Qualification

Active Top Secret security clearanceUnderstanding of data protectionExposure to LLM guardrail platformsExperience with cATO methodologiesActive AWS Certifications (e

Required

Active Top Secret security clearance
Current DoD 8570/8140 IAM Level II or Level III certification (e.g., Security+, CySA+, CISM, CISSP, CCISO, CAP/CISC)
3–5+ years of experience conducting security control assessments, compliance testing, or A&A/RMF activities for DoD or federal information systems
Solid operational understanding of core AWS cloud services (EC2, S3, IAM, VPCs, Security Groups, Security Hub) and how security controls function within cloud-native and CI/CD pipeline environments.
Strong working knowledge of NIST SP 800-53 (Rev. 4/5), NIST SP 800-37 (RMF), DoD Cloud Computing SRG, and FedRAMP baselines.
Demonstrated experience writing and evaluating core RMF artifacts (SSPs, SAPs, SARs, POA&Ms)
Exceptional written and verbal communication skills, with the ability to articulate technical risk clearly to executive stakeholders, Authorizing Officials, and engineering teams.
Hands-on experience navigating government GRC repositories, such as eMASS or XACTA.

Preferred

Hands-on experience mapping security controls to the NIST AI Risk Management Framework (AI RMF), the OWASP Top 10 for LLM Applications, or the DoD Responsible AI (RAI) Guidelines.
Familiarity evaluating secure design patterns for autonomous AI Agents (e.g., tool-calling permissions, sandboxing agent execution environments, prompt boundaries, and ReAct/LangGraph architectures).
Experience assessing cloud-managed AI ecosystems and foundation model platforms (e.g., AWS Bedrock, AWS SageMaker, Hugging Face Enterprise, or self-hosted open-source models).
Understanding of data protection, access controls, and boundary security for RAG pipelines and vector databases (e.g., OpenSearch Vector Engine, Pinecone, Milvus, or PostgreSQL pgvector).
Familiarity evaluating risks unique to LLMs—including prompt injection, data poisoning, model inversion, insecure output handling, and open-source supply chain vulnerabilities in AI libraries (PyTorch, LangChain, LlamaIndex).
Exposure to LLM guardrail platforms, evaluation frameworks, or AI security tools (e.g., Promptfoo, Garak, Giskard, NeMo Guardrails) used to test model robustness and output safety.
Experience with cATO methodologies, Infrastructure as Code (IaC) templates (Terraform, CloudFormation), and container security (AWS EKS/ECS, Docker).

Security Control Assessor/Representatives at Darkwolf Solutions · LumiJobs

Security Control Assessor/Representatives

Responsibilities

Qualification

Required

Preferred

Company

Darkwolf Solutions

More roles at Darkwolf Solutions