This topic covers the capabilities and safeguards that matter when choosing AI image tools today: photorealistic generation, workflow-focused editors, and governance mechanisms such as watermarking, provenance metadata, and intellectual‑property controls. Since image-generation models have grown more convincing and integrated into marketing pipelines, practitioners need to weigh creative features (upscaling, template editing, batch variants) against technical and legal controls that preserve attribution and manage rights. Key categories include AI Image Generators (tools for prompt-based creation, editing, and variant generation), AI Governance Tools (provenance, auditing, and policy enforcement), and AI Security Governance (risk assessment, model-level access controls). Representative tools show common use cases: Playground AI provides prompt-based image creation plus upscaling and editing; PFPMaker turns a single selfie into many stylized profile photos; Venngage’s AI Poster Generator and AdGenAI convert text or a URL into marketing-ready posters and ads; Marketr focuses on turning website content into branded social campaigns. These examples illustrate how generation and distribution are collapsing into single workflows for agencies and brands. Emerging best practices include embedding robust visible or imperceptible watermarks, attaching standardized provenance metadata (e.g., C2PA-style assertions), clear licensing and consent records, and implementing enterprise controls for usage, model access, and retention. For teams using generator tools in 2026, priorities are: ensuring sources and permitted uses are auditable, balancing image fidelity with traceability, and choosing platforms that support downstream governance (exportable metadata, access policies, and license management). The right combination of creative features and governance safeguards helps organizations scale generative image use while managing legal, reputational, and security risks.