AI Security Assessment - Cedrus Strategic

What's at Stake

AI systems introduce risks that traditional security assessments miss entirely

Prompt Injection Attacks

Chatbots manipulated to expose PHI, execute unauthorized actions, or leak proprietary data through carefully crafted inputs.

Agentic AI Escape

Autonomous agents with tool access (Copilot Studio, etc.) tricked into transferring funds, sending phishing emails, or deleting records.

Supply Chain Compromise

Vendor models with unknown training data, backdoors, or exploitable vulnerabilities that propagate into your systems.

Compliance Violations

GDPR fines up to €20M or 4% of revenue. HIPAA breaches at $50K per record. AI-specific regulations emerging rapidly.

Why This Assessment Is Different

Traditional Assessments

This AI Security Assessment

Vague "high/medium/low" risk metrics

Framework quantified, observable risk scoring with decision-tree methodology

Treats AI like any other app

Agentic AI Focus: Explicit assessment of autonomous agents with tool access, non-linear risk areas

Generic "vendor risk" checkbox

Supply Chain Depth: Model provenance, MCP security, API dependencies, training data lineage

Hand-wave "compliance frameworks"

Specific Mapping: NIST AI RMF, ISO 42001, OWASP Top 10 for LLM, HIPAA/SOX/PCI for AI

Ignores machine identity

OAuth Architecture: Service principals, delegated permissions, API key lifecycle

"Here are your gaps"

Implementation-Ready: Reference architectures, control specs, phased roadmap

Solution Roadmap

Quantified, Defensible AI Risk Assessment in 5 Phases

Business-Aligned Risk Calibration

Executive workshop defining AI-specific impact levels
Aligns security architecture to your actual risk tolerance
Eliminates "we should protect everything equally" paralysis

Complete AI System Inventory & Risk Scoring

Every AI system classified using classification framework (Low/Medium/High/Critical)
Agentic AI systems (autonomous agents) explicitly identified
Model provenance and supply chain documented

Gap Analysis & Threat Modeling

AI-specific threats: prompt injection, model extraction, adversarial attacks, agent escape scenarios
Compliance gaps: NIST AI RMF, ISO 42001, OWASP Top 10 for LLM, sector regulations
Machine identity review: OAuth flows, service principals, delegated permissions

Reference Architecture Design

Risk-based control selection (control framework)
Network segmentation strategies
Model governance framework
Secure AI deployment patterns

Phased Implementation Roadmap

Quick wins (0-3 months) → Foundation (3-6 months) → Strategic enhancements (6-12 months)
Cost-benefit analysis with ROI projections
Resource requirements and timeline

Executive Narrative Report

50-80 pages

Quantified risk classifications for each AI system reviewed
AI-specific threat scenarios with business impact
Compliance crosswalk matrices (NIST, ISO, OWASP, sector-specific)
Reference architectures for secure AI deployment
Phased implementation roadmap with cost-benefit analysis

Executive Presentation

Board/C-suite ready

Visual risk heatmaps and key findings
Clear, actionable recommendations
Budget and timeline proposals
Strategic context for leadership decisions

The Bottom Line

Every day without understanding your AI security posture increases risk exposure.

Attackers are perfecting prompt injection techniques. Regulators are developing AI-specific enforcement actions. Autonomous agents have access to sensitive systems with inadequate controls.

You need to know:

Which AI systems pose the greatest risk (and why)
Where your critical security gaps are
How to deploy AI securely under good governance while maintaining business velocity

This assessment delivers those answers in 6-12 weeks with quantified, defensible, implementation-ready recommendations.

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