Enterprise Security & Governance

AI Data Security & Governance: Build Trust, Ensure Compliance, Manage Risk

Implement enterprise-grade security and governance frameworks for AI systems—from data protection and model security to ethical AI policies and regulatory compliance. Deploy AI confidently while protecting your data, customers, and reputation.

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Zero

Security Incidents Across Client Deployments

SOC 2

Type II, HIPAA, ISO 27001 Aligned

100+

Governance Frameworks Implemented

50+

Regulatory Audits Passed

NERC CIP Compliant • SR 11-7 Model Governance • GDPR/CCPA Privacy • Fed/OCC Examination Support

The Challenge

The AI Security & Governance Challenge

AI introduces new risks that traditional security and governance frameworks weren't designed to address.

AI-Specific Security Risks

AI models vulnerable to adversarial attacks, data poisoning, model theft, and prompt injection

Impact: Model manipulation, data breaches, IP loss

Data Privacy Concerns

Training data contains PII/PHI; models may memorize sensitive information; privacy regulations complex

Impact: Regulatory violations, lawsuits, reputational damage

Regulatory Uncertainty

Evolving AI regulations (EU AI Act, state laws); sector-specific rules (HIPAA, SR 11-7); lack of clear guidance

Impact: Compliance risk, delayed deployments, executive hesitation

Model Bias & Fairness

AI models can perpetuate or amplify bias; discrimination lawsuits; regulatory scrutiny on fairness

Impact: Legal liability, reputational harm, ethical concerns

Lack of Transparency

AI decisions are opaque; regulators and customers demand explainability; accountability unclear

Impact: Regulatory rejection, customer distrust, audit failures

Ungoverned AI Sprawl

Shadow AI projects across departments; no central visibility; inconsistent practices; duplicated efforts

Impact: Security gaps, wasted investment, compliance violations

Third-Party AI Risk

Reliance on OpenAI, Anthropic, cloud providers; vendor security posture; data handling practices unclear

Impact: Vendor breaches, service disruptions, compliance gaps

Intellectual Property Concerns

Training data copyright issues; model IP ownership questions; code generation copyright uncertainty

Impact: Legal disputes, licensing challenges, innovation barriers

Our Solution

Comprehensive AI Security & Governance

End-to-end framework covering data protection, model security, ethical AI, compliance, and risk management.

AI Data Security

Protect training data, implement privacy-preserving AI, and secure infrastructure

Training Data Protection

Data Classification: Classify by sensitivity, define handling requirements, implement labeling and access controls
Encryption: AES-256 at rest, TLS 1.3 in transit, field-level encryption, key management and rotation
Access Controls: RBAC, ABAC, just-in-time provisioning, privileged access management, audit logging
Data Minimization: Collect only necessary data, anonymization, retention policies, automated deletion

Privacy-Preserving AI Techniques

Differential Privacy: Mathematical noise to protect individual privacy while training models
Federated Learning: Train models without centralizing data, useful for multi-org collaboration
Synthetic Data: Generate synthetic training data that preserves statistical properties without PII/PHI
Confidential Computing: Encrypted data in memory during processing (Azure, AWS Nitro Enclaves)

Secure AI Infrastructure

Network Security: Segmented networks, WAF, DDoS protection, zero-trust architecture
Compute Security: Hardened containers, runtime monitoring, secrets management, vulnerability scanning
Model Serving: Authentication (OAuth2, JWT), rate limiting, input validation, output filtering

Data Loss Prevention (DLP)

Prevent Exfiltration: Monitor and block unauthorized data transfers, detect sensitive data in API responses
Output Filtering: Scan model outputs for PII/PHI/secrets, redact sensitive information, alert on leakage

AI Model Security

Protect AI models from attacks, theft, and unauthorized access

Adversarial Robustness

Attack Defense: Adversarial training, input sanitization, ensemble methods, certified defenses, anomaly detection
Types Protected: Adversarial examples, evasion attacks, model inversion, membership inference
Prompt Injection Defense: Input filtering, prompt engineering best practices, system prompt protection, output validation

Model Access Controls

Authentication & Authorization: API key management, OAuth2/OIDC, fine-grained permissions, rate limiting, usage auditing
Model Versioning: Track all versions, link to training data/code, approval workflows, rollback capabilities

Model IP Protection

Prevent Model Theft: Watermarking, rate limiting, output rounding, suspicious query monitoring, legal protections
Secure Storage: Encrypted model artifacts, access-controlled registries, backup and disaster recovery

Supply Chain Security

Training Pipeline: Secure CI/CD, code review, dependency scanning, signed artifacts, isolated environments
Third-Party Models: Vendor assessments, license reviews, API security, fallback strategies, data handling agreements

Monitoring & Incident Response

Model Monitoring: Anomaly detection, performance degradation alerts, drift detection, security event monitoring
Incident Response: IR plan, quarantine procedures, forensics, communication protocols, post-incident review

AI Governance Framework

Establish policies, risk management, and compliance structures

AI Governance Operating Model

Governance Bodies: AI Governance Council (quarterly), AI Risk Committee (monthly), AI Ethics Board, AI Center of Excellence
Roles & Responsibilities: Chief AI Officer, AI Risk Manager, AI Ethics Officer, Model Validators, DPO, clear RACI matrix

AI Policies & Standards

Policy Library: 10-15 core policies including Acceptable Use, Data Governance, Model Development Standards, Validation, Ethics Principles
Policy Management: Regular reviews (annual minimum), version control, communication and training, exception management, compliance monitoring

AI Risk Management

Risk Assessment: Identify technical, operational, compliance, ethical, and reputational risks with scoring methodology
Model Risk Management: SR 11-7 compliance for financial services - development documentation, independent validation, monitoring, board reporting

AI Documentation Requirements

Model Cards: Model purpose, training data, architecture, performance metrics by demographic group, limitations, ethical considerations
Data Cards: Dataset description, collection methodology, quality assessment, biases, privacy considerations, licensing
System Cards: End-to-end system description, architecture diagrams, data flows, security controls, monitoring, incident response

AI Compliance Management

Regulatory Mapping: Identify applicable regulations by jurisdiction and industry, map requirements to controls, gap assessment
Audit Readiness: Documentation repositories, evidence collection automation, mock audits, audit response procedures
Regulatory Examination Support: Preparation for Fed, OCC, CFPB, FDA exams, response coordination, technical experts, issue remediation

Responsible & Ethical AI

Build fair, transparent, and accountable AI systems

AI Ethics Principles

Core Principles: Fairness & Non-Discrimination, Transparency & Explainability, Privacy & Data Protection, Safety & Security
Additional: Accountability, Human Agency & Oversight, Societal & Environmental Wellbeing
Customized: Principles tailored to your organization's values and industry requirements

Bias Detection & Mitigation

Pre-Deployment Testing: Statistical parity, equal opportunity, predictive parity, equalized odds, calibration across demographic groups
Mitigation Techniques: Pre-processing (resampling, reweighting), in-processing (fairness-aware algorithms), post-processing (threshold optimization)
Ongoing Monitoring: Continuous bias monitoring in production, automated alerting, regular audits, feedback loops

Explainability & Transparency

Global Explanations: Feature importance, partial dependence plots, model summaries and decision rules
Local Explanations: SHAP (Shapley values), LIME, counterfactual explanations, example-based explanations
Transparency Practices: User-facing explanations, disclosure when AI is used, model cards published, appeals mechanisms

Human-AI Collaboration

Human-in-the-Loop (HITL): AI provides recommendations; humans make final decisions for high-stakes situations
Human-on-the-Loop (HOTL): AI makes decisions; humans monitor and intervene when needed, override capabilities
Human Oversight: Define when oversight is required, training for reviewers, feedback mechanisms, avoid automation bias

Ethical Review Process

AI Ethics Review: Trigger conditions for high-risk applications, ethics review checklist, stakeholder consultation, ethics board approval
Public Interest: Impact on vulnerable populations, societal benefits and harms, environmental impact, alignment with mission

Regulatory Compliance

Navigate Complex AI Regulations with Confidence

We help you comply with existing and emerging AI regulations across jurisdictions and industries.

EU AI Act

Risk-based regulation of AI systems in the European Union

Risk Categories:

Prohibited AI: Social scoring, subliminal manipulation, biometric categorization

High-Risk AI: Employment, credit scoring, law enforcement, critical infrastructure, education

Limited-Risk: Chatbots and deepfakes (transparency requirements)

High-Risk AI Requirements:

Risk management system

Data governance & quality

Technical documentation

Record-keeping & traceability

Transparency requirements

Human oversight

Accuracy & robustness

Conformity assessment

Our Support:

• Risk classification assessment
• Compliance gap analysis & remediation roadmap
• Documentation and evidence preparation
• Conformity assessment support
• Ongoing compliance monitoring

US AI Regulations (Emerging)

Federal and state-level AI regulations

Federal Level:

AI Bill of Rights: Blueprint for safe, effective systems (non-binding)
Executive Order: Safe, Secure, and Trustworthy AI
Sector-Specific: FDA for medical devices, NIST AI Risk Management Framework

State Level:

California: AI transparency and automated decision-making laws

Colorado: AI bias auditing requirements (employment)

Illinois: Biometric Information Privacy Act (BIPA)

New York: AI hiring tools disclosure requirements

Our Support:

• Jurisdiction-specific compliance assessment
• Multi-state compliance strategy
• Monitoring regulatory developments
• Adaptive compliance frameworks

GDPR & Privacy Regulations

Data protection and privacy compliance

GDPR (EU) Key Requirements:

Lawful basis for processing (consent, legitimate interest, etc.)
Data minimization and purpose limitation
Right to explanation for automated decisions
Data Protection Impact Assessment (DPIA) for high-risk processing
Data subject rights (access, rectification, erasure, portability)

CCPA/CPRA (California):

Consumer right to know

Right to delete

Right to opt-out

Right to correct

Automated decision disclosures

Sensitive data limitations

Our Support:

• Privacy impact assessments
• Privacy by design implementation
• Data subject rights workflows
• Consent management
• Cross-border data transfer solutions (SCCs, BCRs)

Industry-Specific Regulations

Sector-specific compliance requirements

Healthcare (HIPAA, FDA)

• HIPAA privacy and security rules
• FDA regulations for AI/ML medical devices
• 21st Century Cures Act (information blocking)
• State medical privacy laws

Learn more about healthcare compliance

Financial Services (SR 11-7, FCRA, ECOA)

• Federal Reserve SR 11-7 (Model Risk Management)
• Fair Credit Reporting Act (FCRA)
• Equal Credit Opportunity Act (ECOA)
• Gramm-Leach-Bliley Act (GLBA)

Learn more about financial services compliance

Energy & Utilities (NERC CIP)

• NERC CIP (Critical Infrastructure Protection for bulk electric system)
• Cybersecurity requirements for OT systems

Learn more about energy & utilities compliance

Security Frameworks

Security Frameworks & Compliance Certifications

We align AI systems with industry-standard security frameworks and help you achieve certifications.

SOC 2 Type II

Security, availability, processing integrity, confidentiality, and privacy controls

Required by most enterprise B2B customers

Our Support:

Control design and implementation
Evidence collection automation
Mock audits and readiness assessments
Auditor coordination
Remediation and continuous compliance

Timeline: 6-9 months

ISO 27001 (ISMS)

International standard for information security management systems

Global recognition across industries

Our Support:

ISMS design and implementation
Risk assessment and treatment
Policy and procedure development
Internal audits
Certification audit support

Timeline: 9-12 months

NIST AI Risk Management Framework

Voluntary framework for managing AI risks (US NIST)

Emerging standard for AI governance

Four Functions:

Govern

Map

Measure

Manage

Our Support: Framework assessment, implementation roadmap, documentation and evidence, continuous improvement

NIST Cybersecurity Framework

Widely adopted cybersecurity standard with AI-specific considerations

Comprehensive security framework

Five Functions:

Identify

Protect

Detect

Respond

Recover

AI-Specific: AI system inventory, AI threat modeling, AI-specific security controls, AI incident response

FedRAMP

Federal Risk and Authorization Management Program for government cloud services

Required for federal agencies

Impact Levels:

Low Moderate High

Our Support: Readiness assessment, System Security Plan (SSP) development, control implementation, authorization process support

Timeline: 12-18 months

Industry-Specific Standards

Sector-specific security and compliance standards

PCI DSS

Payment Card Industry Data Security Standard

HITRUST

Healthcare security framework

NERC CIP

Energy sector critical infrastructure protection

IEC 62443

Industrial cybersecurity standard

Monitoring & reporting: quarterly governance reports, risk dashboard updates, compliance monitoring
Policy maintenance: annual policy reviews, updates based on regulatory changes, version control
Capability building: ongoing training, community of practice, knowledge sharing, external engagement
Adaptation: monitor regulatory landscape, assess new AI technologies and risks, update frameworks

Deliverables:

Quarterly Reports Policy Updates Training Updates

What You Get

AI Security & Governance Deliverables

Comprehensive deliverables to establish and maintain secure, compliant, and responsible AI systems.

Assessment & Strategy

Current state security and governance assessment
AI risk assessment and heat map
Regulatory compliance gap analysis
Security & governance strategy document
12-18 month implementation roadmap
Executive presentations and business case

Governance Framework

Governance operating model and charter
Policy library (10-15 core policies)
Standards and procedure documents
Risk management framework
Model risk management program (for financial services)
Ethics principles and review process
Documentation templates (model cards, data cards, system cards)

Security Implementation

Data security architecture and controls
Model security measures
Access control and identity management
Encryption key management
Network security and segmentation
Security monitoring and logging
Incident response playbooks

Privacy & Compliance

Privacy impact assessments (DPIA)
Privacy by design implementation
Data subject rights workflows
Consent management (if applicable)
Compliance monitoring dashboards
Regulatory examination support

Responsible AI

Bias testing methodology and tools
Explainability implementation (SHAP, LIME)
Model cards for transparency
Fairness monitoring dashboards
Human oversight procedures
Appeals and redress mechanisms

Training & Enablement

Executive and board training
Data scientist responsible AI training
Security awareness training
Policy and procedure training
Train-the-trainer materials

Ongoing Support

Quarterly governance reporting

Policy updates and maintenance

Regulatory monitoring and updates

Audit and examination support

30-day post-implementation support

Optional: Ongoing managed services

Schedule Security Assessment

Industry Expertise

Industry-Specific Security & Governance

Tailored security and governance solutions that meet the unique regulatory and compliance requirements of your industry.

Healthcare

Key Challenges:

HIPAA privacy and security requirements
PHI in training data and model outputs
FDA regulations for AI/ML medical devices
Health system IT approval processes
Patient safety and liability concerns

Our Solutions:

HIPAA-compliant AI architecture
BAA-eligible infrastructure and vendors
De-identification and privacy-preserving AI
FDA submission support (510(k), De Novo)
Clinical safety monitoring
Audit trail for patient-facing AI

Compliance Frameworks:

HIPAA Privacy & Security Rules FDA 21 CFR Part 11 HITECH Act 21st Century Cures Act State Health Privacy Laws Joint Commission State Medical Board Regulations

Learn more about healthcare compliance

Financial Services

Key Challenges:

SR 11-7 model risk management requirements
Fair lending compliance (FCRA, ECOA)
Explainability for credit decisions
Regulatory examination readiness (Fed, OCC, CFPB)
Consumer protection and privacy (GLBA)
Market manipulation and fraud concerns

Our Solutions:

SR 11-7 compliant model development and validation
Explainable AI for credit decisions (SHAP, counterfactuals)
Bias testing across protected classes
Model governance and documentation
Adverse action letter generation (FCRA)
Regulatory examination support

Compliance Frameworks:

SR 11-7 (Model Risk Management) FCRA ECOA/Regulation B GLBA BSA/AML CFPB Guidance State Consumer Protection Laws

Learn more about financial services compliance

Manufacturing

Key Challenges:

OT/IT security for industrial systems
Safety-critical AI applications
Intellectual property protection
Supply chain security
Worker privacy and surveillance concerns
International data transfer (global operations)

Our Solutions:

IEC 62443 industrial security standards
Air-gapped networks and unidirectional data diodes
Safety instrumented systems (SIS) integration
IP protection (model watermarking, access controls)
Privacy-preserving workforce analytics
Cross-border data transfer mechanisms (SCCs, BCRs)

Compliance Frameworks:

IEC 62443 (Industrial Cybersecurity) ISO 27001 OSHA (Worker Safety) GDPR (EU operations) China Cybersecurity Law Industry-specific standards

Learn more about manufacturing security

Technology & SaaS

Key Challenges:

Customer data security (multi-tenant)
SOC 2 compliance for enterprise sales
Privacy compliance (GDPR, CCPA)
Third-party AI risk (OpenAI, Anthropic dependencies)
Rapid development vs. security trade-offs
Developer access to production data

Our Solutions:

SOC 2 Type II readiness and certification
Multi-tenant data isolation
Privacy by design for product features
Third-party vendor risk assessments
Secure development lifecycle for AI
Developer data access governance

Compliance Frameworks:

SOC 2 Type II ISO 27001 GDPR CCPA/CPRA State Privacy Laws Industry-specific (FERPA, COPPA)

Learn more about tech & SaaS security

Energy & Utilities

Key Challenges:

NERC CIP (Critical Infrastructure Protection)
OT/IT convergence security risks
Safety-critical applications (grid stability)
Public trust and transparency
Environmental and social impact
Regulatory scrutiny (state PUCs, FERC)

Our Solutions:

NERC CIP compliant AI architecture
OT security best practices (ISA/IEC 62443)
Grid stability safety controls
Transparency and explainability for regulators
Environmental impact assessment
Rate case support and public communications

Compliance Frameworks:

NERC CIP (Bulk Electric System) FERC Regulations State PUC Rules EPA Environmental OSHA Safety Pipeline Safety (PHMSA)

Learn more about energy & utilities compliance

Ready to Secure Your AI Systems?

Schedule a security assessment to identify risks and create a roadmap for comprehensive AI security and governance.

Schedule Security Assessment Request Proposal

Technology Stack

Security & Governance Technology Stack

Industry-leading tools and platforms we use to implement comprehensive AI security and governance

Security Tools

Data Security

Encryption:

AWS KMS, Azure Key Vault, GCP KMS, HashiCorp Vault

Data Loss Prevention:

Microsoft Purview, Symantec DLP, Google DLP API

Access Management:

Okta, Azure AD, AWS IAM, Google Cloud Identity

Secrets Management:

HashiCorp Vault, AWS Secrets Manager, Azure Key Vault

Model Security

Model Registry:

MLflow, Weights & Biases, Neptune, Azure ML Registry

Version Control:

Git, DVC (Data Version Control), Pachyderm

Access Control:

Custom RBAC, Cloud IAM, Kubernetes RBAC

Adversarial Testing:

IBM ART, CleverHans, Foolbox

Infrastructure Security

Container Security:

Aqua Security, Snyk, Prisma Cloud, Anchore

Network Security:

Palo Alto, Fortinet, AWS WAF, Cloudflare

SIEM:

Splunk, Datadog, Sumo Logic, Azure Sentinel

Vulnerability Management:

Qualys, Tenable, Rapid7, AWS Inspector

Governance Tools

Policy Management

GRC Platforms:

ServiceNow GRC, OneTrust, LogicGate, NAVEX Global

Document Management:

SharePoint, Confluence, Notion

Workflow Automation:

Jira, ServiceNow, Monday.com

Risk Management

Risk Assessment:

Resolver, Riskonnect, LogicManager

Model Risk:

SAS Model Risk Management, Moody's RiskAuthority

Third-Party Risk:

Prevalent, BitSight, SecurityScorecard

Compliance & Audit

Compliance Automation:

Vanta, Drata, Secureframe (SOC 2)

Evidence Collection:

Drata, Vanta, Tugboat Logic

Audit Management:

AuditBoard, Workiva, HighBond

Responsible AI Tools

Bias Detection

Comprehensive security and governance assessment
AI risk assessment and prioritization
Regulatory compliance gap analysis
Current state vs. target state analysis
Security & governance strategy document
Implementation roadmap (12-18 months)
Quick wins identification
Executive presentations

Deliverables:

Assessment Report (50-100 pages) Risk Register Gap Analysis Strategy Document Executive Summary

Best For:

• Organizations beginning AI governance journey
• Need for executive alignment and business case
• Regulatory or audit pressure
• Pre-investment planning

Schedule Assessment

POPULAR

Foundation Implementation

$250K-$450K

Duration: 4-6 months

What's Included:

Everything in Assessment & Strategy, plus:
Governance structure design and implementation
Policy library development (10-15 policies)
Standards and procedures
Risk management framework
Core security controls implementation
Privacy framework and DPIA process
Responsible AI framework (bias testing, explainability)
Documentation templates (model cards, etc.)
Training and enablement (executives, practitioners)
30-day post-implementation support

Best For:

• Organizations deploying first production AI systems
• Building governance from scratch
• SOC 2 or ISO 27001 preparation
• Regulatory compliance requirements

Get Started

Comprehensive Program

$500K-$1M+

Duration: 9-12 months

What's Included:

Everything in Foundation Implementation, plus:
Advanced security controls (DLP, confidential computing)
Privacy-preserving AI implementation
Model risk management program (SR 11-7)
Certification support (SOC 2, ISO 27001)
Regulatory examination preparation
Advanced responsible AI (fairness monitoring, appeals)
Integration with MLOps/LLMOps platforms
Comprehensive training program (all roles)
Change management and adoption support
Audit and examination support
90-day post-implementation support

Best For:

• Large enterprises with multiple AI initiatives
• Highly regulated industries (healthcare, financial services)
• Organizations pursuing certifications
• Need for comprehensive, enterprise-scale program

Request Consultation

Managed Services

$25K-$100K /month

Ongoing support after implementation

What's Included:

Governance program management and operations
Policy updates and regulatory monitoring
Quarterly compliance assessments
Bias monitoring and fairness audits
Security monitoring and incident response
Regulatory examination support (on-call)
Risk assessment updates
Training refreshers and new hire onboarding
Continuous improvement recommendations
Technology updates and patches
Executive reporting (monthly/quarterly)

Best For:

• Organizations without dedicated AI governance team
• Maintaining certifications (SOC 2, ISO)
• Ongoing regulatory compliance
• Peace of mind with expert support

Learn More

Custom Engagements: Available for specific needs (e.g., regulatory examination support only, bias audit only, SOC 2 readiness, etc.)

Request Custom Proposal

Success Stories

Security & Governance Success Stories

Real results from organizations that built secure, compliant, and responsible AI systems

Regional Health System - HIPAA Compliance & Governance

5-hospital health system, 8,500 employees

Challenge

• Wanted to deploy AI for clinical decision support and operations
• Previous AI pilots blocked by compliance and IT security
• No AI governance framework
• Privacy team concerned about PHI in models
• Fear of HIPAA violations and patient safety issues

Solution

• Comprehensive AI governance framework
• HIPAA-compliant AI architecture design
• BAA agreements with all AI vendors
• De-identification pipeline for training data
• Model documentation and risk assessment process
• Privacy impact assessments (DPIA)
• Clinical safety monitoring protocols
• Audit trail and logging for all AI interactions
• Training for 200+ staff on responsible AI

Results

Passed HIPAA audit with zero AI-related findings
Deployed 3 AI applications to production within 6 months
12 additional AI projects approved and in pipeline
Clinical AI governance committee operational
85% user adoption (trained clinical staff)
Zero patient safety incidents
Zero privacy breaches
$4.2M in operational savings from AI applications

"Augmentry.ai didn't just help us comply with HIPAA—they built a governance framework that actually accelerates AI adoption instead of blocking it. We went from 'AI is too risky' to '3 production systems in 6 months.'"

— Dr. Patricia Rodriguez, Chief Medical Information Officer

Regional Bank - SR 11-7 Model Risk Management

$12B regional bank, 150 branches

Challenge

• Federal Reserve examination upcoming
• Multiple AI/ML models in use (credit risk, fraud detection, marketing)
• Inconsistent model documentation
• No independent model validation
• No formal model risk management program
• Risk of regulatory findings and penalties
• Executive concern about model explainability

Solution

• SR 11-7 model risk management framework
• Model inventory and tiering (by risk)
• Comprehensive model documentation (MDD) for all models
• Independent model validation program
• Ongoing model monitoring dashboards
• Model governance structure (committee, policies)
• Explainable AI implementation (SHAP) for credit models
• Bias testing across protected classes (ECOA compliance)
• Board reporting packages
• Examiner response procedures and materials

Results

Passed Federal Reserve examination with zero MRM findings
All 8 AI/ML models validated and documented
Model governance committee operational
Automated monitoring for 6 key models
Bias testing showed no disparate impact
Executive confidence in AI systems increased
Faster model deployment (clear approval process)
Avoided $500K-$2M in potential penalties

"The Fed examiners were impressed with our model risk management program. Augmentry.ai gave us the framework and documentation that made the exam smooth and successful."

— James Chen, Chief Risk Officer

SaaS Company - SOC 2 Type II Certification

B2B SaaS platform, 5,000 customers, adding AI features

Challenge

• Enterprise customers requiring SOC 2 Type II
• Adding AI features (chatbot, recommendations) increased scope
• No formal security governance
• Fast-moving development culture resistant to process
• 6-month deadline for certification (customer commitments)
• Concerns about customer data in AI training

Solution

• SOC 2 Type II readiness assessment and gap remediation
• Security controls for AI features (access control, encryption, monitoring)
• Customer data handling policies and procedures
• AI-specific controls (model access, data minimization, output filtering)
• Evidence collection automation
• Developer training on secure AI development
• Mock audit and remediation
• Auditor selection and coordination
• Continuous compliance monitoring

Results

Achieved SOC 2 Type II certification in 7 months
Zero audit findings (clean report)
AI features fully included in scope
Customer data handling policies approved by enterprise customers
15 enterprise deals unblocked ($4.5M ARR)
Developer adoption of secure practices (90%+)
Automated compliance monitoring (reduced ongoing effort 60%)
Competitive advantage in enterprise sales

"SOC 2 seemed like a bureaucratic nightmare that would slow us down. Augmentry.ai made it manageable, fast, and actually improved our security posture. Now it's a competitive advantage."

— Sarah Kim, CTO and Co-Founder

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