Enterprise AI Security: What Your CISO Is Worried About

Every AI conversation eventually reaches the security team. And they have legitimate concerns that business advocates often dismiss too quickly.

Understanding these concerns – and addressing them properly – is essential for successful AI adoption. Here’s what your CISO is probably worried about.

The Data Exposure Question

The fundamental concern: when you use AI services, where does your data go?

For cloud AI services (OpenAI, Azure, AWS, etc.):

  • Your prompts and data are transmitted to provider servers
  • They may be stored temporarily or permanently
  • They may or may not be used to train future models
  • Data residency and sovereignty become complicated

The specific worries:

  • Sensitive business information in prompts
  • Customer PII in processed documents
  • Trade secrets exposed during analysis
  • Compliance violations from data transfer

The mitigation:

  • Understand exactly what happens to your data (read the ToS, ask specific questions)
  • Use enterprise tiers with explicit data handling commitments
  • Implement data classification to prevent sensitive data in AI systems
  • Consider private deployment options where necessary

Shadow AI

This is arguably the biggest security concern right now: employees using AI tools without IT knowledge or approval.

The reality: Your employees are already using ChatGPT, Claude, and other tools. They’re pasting customer data, code, and internal documents into systems you don’t control.

The risk:

  • No governance over what data goes where
  • No audit trail
  • No ability to respond to data exposure
  • Potential compliance violations at scale

The response:

  • Accept that prohibition doesn’t work – people will find workarounds
  • Provide approved alternatives that meet business needs – AI consultants Brisbane like Team400 offer secure, governed alternatives to consumer AI tools
  • Implement technical controls (network monitoring, DLP) where appropriate
  • Educate staff on appropriate use
  • Create clear, practical policies

Shadow AI is a symptom of unmet need. Address the need and the shadow use decreases.

Prompt Injection and Manipulation

AI systems can be manipulated through carefully crafted inputs.

Prompt injection: Inputs designed to make the AI ignore its instructions or reveal information it shouldn’t.

Data poisoning: If AI learns from user feedback, malicious feedback can degrade performance or introduce biases.

Model extraction: Techniques to reverse-engineer proprietary AI capabilities.

The implications:

  • Customer-facing AI could be manipulated to behave inappropriately
  • Internal AI could be tricked into revealing sensitive information
  • Competitors could potentially extract valuable IP

The mitigation:

  • Input validation and sanitisation
  • Output filtering for sensitive information
  • Monitoring for anomalous queries
  • Regular security testing of AI systems
  • Clear boundaries on what AI systems can access

Authentication and Authorisation

AI systems that access your data need appropriate access controls.

The concern: An AI that can answer any question about your organisation is powerful – and dangerous if access isn’t controlled.

Questions to answer:

  • What data can the AI access?
  • Does access respect existing role-based permissions?
  • How are permissions managed as they change?
  • What audit trail exists?

Example problem: An AI knowledge system that lets any employee ask about any document, bypassing the access controls those documents normally have.

The mitigation:

  • AI access should mirror existing data permissions
  • Implement user-context-aware AI responses
  • Maintain audit logs of AI queries and responses
  • Regular access reviews for AI systems

Third-Party AI Risk

When you use AI vendors, you inherit their security posture.

Assessment areas:

  • How do they protect your data?
  • What’s their security certification status (SOC 2, ISO 27001, etc.)?
  • What happens if they experience a breach?
  • Who has access to your data within their organisation?
  • What are their data retention and deletion policies?

Contractual considerations:

  • Data handling obligations should be explicit
  • Breach notification requirements
  • Audit rights
  • Indemnification provisions
  • Compliance commitments

Don’t assume vendor security is adequate. Verify it.

AI-Generated Code Risks

Developers are increasingly using AI to generate code. This creates security considerations:

The risks:

  • Generated code may have vulnerabilities
  • AI might suggest deprecated or insecure libraries
  • Proprietary code might be exposed through AI coding assistants
  • Security review processes may not catch AI-specific issues

The mitigation:

  • Security scanning of AI-generated code
  • Code review requirements regardless of source
  • Training developers on AI coding risks
  • Policies on what code/information can be shared with coding assistants

Governance Framework

Security requires governance. Elements of an AI security governance framework:

Policies:

  • Acceptable use policy for AI tools
  • Data classification requirements for AI use
  • Vendor assessment requirements
  • Incident response procedures for AI-related issues

Processes:

  • AI system security assessment before deployment
  • Ongoing monitoring and audit
  • Regular security testing
  • Vendor security review and management

Controls:

  • Technical controls (DLP, monitoring, access management)
  • Administrative controls (policies, training, reviews)
  • Physical controls where applicable

Accountability:

  • Clear ownership of AI security
  • Reporting structure for AI security issues
  • Integration with existing security governance

The Practical Path Forward

Security teams often default to “no” when faced with new technology. This doesn’t work for AI because business pressure is too high.

A better approach:

1. Enable rather than block: Provide secure paths to AI adoption rather than trying to prevent all use.

2. Risk-tier your approach: Not all AI use is equally risky. Differentiate between high-risk (sensitive data) and lower-risk (general productivity) use cases.

3. Start with visibility: Before controlling, understand what AI use is happening. Shadow AI assessment is a valuable first step.

4. Partner with business: Security teams that understand business needs and work collaboratively get better outcomes than those who impose controls unilaterally.

5. Accept imperfection: Perfect security is impossible. Aim for appropriate security that balances risk management with business enablement.

The Conversation to Have

Security and business teams need to have an honest conversation:

Security should articulate:

  • Specific risks and their potential impact
  • Minimum requirements for AI use
  • Resources needed to support secure AI adoption
  • Willingness to find workable solutions

Business should articulate:

  • Why AI is needed for business objectives
  • Willingness to operate within reasonable constraints
  • Commitment to proper governance
  • Resources to support security requirements

The organisations that get AI security right are those where this conversation happens early and both sides approach it constructively.

Final Thought

AI security concerns are legitimate, not obstacles to be dismissed. At the same time, blocking AI adoption entirely isn’t realistic.

The path forward is thoughtful governance: understanding the risks, implementing appropriate controls, and enabling AI use within reasonable boundaries.

Get this right and you can capture AI value while managing risk appropriately. Get it wrong and you either suffer security incidents or fall behind competitors who figure it out.

Neither extreme – reckless adoption or paralytic caution – serves the organisation.