HIPAA-aligned processes and infrastructure: encryption, MFA, RBAC, audit logging, backups, retention/deletion, incident response, vendor management, and protection of AI prompts, conversations, documents, and outputs. A Business Associate Agreement (BAA) may be required when a covered entity uses your service to handle PHI. Cloud or AI providers may also be business associates or subcontractors.

Typical technical focus areas
  • Encryption in transit and at rest
  • Strong auth, MFA, and role-based access
  • Audit logging and access monitoring
  • Secure backups and incident response
  • Retention, deletion, and vendor controls
  • PHI in prompts, RAG, and model outputs

HIPAA vs SOC 2 and the wider stack

SOC 2 examines organizational controls; HIPAA is U.S. health-information law. A company can pursue both. OWASP secures the app, CIS hardens infrastructure, NIST manages risk, ISO 27001 builds an ISMS, SOC 2 demonstrates control effectiveness, and HIPAA adds healthcare/PHI-specific legal requirements. HIPAA does not apply simply because data is health-related; applicability depends on the organization, its role, and the nature of the information and activities.

Complementary roles
  • OWASP: secure application development
  • CIS: secure infrastructure configuration
  • NIST CSF: cybersecurity risk management
  • ISO 27001: information security management
  • SOC 2: independently examined controls
  • HIPAA: U.S. PHI legal requirements

Mapping is not HIPAA certification

SecureAI maps findings to HIPAA Security Rule themes for reporting and prioritization. That does not mean a product is "HIPAA certified," and it does not replace legal assessment, a BAA, or an audit. Whether HIPAA applies depends on roles, data, and activities.

AI concept

SGI: Scientific / Specialized Generalist Intelligence

An emerging AI concept for systems that act as intelligent specialists and agents, not only chatbots that answer questions.

SGI is not yet a universally standardized term like AI or AGI. Researchers currently use it in two main ways: Scientific General Intelligence and Specialized Generalist Intelligence. Both matter for modern AI SaaS and agent systems that use company data, retrieve knowledge, take actions, generate reports, and continuously improve.

Core SGI capabilities

  • Deliberation
  • Conception
  • Action
  • Perception
  • Specialized domain expertise
  • Broad general reasoning

1. Scientific General Intelligence

This usage focuses on AI that can perform a full scientific discovery cycle, not just answer questions. A system would research, formulate hypotheses, design experiments, execute or assist experiments, analyze results, learn, and formulate the next hypothesis.

Scientific cycle
  • Research literature & data
  • Formulate hypotheses
  • Design experiments
  • Execute / assist experiments
  • Analyze results
  • Reject weak hypotheses
  • Generate the next hypothesis
  • Repeat and improve

2. Specialized Generalist Intelligence

This research usage describes AI that becomes extremely strong in a specialized professional domain while still retaining broad general reasoning abilities, for example deep expertise in finance, medicine, engineering, or law combined with communication, planning, and coding.

Specialized generalist balance
  • Domain specialization
  • General reasoning
  • Communication
  • Planning
  • Coding & tooling
  • Useful autonomous agents

Why SGI matters

The shift is from AI as a simple tool to AI as an intelligent specialist/agent. Instead of only User → Question → Answer, an SGI-style system can understand a goal, research, plan, take action, observe results, evaluate, learn, and either finish or try again. That architecture fits AI SaaS products that connect to company data, use RAG, call APIs, schedule work, and generate reports.

From chatbot to agent
  • Understand goals
  • Research & retrieve knowledge
  • Plan and reason
  • Take action via tools/APIs
  • Observe results
  • Evaluate success or failure
  • Learn and adapt

SGI is not AGI

A typical chatbot is AI, but not SGI. SGI aims for specialized expertise plus stronger general reasoning and, in one definition, autonomous research loops. AGI aims at human-level general intelligence across domains. SGI can be an intermediate path toward more capable agents without claiming full AGI.

AI SGI AGI
Typical chatbot
Specialized expertise
General reasoning Limited
Autonomous research Limited Potentially
Multiple domains Sometimes
Human-level general intelligence Not necessarily Goal
Scientific discovery Limited Core (one definition) Potentially
AI concept

AGI: Artificial General Intelligence

An AI system that can perform a wide range of intellectual tasks at a level comparable to or beyond humans, rather than being designed for one narrow task.

Today's AI is usually narrow or bounded: coding, writing, analysis, images, and similar tasks. AGI aims at general-purpose intelligence with flexible reasoning across domains, stronger transfer learning, and the ability to pursue complex goals with less task-specific training. The key distinction is generalization and adaptability, not simply being "very smart."

What AGI aims for

  • General-purpose intelligence
  • Flexible cross-domain reasoning
  • Strong knowledge transfer
  • Learning new tasks with less training
  • Independent planning toward goals

AGI vs AI agents

An AI agent can already run multiple steps with tools, APIs, browsers, databases, memory, RAG, and planning. That does not automatically make it AGI. You can build a powerful agent with today's models without having achieved AGI.

Agent stack today
  • LLM + memory
  • Tools and APIs
  • RAG and databases
  • Planning and automation
  • Still not automatically AGI

Why AGI matters for SaaS

AGI-like capabilities could shift architecture from User → Software → AI feature toward User → AI Agent → understands the business → accesses authorized data → uses tools → performs tasks → reports results. For example: "Handle this month's customer follow-ups and identify customers at risk of leaving" could mean analyzing CRM, invoices, and support data, contacting customers through approved channels, updating records, and producing a management report.

Direction of change
  • From chatbot feature to agentic systems
  • Business-goal oriented workflows
  • Authorized data and tool use
  • Multi-step execution and reporting
  • Closer to agentic/AGI direction than a simple ERP chatbot

AGI is contested and not a certification

There is currently no universally accepted scientific or legal definition of exactly when an AI system qualifies as AGI, and claims that a particular system has definitively achieved AGI remain contested. AGI is an AI capability concept, not a compliance framework like CIS, OWASP, NIST, ISO, SOC 2, or HIPAA.

Narrow AI / current AI AGI
Purpose Specific or bounded tasks General-purpose intelligence
Reasoning Strong in many contexts, with limits Flexible across domains
Learning Task-specific training/adaptation Learn new tasks with less training
Transfer Limited across unrelated tasks Strong knowledge transfer
Tasks Coding, writing, analysis, images Potentially almost any cognitive task
Autonomy Usually needs human direction Can plan and execute complex goals

Quick answers

What is AI EU compliance?

AI EU compliance means designing AI products so they can meet EU AI Act expectations and related security and privacy controls, including clear data flow, oversight, logging, and framework-aligned evidence for customers and regulators.

What frameworks does VemeloAI SecureAI map?

SecureAI maps scanner evidence to CIS, OWASP, NIST CSF, ISO 27001, SOC 2, and HIPAA themes, and supports EU AI Act readiness conversations for AI-built products.

What is SAML in AI engineering?

SAML is an enterprise authentication and SSO standard. In AI platforms it connects company Identity Providers such as Microsoft Entra ID so employees, AI agents, RAG, and tools operate under real user identity, roles, and audit trails.

Is SecureAI a security certificate?

No. SecureAI is a mapping and reporting layer. Formal certificates such as ISO 27001 or SOC 2 Type II still come from independent audits when your organization is ready.

Important

SecureAI maps findings to CIS, OWASP, NIST, ISO 27001, SOC 2, and HIPAA control themes for reporting and prioritization. It does not certify compliance and does not replace an audit or BAA. SAML is enterprise identity and SSO for AI platforms, not a compliance certificate. SGI and AGI are AI capability concepts, not compliance certification frameworks.

Building with AI and need compliance guidance?

Talk to our AI orchestrating engineers. We help vibe-coded products and AI-built applications align with the frameworks and AI regulations that matter for your customers.

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