What should an enterprise AI security checklist for marketing cover?

Key facts

Access control	Scoped API keys with rotation; enterprise SSO and RBAC options
Client separation	Separate keys and retrieval indexes per client or brand
Deployment	Cloud, VPC, on-prem or air-gapped for customer-data workloads
Auditability	Request, model and response logging for content review trails
Data grounding	Embeddings and RAG are live for brand guidelines and product copy
Models	30+ models behind one OpenAI-compatible API
Pricing model	Flat monthly self-serve plans; no per-token billing on self-serve
Endpoint roadmap	Images, moderation, files and fine-tuning are coming soon

TL;DR

• Classify audience and customer data separately from public brand content.
• Keep per-client keys and indexes so agency work never crosses brands.
• Treat scraped web content as untrusted input against prompt injection.
• Log generated assets and the reviewer for every published claim.
• Route legal and compliance-sensitive copy through named approvers.

How it works, step by step

1. Inventory AI use cases across copy, personalisation, SEO, research and reporting.
2. Classify content by client confidentiality, audience data and regulatory sensitivity.
3. Choose deployment per class: cloud, VPC, on-prem or air-gapped.
4. Issue per-client and per-channel keys with rotation and a central inventory.
5. Define log fields and retention: request ID, model, sources, output, approving editor.
6. Sandbox external content and require human review before publication.
7. Approve a model allow-list with evaluation evidence and re-test on version changes.

Try it yourself

Open the prompt optimizer →

Brands, audiences and data classification

Marketing content spans public copy, brand guidelines, campaign performance data, audience segments and customer personal data. The first two are low risk; the last two usually carry contractual and privacy constraints, especially in agency settings where several clients sit behind one team.

Write the client separation rule down before any shared workflow exists, because a single assistant that remembers one brand while drafting for another is a confidentiality incident waiting to happen.

Keys, agencies and least privilege

Issue a distinct API key per client, channel and environment, and separate retrieval indexes so brand knowledge cannot leak across accounts. Store keys in a secrets manager, rotate on a schedule, and revoke them when a client or freelancer relationship ends. Enterprise SSO and RBAC options keep console access aligned with account team changes.

Never place customer personal data in prompts where retrieval can supply only the segment attributes a task requires.

Residency, retention and content audit trails

Decide where processing happens and how long records live. Region selection covers many needs; VPC, on-prem and air-gapped deployment covers client contracts that require data to stay in a jurisdiction or inside an estate. Retention applies to prompts, outputs, logs and retrieval indexes, and generated assets often need to be retained as marketing records.

Log enough to reconstruct a published claim: request ID, model and version, retrieved source identifiers, output and the approving editor. See AI audit logs for a schema.

Prompt injection and approval workflows

Scraped pages, briefs and third-party content are untrusted input: treat instructions embedded in them as hostile and keep tool permissions minimal. Ground copy in current brand guidelines with citations, and require a named editor or legal reviewer before anything regulated or comparative is published. Keep an approved model list with evaluation evidence and re-test when versions change. Image and moderation endpoints are coming soon; plan asset pipelines around that status.

Honest comparison

Control area	Plugsky capability	Common gap	Owner
Client separation	Separate keys and indexes per client or brand	One assistant for all accounts	Agency operations
Identity	Scoped keys, rotation, enterprise SSO and RBAC options	Shared freelancer credentials	IT security
Data boundary	Cloud, VPC, on-prem or air-gapped deployment	Audience data path undocumented	Privacy lead
Retention	Configurable logging of prompts and outputs	Prompts kept indefinitely	Marketing operations
Injection defense	Retrieval from approved sources and minimal tool scopes	Raw web content trusted	Content engineering
Approval	Citations and structured output for editors	Unreviewed regulated claims	Brand and legal

Frequently asked questions

Does using Plugsky make us compliant?

No. Compliance is your program. Plugsky provides deployable controls - scoped keys, client separation, private deployment, logging - that you document and audit against client and privacy obligations.

How do we stop brands from mixing?

Use separate keys and retrieval indexes per client or brand, and keep conversation history scoped to the account that created it.

What should we log?

Request IDs, model names and versions, retrieved sources, outputs and the approving editor, retained under your policy so published claims can be reconstructed.

Can we process customer segments?

Yes, on private deployment where required, with retrieval restricted to the attributes a workflow needs and personal identifiers kept out of prompts.

How do we defend against prompt injection?

Treat scraped and third-party content as untrusted, ground answers in approved sources, and keep tool and agent permissions to the minimum a task requires.

Are image generation and moderation available?

Images, moderation, audio and file endpoints are coming soon. Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live today.

Where should a pilot start?

Pilot on public brand guidelines and product copy with the free plan, prove review workflows and logging, then extend to audience data on private deployment.