Secure AI/ML Infrastructure Assessment
Comprehensive security assessment and hardening program for artificial intelligence and machine learning infrastructures, focusing on model security, data protection, and adversarial defense.
In scope
- Model security assessment
- Training pipeline security
- Data privacy evaluation
- Adversarial attack testing
- Infrastructure hardening
You receive
- AI security architecture document
- Model protection guidelines
- Data security controls
- Adversarial defense playbook
- Security monitoring framework
- Implementation roadmap
Tiers
Choose the depth.
Entry — market-sized scope
$16.5K
Point-in-time audit of 1 AI system (one model or one LLM application) in 1 cloud account: threat model, adversarial testing of the deployed endpoint, and a data-exposure check. Findings report only.
- ai systems
- 1
- cloud accounts
- 1
- 1 AI system
- Threat model
- Adversarial testing of the deployed endpoint
- Data exposure check
- Findings report with fixes
- — Training pipeline review
- — Infrastructure hardening
- — AI security architecture document
- — Retest
Core AI/ML Security Assessment
$75K
Up to 3 AI systems in 1 cloud account: model security assessment, training pipeline review, data privacy evaluation, adversarial testing of each system and hosting infrastructure hardening review. Delivers all six documents.
- ai systems
- 3
- cloud accounts
- 1
- Up to 3 AI systems
- Training pipeline security review
- Data privacy evaluation
- Adversarial testing per system
- AI security architecture document
- Implementation roadmap
- — MLOps toolchain and CI/CD testing
- — Multi-layer testing of RAG and agent components
- — Custom protection tooling
- — Hands-on hardening work
Advanced AI/ML Security Program
$125K
Up to 5 AI systems across up to 3 cloud accounts: adds adversarial testing across application, RAG and agent layers, training pipeline and MLOps toolchain testing, poisoning and model-theft scenarios, and one retest.
- ai systems
- 5
- cloud accounts
- 3
- Up to 5 AI systems
- App, RAG and agent layer adversarial testing
- MLOps and training pipeline testing
- Poisoning and model-theft scenarios
- One retest
- Adversarial defense playbook
- — Custom detection or guardrail tooling
- — Monitoring implementation
- — Hands-on infrastructure hardening
Enterprise AI/ML Security Platform
$195K
Up to 10 AI systems across up to 5 cloud accounts: Advanced scope plus custom guardrail and detection tooling, security monitoring implemented for the model estate, and hands-on hardening of the ML infrastructure.
- ai systems
- 10
- cloud accounts
- 5
- Up to 10 AI systems
- Custom guardrail and detection tooling
- Monitoring framework implemented
- Hands-on infrastructure hardening
- Everything in the Advanced tier
- — Ongoing managed monitoring after handover
- — Tool licence costs
- — Model retraining
Members: engagement coupons from the CISO Marketplace coupon book apply to services. There is no blanket discount.
What's inside this engagement
Phase by phase.
How a ai red teaming & ai security testing engagement runs, what happens in each phase and what you see. Exact scope, tier and timeline are fixed in your proposal and SOW.
01Architecture & threat model
Models, system prompts, retrieval sources, tools/MCP servers, memory and permissions mapped as trust boundaries.
You see · Architecture diagram and access to a test tenant.
02Attack-surface enumeration
Every input path (user, documents, web, tool results) and every action the agent can take is catalogued.
You see · Confirmation of in-scope tools and data.
03Adversarial testing
Prompt injection (direct and indirect), jailbreaks, data exfiltration, excessive agency and tool abuse, mapped to the OWASP LLM Top 10 and MITRE ATLAS.
You see · Escalation of anything exploitable in production.
04Chaining with classic weaknesses
AI findings combined with application and cloud weaknesses to show real impact.
You see · The full attack chain.
05Reporting & hardening
Reproducible transcripts, impact, and fixes at the right layer: prompt, retrieval, tool permissions or output handling.
You see · A report your AI and platform teams can act on.
Commercials
From first call to final report.
- 01
Scoping call
A practitioner, not a salesperson, walks through targets, constraints and what a good outcome looks like for you.
- 02
Proposal & rules of engagement
A fixed-scope proposal with tier, price and deliverables. Rules of engagement, contacts and out-of-bounds systems are agreed in writing.
- 03
Sign, then start
MSA and SOW are signed electronically and the deposit is paid. Only then does testing begin.
- 04
Execution
Testing runs to the agreed plan. Critical findings are escalated as they are found; you don't wait for the report.
- 05
Report & debrief
An executive summary plus technical findings with evidence, reproduction steps and fixes, walked through with your team.
- 06
Retest
Where the tier includes it, we verify your fixes and reissue the report, so auditors and customers see the issues closed.
Timelines are set per engagement in the SOW.
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Research
Latest from the blog
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Root RCE Zero-Day in Cisco Secure Email Gateway Under Active Exploitation
A critical, unauthenticated root RCE flaw in Cisco Secure Email Gateway is under active exploitation, added to CISA's KEV catalog with a September 17 federal deadline.
cloud-security · Sep 6, 2026
GPUThor and the Unpatchable Layer: When Your AI Infrastructure Risk Is Silicon, Not Software
University of Toronto researchers demonstrated a Rowhammer attack that defeats on-die ECC on NVIDIA workstation GPUs and reaches host root in about 1.1 minutes, down from roughly 22 hours. No software patch is possible, and the proof of concept is scheduled for November 15. Paired with NVIDIA's NemoClaw DNS-rebinding flaw, it defines the hardware boundary of AI infrastructure risk.
operations · Aug 25, 2026
A Tale of Two SOCs: CISA Ran the Same Attack Against Two Organizations. One Caught It in Two Minutes. One Never Saw It.
CISA advisory AA26-237A, published August 25, 2026, documents red team assessments against two critical infrastructure organizations using comparable tradecraft. Organization B isolated hosts in 2 to 20 minutes. Organization A was fully compromised — domain, cloud, and the security team's own communications — and detected nothing. CISA attributes the gap to people and process, not tooling. That finding should change how you spend.
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