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OWASP · NIST AI RMF · AWS Security

AI Cybersecurity
on AWS

Protect Your AI Systems — From Threat Assessment to Continuous Defense

A comprehensive AI security program that covers threat modeling, prompt injection defense, data leakage prevention, agentic AI governance, and regulatory compliance. Built on AWS-native security services, mapped to OWASP Top 10 for LLM, NIST AI RMF, and EU regulatory frameworks. Designed for organizations that deploy AI in production and cannot afford to get security wrong.

Explore the program

The AI Threat Landscape

AI systems introduce attack surfaces that traditional security tools were never designed to detect.

73%
of production AI deployments are vulnerable to prompt injection
59%
of organizations have no agentic AI security strategy
$6.08M
average data breach cost in financial services
97%
of orgs that experienced AI breaches lacked basic access controls

Sources: OWASP, Verizon DBIR, IBM Cost of a Data Breach 2025, CSA

Our Services

Five focused service lines covering AI risk, secure architecture, prompt protection, governance, and operational monitoring.

Assess

AI Security & Risk Assessment

Identify and assess risks related to AI adoption across your environment.

Architect

Secure AI Architecture & Implementation

Design and deploy secure and scalable AI architectures on AWS.

Protect

Input & Prompt Security

Protect AI systems against prompt-based attacks and misuse.

Govern

AI Governance & Compliance

Ensure your AI systems meet regulatory and governance requirements.

Monitor

Monitoring & Operational Security

Ensure continuous visibility and protection of AI systems.

Service Details

Practical security services for AI adoption, secure deployment, governance, and continuous protection.

AI Security & Risk Assessment

Identify and assess risks related to AI adoption across your environment.

  • AI-specific risk identification (prompt injection, data leakage, misuse)
  • Security posture assessment for AI workloads
  • Compliance alignment (NIST, DORA, ISO)
  • Recommendations for secure AI adoption

Secure AI Architecture & Implementation

Design and deploy secure and scalable AI architectures on AWS.

  • Secure integration with Amazon Bedrock and AI services
  • Implementation of security best practices (IAM, encryption, network isolation)
  • Secure API exposure and access control
  • Design of robust and production-ready AI pipelines

Input & Prompt Security

Protect AI systems against prompt-based attacks and misuse.

  • Defense against prompt injection
  • Input validation and sanitization
  • Separation of system and user prompts
  • Use of structured input templates

AI Governance & Compliance

Ensure your AI systems meet regulatory and governance requirements.

  • AI governance frameworks and policies
  • Data protection and privacy controls
  • Traceability and auditability of AI usage
  • Alignment with regulatory standards

Monitoring & Operational Security

Ensure continuous visibility and protection of AI systems.

  • Monitoring of AI usage and activity
  • Logging and traceability of AI interactions
  • Integration with SIEM platforms such as Microsoft Sentinel
  • Detection of abnormal or suspicious AI behavior

Standards & Frameworks We Apply

Every control is mapped to recognized standards. No proprietary checklists — only frameworks your auditors already trust.

OWASP Top 10 for LLM (2025)

Industry-standard risk taxonomy for LLM applications. Foundation for all threat modeling and control design.

OWASP Agentic AI Threats

First threat model for autonomous AI agents — memory poisoning, tool misuse, privilege compromise, cascading failures.

NIST AI Risk Management Framework

US federal framework for AI risk. Core functions: Govern, Map, Measure, Manage. Control family mapping (AC, SC, SI, PL).

DORA / NIS2 / EU AI Act

European regulatory triad for financial services. Cross-mapped controls, unified incident classification, liability management.

ISO 42001

International standard for AI management systems. Compliance frameworks now mandate specific controls for prompt injection and model governance.

AWS Well-Architected AI Lens

AWS-native guidance for responsible AI. Combined with Bedrock Guardrails, Security Hub, and the AWS GRC Guide for FSI.

All controls are documented with NIST control family mappings and exportable evidence packs for audit.

AWS Security Stack

We implement using AWS-native services — no third-party agents, no external data flows.

AI Layer

  • Amazon Bedrock Guardrails — content filtering, PII redaction, topic controls, grounding checks
  • Bedrock Agents — managed agent orchestration with built-in security and tool permissions
  • SageMaker Model Monitor — drift detection, bias monitoring, quality alerts
  • SageMaker Clarify — fairness analysis on model outputs

Infrastructure Layer

  • IAM Identity Center — federated SSO, least-privilege roles, permission sets per agent
  • VPC + PrivateLink — private subnets, VPC Endpoints, no public exposure
  • KMS — encryption at rest and in transit for all model interactions
  • Secrets Manager — API keys and credentials never in code or environment variables

Detection & Response

  • Security Hub — consolidated findings, automated alerting, compliance scoring
  • GuardDuty — threat detection across accounts and workloads
  • CloudTrail + CloudWatch — full audit trail, every API call logged, custom dashboards
  • AWS Config — continuous compliance monitoring, drift detection, automated remediation

Delivery Model

Assessment to hardened production in 6–10 weeks. Continuous operations thereafter.

Phase 1

AI Security Assessment

1–2 weeks
  • Inventory all AI workloads, models, agents, and data flows
  • Threat model against OWASP LLM Top 10 + Agentic AI threats
  • Regulatory gap analysis (DORA, NIS2, EU AI Act)
  • Prioritized risk register and remediation roadmap
Phase 2

Controls Design

1–2 weeks
  • Security architecture with NIST control family mapping
  • Guardrail configuration design (Bedrock + custom)
  • Agent permission model and tool-level authorization
  • Incident response plan covering all three EU regulations
Phase 3

Implementation

2–4 weeks
  • Bedrock Guardrails deployed and tuned
  • Input validation, output filtering, RAG security controls
  • IAM hardening, VPC isolation, encryption configuration
  • Monitoring pipeline — CloudWatch, Security Hub, custom dashboards
  • Supply chain scanning and SBOM generation
Phase 4

Validation & Handover

1 week
  • Red-team testing — prompt injection, data exfiltration, privilege escalation
  • Compliance evidence pack generation
  • Security review and sign-off with stakeholders
  • Operational runbooks and team onboarding
Phase 5

Managed Security Ops

Ongoing
  • Continuous monitoring and incident response (SLA-backed)
  • Quarterly red-team exercises and guardrail recalibration
  • Regulatory change tracking and compliance updates
  • Monthly security report: findings, remediations, posture score

Why This Approach

Standards-led

Every control maps to OWASP, NIST, or ISO — no proprietary frameworks, no vendor lock-in on methodology

AWS-native

Bedrock Guardrails, Security Hub, GuardDuty, IAM — we use the services already in your account, not third-party agents

Regulatory-ready

DORA, NIS2, EU AI Act cross-mapped from day one. Evidence packs ready for your next audit cycle

Actionable

Not a slide deck of recommendations — we implement controls, deploy guardrails, and harden your stack

Continuous

Security is not a one-off assessment. Ongoing monitoring, quarterly red-teams, and guardrail recalibration

Sectors

Built for organizations deploying AI in environments where a security failure means regulatory penalties, financial loss, or reputational damage.

Financial Services Insurance Private Equity & Asset Management Banking Healthcare Regulated SaaS Government & Defense Legal

Ready to secure your AI systems?

Let's assess your AI risk surface and build a security program that satisfies your auditors and protects your business.