Review: AI Strategy and Security - What Executive Teams Need To Know
AI Strategy and Security is a practical guide for leaders who want AI programs that actually deliver. It treats AI as an enterprise capability, not a one-off project-tying strategy, security, governance, and operations into a single operating model.
If you set budgets, own risk, or sponsor transformation, this book gives you a clear blueprint: what to prioritize, how to staff, and which controls keep the business safe while you scale.
About the author
Dr. Donnie W. Wendt is a Lecturer at Columbus State University and a seasoned AI and cybersecurity professional. He previously wrote The Cybersecurity Trinity: Artificial Intelligence, Automation, and Active Cyber Defense and co-authored the AI Adoption & Management Framework. That mix of academic rigor and operator experience comes through on every page.
What the book does well
- Anchors AI to business outcomes. Initiatives map to clear objectives-differentiation, market expansion, process optimization, and workforce enablement-backed by concrete examples from finance, healthcare, retail, manufacturing, and energy.
- Builds enterprise readiness. Readiness assessments cover technical capabilities, data maturity, skills, and culture. Infrastructure choices (cloud, on-prem, hybrid) are weighed against compliance, scalability, and cost.
- Defines the org you need. Roles across AI engineering, data science, MLOps, security, governance, and ethics are spelled out, including titles like Chief AI Officer, AI architect, AI security engineer, and AI ethics officer. The emphasis is on collaboration patterns, not org charts for their own sake.
- Security from day one. It details AI-specific threats-data poisoning, model manipulation, backdoors, privacy attacks, and supply chain risks-and ties each to actionable defenses: data controls, change management, API protection, adversarial testing, monitoring, and drift analysis.
- Governance that scales. Inventory, third-party oversight, continuous monitoring, and risk workflows are treated as normal management practices, not exceptions. Regulatory context spans U.S. and international requirements with pointers to emerging AI standards.
- Responsible AI as a process. Transparency, explainability, accountability, and bias mitigation connect directly to impact assessments, documentation, and human oversight during design and operation.
- Treats AI as a living system. Deployment, monitoring, lifecycle management, performance evaluation, retraining, and end-of-life decommissioning are built around feedback loops and metrics.
Security you can act on now
- Threats to expect: Data poisoning, model manipulation, backdoor insertion, privacy attacks, and compromised third-party datasets or pre-trained models.
- Controls to implement: Strong data handling and lineage, model change management and approval gates, authenticated API access with rate limits, adversarial testing before and after release, continuous monitoring with drift alerts, and supply chain due diligence for models and datasets.
Governance and regulatory alignment
The book's governance model is pragmatic: maintain a live inventory of AI systems, define accountability, standardize risk assessments, and monitor continuously. That approach aligns well with recognized guidance like the NIST AI Risk Management Framework and upcoming obligations from the EU AI Act.
How to use this book as an executive
- Set the target. Pick 2-3 business objectives and define measurable outcomes. Tie every AI initiative to one of them.
- Run a readiness check. Assess data quality, infrastructure, skills, and culture. Close gaps before scale.
- Establish the core team. Name a CAIO (or equivalent), align Security, Risk, Legal, and Ops, and clarify decision rights.
- Codify security and governance. Approve a minimal control set (data, model, API, monitoring, third-party) and make it default for all teams.
- Pilot with intention. Limit scope, instrument everything, and define clear go/no-go criteria and decommission paths.
- Measure and iterate. Track business value, model performance, risk posture, and cost. Adjust quarterly.
Operational details executives will appreciate
- Infrastructure trade-offs: Cloud for speed and scale, on-prem for strict control, hybrid for regulated workloads with burst capacity.
- Lifecycle guardrails: Versioning for data and models, approval workflows, audit trails, and rollback plans.
- People systems: Training pipelines, cross-functional rituals (security testing, red teaming), and ongoing upskilling as a budgeted line item.
- End-of-life discipline: Retire models that no longer meet performance, cost, or risk thresholds.
Who should read this
CISOs, security architects, risk leaders, and technology executives who need one reference that connects strategy with security, governance, and operations. It's also useful for CEOs, COOs, and CFOs who need to fund AI responsibly without creating hidden risk or runaway cost.
Bottom line
AI Strategy and Security is a straightforward playbook for building AI capability the right way-aligned to outcomes, secured by design, and governed for scale. If you're setting your 12-18 month roadmap, this will help you choose where to start, what to staff, and how to keep the program accountable.
Next step for your team
If you're building leadership and practitioner skills in parallel with adoption, explore role-based learning paths at Complete AI Training - Courses by Job or review the latest programs here: Latest AI Courses.
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