AI Governance Meets Strategic Execution
NOVUM helps enterprises deploy AI responsibly—with compliance, cybersecurity, and ethics built in.
About
At NOVUM, we’re dedicated to helping the enterprises leverage AI ethically and securely.
Proactive cyber resilience for AI-driven enterprises
Our Services
What we do
01
AI Governance & Ethical Advisory
Key Offerings:
1. Compliance with EU AI Act, NIST AI RMF, ISO 42001
2. Ethical AI audits (bias/transparency reports)
3. Board-level AI risk workshops
02
Cybersecurity Strategy
Key Offerings:
1. Cyber maturity assessments (NIST CSF, ISO 27001)
2. AI-driven threat modeling (e.g., LLM security risks)
3. Zero-trust roadmap implementation
03
Digital Risk & Compliance
Key Offerings:
1. GDPR/CCPA/NIS2 readiness
2. Vendor risk management (third-party cyber reviews)
04
Strategic AI Adoption
Key Offerings:
1. AI Pilot-to-Production roadmaps
2. Generative AI policy design (e.g., ChatGPT governance)
3. AI training for teams
Niche Expertise
Standards, Frameworks, & certifications across Novum Strategy’s services
01
AI Governance & Ethical Advisory
Regulatory Standards:
1. EU AI Act (Risk-based compliance: prohibited/high-risk AI)
2. NIST AI Risk Management Framework (RMF) (U.S. federal guideline)
3. OECD AI Principles (International policy alignment)
Certifications & Badges:
1. ISO 42001 (AI management systems)
2. IEEE 7000 Series (Ethical AI design)
3. Responsible AI Institute (RAI) Certification
02
Cybersecurity Strategy
Frameworks:
1. NIST Cybersecurity Framework (CSF)
2. ISO 27001 (Information security management)
3. CIS Critical Security Controls v8
Certifications:
1. Zero Trust Architecture (ZTA) Badge (For implementations)
2. SOC 2 Type II (For client assurance)
3. MITRE ATT&CK® Evaluations (Threat response validation)
03
Digital Risk & Compliance
Regulations:
1. GDPR (EU data protection)
2. NIS2 Directive (EU cyber resilience)
3. CCPA/CPRA (California privacy law)
Sector-Specific Badges:
1. HIPAA Compliance (Healthcare)
2. FFIEC AI Guidance (Banking)
3. FDA AI/ML-Based Software as a Medical Device (SaMD)
04
Strategic AI Adoption
Best Practice Frameworks:
1. COBIT® (Governance of enterprise IT)
2. ITIL 4 (AI service management)
3. MLOps Standards (e.g., Google’s MLOps maturity model)
Partnerships (TBA)
1. Microsoft AI Partner Network
2. AWS AI/ML Competency
3. Google Cloud Responsible AI
Ready to Elevate Your Trading Game?
Explore Our Range of Tailored Services and Take the First Step Towards getting your Exterprise Secure, Compliant, AI-Ready!
“I’ve used NOVUM’s AI Pilot-to-Production services for a couple of my start-up ideas. I couldn’t be happier with the results!. Their team of experts took the time to understand my ideas and risk tolerance, crafting a customized strategy that has helped me bring my ideas to life”
D.V.
OmniBots
F.A.Q.
Find answers to commonly asked questions about our services and strategies
What is AI governance, and why does my business need it?
AI governance ensures your AI systems are legally compliant, ethically sound, and technically robust. With regulations like the EU AI Act imposing fines of up to 7% of global revenue for non-compliance, proactive governance mitigates financial, repetitional, and operational risks. Our expert ‘AI Governance Consultants’ can help you implement frameworks (e.g., NIST AI RMF, ISO 42001) tailored to your industry.
How do you assess AI bias in our models?
We use a three-step audit process:
1. Data Review: Check training datasets for representation gaps.
2. Algorithmic Testing: Run tools like IBM Fairness 360 or Google What-If Tool.
3. Impact Analysis: Measure outcomes for protected groups (e.g., loan approvals by demographic).
Example: Uncovered a 22% bias in a client’s hiring AI—saved $3M in potential lawsuits.
Can you help us prepare for the EU AI Act?
Yes. Our EU AI Act Readiness Package includes:
1. Risk classification of your AI uses (prohibited/high-risk/limited).
2. Documentation templates (technical files, transparency notices).
3. Board training on Article 9 obligations.
Timeline: Most clients achieve compliance in 8–12 weeks.
What’s the difference between NIST CSF and ISO 27001?
NIST CSF:
Flexible, outcome-focused (Identify/Protect/Detect/Respond/Recover)
Ideal for U.S. govt. contractors
ISO 27001:
Rigorous, certification-based (114 controls)
Required for EU/global enterprises
We align programs with both frameworks for cross-border compliance.
How do you secure AI/ML systems from cyber threats?
Our AI-Secure Methodology covers:
1. Adversarial Defense: Input sanitization for LLMs (e.g., ChatGPT jailbreaking).
2. Model Integrity: Checksums for training data tampering.
3. API Shielding: Zero-trust access controls for model endpoints.
Example: Reduced a client’s AI attack surface by 74% in 6 months.
Do you provide incident response for AI breaches?
While we focus on proactive prevention, we partner with MSSPs (Managed Security Service Providers) for 24/7 breach response. Ask about our vetted partner network.
How do you handle GDPR compliance for AI systems?
We map AI data flows to GDPR’s Article 22 (automated decision-making) and Article 35 (DPIAs), ensuring:
1. Right to Explanation: Users understand AI-driven decisions.
2. Data Minimization: Only collect essential training data.
Toolkit: Includes GDPR-compliant model cards and DPIA templates.
What’s your approach to third-party vendor risk?
Our Vendor Risk Scorecard evaluates:
AI Ethics (e.g., bias audits conducted).
Cyber Posture (SOC 2 reports, pentest results).
Regulatory Alignment (e.g., HIPAA for HealthTech).
Red Flag Example: Flagged a vendor with no AI governance—saved a client $500K in fines.
How do you prioritize AI use cases for ROI?
Our AI Prioritization Matrix scores projects by:
1. Cost (implementation complexity).
2. Risk (regulatory exposure).
3. Value (revenue uplift/cost savings).
Outcome: A retail client prioritized chatbot CX over inventory AI, yielding 300% faster ROI.
What’s included in your Generative AI policy draft?
A customizable policy covering:
Approved Tools (e.g., ChatGPT Enterprise vs. open-source).
Data Rules (no PII in prompts).
Copyright Safeguards (fair use documentation).
Bonus: Includes employee training slides on “Safe LLM Use.”
Are your services tailored for startups vs. enterprises?
Yes. Every enterprise big and small have specific needs and constraints. So, we offer tailored solutions that meed their needs.
How do you charge for projects?
Options include:
1. Fixed-Fee (e.g., $25K for an EU AI Act gap analysis).
2. Retainer (e.g., $10K/month for ongoing cyber strategy).
3. Success-Based (e.g., compliance-linked bonuses).
Can you work with our existing legal/compliance teams?
Absolutely. We embed with internal teams to upskill them via:
1. Co-development sessions (e.g., policy drafting).
2. Training certifications (e.g., NIST CSF for engineers).
What APIs or tools do you use for AI bias detection?
We integrate open-source and enterprise tools depending on your stack:
Open-Source: IBM Fairness 360, Google What-If Tool, Aequitas.
Commercial: Fiddler AI, Arthur AI (for real-time monitoring).
Custom Scripts: Python libraries (SHAP, LIME) for explainability.
Example: Deployed Fiddler for a client’s loan-approval AI, reducing false negatives by 18%.
How do you handle adversarial attacks on our ML models?
We implement:
Input Sanitization: Filter malicious prompts (e.g., jailbreaking attempts on LLMs).
Model Hardening: Adversarial training (e.g., using CleverHans library).
API Security: Rate limiting, authentication (OAuth2/OIDC).
Case Study: Thwarted a GAN-based attack on a client’s facial recognition system.
Can you audit our existing MLOps pipeline for compliance?
Yes. We assess:
Data Lineage: Tracking datasets from source to model (using MLflow or Kubeflow).
Model Versioning: Git-like control (DVC, Neptune.ai).
Regulatory Logs: Audit trails for GDPR/EU AI Act (e.g., Whylogs for data drift).
Deliverable: A compliance-ready MLOps checklist.
Do you support on-premise AI deployments, or only cloud?
Both. We design governance for:
Cloud: AWS SageMaker (Model Monitor), Azure ML (Responsible AI Dashboard).
On-Prem: Docker/Kubernetes clusters with OpenShift security policies.
Tip: Hybrid? We’ll map data flows to ensure no compliance gaps.
What’s your approach to securing LLM APIs (e.g., ChatGPT plugins)?
We recommend:
Zero-Trust Architecture: API gateways (Kong, Apigee) with role-based access.
Prompt Injection Guards: Regex filters + LLM-based anomaly detection.
Data Masking: Strip PII before sending to third-party LLMs.
Toolkit: Includes a FastAPI middleware template for secure LLM calls.
How do you quantify cyber risk for AI systems?
Using FAIR (Factor Analysis of Information Risk):
Threat Modeling: STRIDE for AI (e.g., “Spoofing” synthetic voices).
Monte Carlo Simulations: Estimate breach likelihood/cost.
Output: A risk score (0–100) tied to NIST CSF tiers.
Can you help us automate compliance documentation?
Yes. We deploy:
Templates: Markdown/Confluence docs for ISO 42001 technical files.
CI/CD Integration: Auto-generate reports using GitLab CI/Jenkins.
Example: Automated EU AI Act Annex IV docs for a client’s GitHub Actions pipeline.
What’s your method for red-teaming generative AI?
Our AI Red-Teaming Protocol:
Step 1: Attack simulation (e.g., prompt injection, training data poisoning).
Step 2: Mitigation patches (e.g., fine-tuning with adversarial examples).
Step 3: Compliance validation (NIST AI RMF, MITRE ATLAS).
How do you handle model drift in production AI?
Our Drift Detection Stack:
Statistical Tests: Kolmogorov-Smirnov for data drift.
Automated Alerts: Slack/Teams hooks when thresholds are breached.
Retraining Triggers: GitOps-style approval workflows.
Tech Stack: Evidently AI, Amazon SageMaker Model Monitor.