Cloud AI Security - Manager

Summary Position Summary Cyber Deloitte Cyber understands the unique challenges and opportunities businesses face in cybersecurity. Join our team to deliver powerful insights to help our clients navigate the ever-changing threat design, and technology as we partner with clients to transform finance. Job Title: Manager - Cloud AI Security Are you interested in working in a dynamic environment that offers opportunities for professional growth and new responsibilities? If so, Deloitte could be the place for you. Traditional security programs have often been unsuccessful in unifying the need to both secure and support technology innovation required by the business. Join Deloitte's Cyber Cloud AI Security team and become a member of the largest group of cybersecurity professionals worldwide. Work you’ll do As a Cloud AI Security Manager, you will lead client engagements focused on securing AI, generative AI, and ML workloads across cloud platforms. You will help clients define and implement secure AI strategies, architectures, governance models, and control frameworks across the AI lifecycle, including data ingestion, model development, validation, deployment, runtime monitoring, and retraining. You will be expected to lead engagement delivery, manage senior client stakeholders, guide cross-functional teams, mentor junior practitioners, and drive high-quality outcomes across AI security, cloud security, governance, compliance, and risk management. Responsibilities as a Cloud AI Security Senior Consultant will include: • Lead client engagements focused on AI, GenAI, and ML security across AWS, Microsoft Azure, and Google Cloud environments. • Lead the design and implementation of AI security solutions aligned to client business objectives, enterprise architecture, regulatory expectations, and security requirements. • Oversee AI security assessments for cloud-hosted AI/ML and GenAI workloads, evaluating controls against frameworks and standards such as NIST CSF, CSA CCM, ISO 27001, NIST AI RMF, and ISO 42001. • Lead threat modeling, architecture reviews, and control design for AI and GenAI solutions, including risks related to prompt injection, jailbreaks, insecure output handling, excessive agent permissions, sensitive data exposure, model misuse, and insecure tool or connector use. • Define secure AI reference architectures spanning model endpoints, APIs, retrieval pipelines, vector stores, plugins, agent frameworks, orchestration layers, and external integrations. • Guide secure deployment and configuration of cloud-native AI services including Amazon Bedrock, Amazon SageMaker, Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and Google Vertex AI. • Oversee implementation of platform-specific controls such as private connectivity, encryption, key management, secrets management, access restrictions, tenant isolation, content safety controls, and service-native logging and monitoring. • Lead DevSecOps, MLOps, and MLSecOps integration by embedding security checks, policy validation, secrets handling, model governance, and compliance controls into CI/CD and ML pipelines. • Establish secure AI lifecycle controls across data ingestion, training, validation, deployment, runtime monitoring, incident response, drift review, and retraining. • Lead AI governance and compliance activities including model inventory, risk classification, approval workflows, traceability, control mapping, audit readiness, and policy alignment. • Drive AI security posture management using cloud-native tooling, CNAPP, CSPM, CWPP, DSPM, SIEM, and related capabilities to identify misconfiguration, data exposure, anomalous activity, and policy violations. • Oversee AI-specific monitoring and incident response capabilities for suspicious prompt behavior, agent misuse, unauthorized model access, data leakage, and control failures. • Lead or coordinate AI security testing activities including misuse-case testing, prompt attack validation, model guardrail reviews, red-team support, and remediation planning. • Manage client stakeholders across security, cloud engineering, DevOps, architecture, data, ML engineering, privacy, legal, and risk teams. • Lead teams of analysts, consultants, and senior consultants by assigning work, reviewing outputs, coaching team members, and ensuring quality of delivery. • Contribute to business development, proposals, solutioning, thought leadership, reusable assets, and practice development initiatives related to cloud AI security. The team Deloitte Cyber team helps complex organizations more confidently pursue their growth, innovation and performance agendas through proactive management of the associated cyber risks. Our professionals provide advisory and implementation services that integrate risk, regulatory, and technology skills to help clients transform their legacy programs into proactive cyber risk programs. Join the team developing the future state of cyber risk solutions. Learn more about Deloitte Advisory’s Cyber Risk Services practice. Required: • 9+ years of experience in cloud security, cybersecurity consulting, cloud engineering, DevSecOps, AI security, or related disciplines. • Demonstrated experience leading client-facing engagements or major workstreams in security, cloud, or AI-related programs. • Experience designing, assessing, or implementing security controls for cloud-hosted applications, platforms, and AI/ML environments. • Strong experience with one or more major cloud platforms, with working knowledge across AWS, Microsoft Azure, and Google Cloud. • Strong understanding of AI/ML and GenAI security concepts, including prompt injection, model misuse, data leakage, secure model access, agent security, model governance, and responsible AI controls. • Experience implementing cloud security controls across IAM, network security, encryption, key management, secrets management, logging, monitoring, and policy enforcement. • Experience with Infrastructure as Code and automation using tools such as Terraform, CloudFormation, Ansible, Bash, and PowerShell. • Experience with DevSecOps, CI/CD, and secure engineering practices, including policy validation, secrets handling, and continuous compliance. • Familiarity with cloud AI services such as Amazon Bedrock, Amazon SageMaker, Azure AI Foundry, Azure Machine Learning, Azure OpenAI, Mythos, Anthropic, and Vertex AI. • Familiarity with governance and security frameworks such as NIST CSF, ISO 27001, NIST AI RMF, ISO 42001, and related control frameworks. • Strong written and verbal communication skills, including experience communicating technical and risk matters to executive and non-technical stakeholders. • Experience leading teams, mentoring junior practitioners, and managing quality across client deliverables. Preferred: • Experience leading AI security assessments, architecture reviews, control design, remediation programs, or transformation initiatives across one or more major cloud platforms. • Deep hands-on experience with identity and access management, including AWS IAM, Microsoft Entra ID, Azure RBAC, and Google Cloud IAM. • Experience designing governance and guardrails for cloud AI environments using services such as AWS Organizations, AWS Control Tower, Azure Policy, Security Command Center, and equivalent policy enforcement capabilities. • Experience with cloud-native monitoring and security tooling such as AWS Security Hub, CloudTrail, AWS Config, Microsoft Defender for Cloud, Azure Policy, and Google Security Command Center. • Experience with CNAPP, CSPM, CWPP, DSPM, SIEM, vulnerability management, container security, and posture management tools supporting AI workloads. • Experience with prompt security, content safety, model guardrails, AI runtime monitoring, and agent security for generative AI applications. • Exposure to AI red teaming, misuse-case testing, prompt attack validation, or adversarial testing of LLM-enabled