Cyber-Led AI

The Hidden Risks of Running AI on AWS

AI is transforming how businesses operate — but most AWS environments were not designed with AI workloads in mind. When you add SageMaker, Bedrock, or GPU-backed EC2 instances to a cloud environment built for web apps and databases, the security gaps multiply fast.

The most common risk we find: overprivileged IAM roles attached to ML workloads. A SageMaker execution role with AmazonS3FullAccess or AdministratorAccess is not unusual. In a breach scenario, that role becomes an attacker’s master key to your entire account.

The second most common: unencrypted training data. S3 buckets storing proprietary datasets, customer records, or model training inputs without server-side encryption or tight bucket policies. The bucket is private today — but a single misconfigured policy change exposes everything.

Beyond access and data, there are zombie GPU instances — p3 or g4 instances left running after training jobs complete, burning $5–$30 per hour with no workload attached. We regularly find clients spending $3,000–$8,000 per month on compute they are not using. And with the rise of large language models comes a new attack surface: prompt injection, where malicious inputs manipulate AI model behavior — a risk that requires controls at the API, VPC, and application layer simultaneously.

How Our Cyber-Led AI Readiness Check Works

Step 1: Automated Assessment (Days 1–3)

We connect to your AWS environment using a read-only IAM role and run our AI security scanner alongside AWS-native tools. No agents to install. No disruption to running workloads.

What we scan:

• IAM analysis — SageMaker execution roles, Bedrock model access policies, cross-account trust relationships, unused permissions, MFA enforcement
• Data protection — S3 bucket encryption and access policies for training data, EBS volume encryption on GPU instances, KMS key policies
• Network exposure — VPC endpoints for private Bedrock access, SageMaker endpoint security group rules, public subnet exposure
• Logging completeness — CloudTrail coverage, VPC Flow Logs, SageMaker model invocation logging, Bedrock audit trails
• GPU usage patterns — Running instance inventory, utilization metrics, idle instance detection

Step 2: Report & Prioritization (Days 4–6)

Manual review follows the automated scan. Our engineers examine the logic behind role trust policies, the architecture of your AI data flows, and the completeness of your monitoring setup. Automated tools catch configuration errors — manual review catches architectural risk.

You receive a findings report with every issue ranked Critical, High, Medium, or Low — with specific remediation steps, not generic advice. Critical findings are shared verbally within 48 hours of discovery, not buried in a PDF you receive on day 7.

Step 3: Remediation Options (Day 7+)

You choose your path:

• Self-serve — Use the report to fix issues with your own team
• Co-pilot — We advise while your engineers execute
• Full-service RaaS — We remediate directly, with post-fix validation and sign-off

Most SMBs who engage for remediation reach a clean baseline within 3 weeks.

What We Check

After the Fix: Continuous AI Posture Management

A one-time assessment captures your security posture on a single day. But AI environments drift — new SageMaker endpoints get created, IAM roles get broadened to unblock a developer, training jobs leave S3 buckets open. What is secure today becomes a gap by next quarter.

Our Continuous AI Posture Management service keeps you protected after the initial fix:

• Configuration drift alerts — Real-time notification when any AI-related resource deviates from your approved baseline
• Monthly posture reports — Trend analysis of your security score, new findings, and remediated issues
• New service coverage — As you adopt new AI services (Bedrock Knowledge Bases, Amazon Q, SageMaker Pipelines), we extend monitoring automatically
• Quarterly reviews — Engineering call to review posture, update policies, and plan for upcoming AI initiatives

This is not a retainer for retainer’s sake. If your posture is clean and nothing has changed, the monthly report takes 10 minutes to review. When something needs attention, you hear from us the same day.

Who This Is For

Pre-launch AI teams evaluating whether their AWS environment is ready to host AI workloads securely. The check prevents expensive post-launch remediation and positions you to pass customer security reviews with confidence.

Engineering teams post-incident who need an independent assessment of how a breach or data exposure happened and what gaps remain. We provide a clean audit trail and remediation evidence for customers, insurers, or regulators.

CTOs and cloud architects who inherited an AWS environment and want to understand the actual security posture before committing to an AI roadmap. Knowing what you have is the prerequisite to knowing what you can safely build.

SMBs scaling AI quickly who do not have a dedicated security team. We serve as your AI security function — assessment, remediation, and ongoing monitoring — without the cost of a full-time hire.

For organizations that also need broader cloud security coverage beyond AI workloads, see our AWS Cloud Security and Compliance service.