AI Governance and Security FAQ

AI tools can produce content that's factually wrong while sounding completely confident. They fabricate citations, invent statutes that don't exist, attribute quotes to people who never said them, and generate plausible-looking but incorrect numbers. This is real and persistent.

The mitigation is not to avoid AI. It's to treat AI output as a draft that requires human verification — especially anything going to clients, regulators, or anywhere with consequences. The principle to internalize: you, not the AI tool, are responsible for what your business says and does.

Prompt injection is an attack where instructions are hidden in content an AI tool reads — a web page, an email, a document — and those hidden instructions hijack the AI's behavior. The AI ends up following the attacker's instructions instead of yours.

For AI agents that take actions, prompt injection is much more serious — an attacker who can influence what an agent reads may cause it to send messages, transfer files, or take other actions outside its intended scope.

Yes — and they already do. AI has measurably improved the quality of phishing emails, voice and video impersonations, and malware that evades signature-based defenses. The threat landscape has shifted, and a security program that hasn't been updated in the last two years is increasingly out of step with it.

Shadow AI is the use of AI tools by employees without their employer's knowledge or approval. Surveys consistently find that the majority of employees who use AI for work are using tools their employer hasn't approved — meaning your sensitive information is being processed by services you have no visibility into.

A practical AI acceptable use policy should address, at minimum:

• Which AI tools are approved, and which are prohibited.
• Whether personal AI accounts may be used for business work (typically: no).
• What types of company or client information may be submitted to AI tools.
• How AI-generated content must be verified before use.
• Whether and when to disclose AI use to clients.
• How to use AI when web search is involved.
• How to use AI agents and automation features.
• How to report problems, including suspected data exposure.
• Required training and acknowledgment by employees.

Coretelligent helps clients develop and operationalize these policies through CoreArmor Govern.

Business-grade Copilot and Claude subscriptions on managed tenants generally commit that prompts and outputs aren't used to train models, and that data stays within tenant boundaries. "Safe" still depends on the data — regulated health data, cardholder data, and secrets like credentials should get extra scrutiny or avoid AI tools entirely.

AI used as a productivity aid generally doesn't require proactive disclosure. AI that takes autonomous actions in client environments or makes substantive decisions affecting client outcomes should be disclosed. Never misrepresent AI involvement, and always answer honestly if asked directly.

Yes — with appropriate governance:

• Each agent should have a defined scope of authority.
• Agents should have their own identities, distinct from the humans who use them.
• Consequential actions should require human confirmation.
• All agent activity should be logged and reviewable.
• Agents should be tested for response to manipulation before access to sensitive systems.

An agent with broad authority manipulated to exfiltrate data, modify records, transfer funds, or send fraudulent communications — while behaving as though it were following its instructions. Mitigations: limit authority, require human approval for irreversible actions, isolate sensitive operations, monitor continuously.

Three questions: What's the worst thing this agent could do if manipulated? What controls prevent that? Who's accountable when it makes a mistake? Match the level of control to the level of authority you're giving the agent.

Beyond standard SaaS diligence: training data practices, data residency and retention, sub-processor disclosure, model update notification, prompt/output logging, incident response, and whether commitments are reflected in the contract itself — not just marketing.

The security of an AI feature is the security of the weakest link in the chain. Your vendor's strong security doesn't protect you if the AI provider underneath has weaker practices.

Existing frameworks largely apply. Newer frameworks like the NIST AI Risk Management Framework and ISO/IEC 42001 focus specifically on AI governance gaps — model behavior, prompt injection, agent oversight.

Don't submit regulated data to free/consumer AI tools, let AI make decisions regulations require humans to make, use AI customer-facing without required disclosure, or rely on AI for compliance-critical functions without verification and an audit trail.

CoreArmor Govern — policy development, compliance mapping. SSPM — shadow AI discovery. Vendor Risk Management — AI vendor due diligence. Security Awareness Training — AI-specific training.

Launching soon: AI Security Governance (program development, agent risk assessment, incident response) and Information Protection Services (data loss prevention, classification, platform management).

Inventory actual AI use (including shadow AI), decide what's approved, publish and train on a basic policy, stand up a governance body, then build out vendor management, incident response, and agent governance from there.

Reach out to your account team to start a conversation about where your business is today and what to focus on first.