AI Security at CyberDesserts

Shadow AI is employees using AI tools like ChatGPT, Claude, Gemini or niche AI services without IT approval or oversight. 68% of organisations have employees using unsanctioned AI platforms, often with sensitive data. Unlike traditional Shadow IT, these tools process and learn from your data in ways you cannot see or control.

The risk is not theoretical. IBM's Cost of a Data Breach report puts the average breach at $4.88 million globally. When employees paste customer data, source code or financial records into public AI tools, you have handed your crown jewels to a third party with unknown security practices.

Traditional Shadow IT involved unauthorised software or cloud services. Shadow AI adds three dimensions:

• Active data analysis: the tools analyse and potentially store your data for model training
• Conversational interfaces: employees naturally paste entire documents, not just snippets
• Immediate blast radius: one employee can expose thousands of records in seconds, and you will never know it happened

Start with network monitoring. Most AI services use distinctive API endpoints you can track through your firewall or SIEM. Look for traffic to openai.com, anthropic.com, claude.ai and similar domains. Do not stop there: many tools offer local deployments or browser extensions that bypass network monitoring.

• Web proxy logs to catch browser-based usage
• DLP tools monitoring clipboard activity
• Endpoint agents tracking AI application installs
• User surveys: sometimes the direct approach works best. Ask your teams what they use.

Build in this order: policy first, discovery second, controls third.

Start with a clear AI Acceptable Use Policy that lists approved tools, prohibited use cases and data classification guidelines. Be specific; "no unauthorised AI" is not a policy.

• Configure DLP rules to block sensitive data patterns being pasted into web forms or uploaded to AI services
• Implement network-level blocking for high-risk AI domains
• Provide approved alternatives so usage is channelled safely rather than driven underground

The goal is not to stop AI usage but to channel it safely.

Maturity is not binary. Organisations typically progress through three stages: Foundational (policies exist, enforcement is minimal), Developing (active monitoring with some technical controls) and Adaptive (comprehensive governance with continuous validation).

Score yourself honestly across four domains; the gaps are your roadmap:

• Governance and policy: do you have documented AI usage rules?
• Technical controls: can you detect and prevent unauthorised usage?
• Data handling: are sensitive data classifications enforced?
• Employee awareness: do people understand the risks?

Quick wins, 2 to 4 weeks: publish your AI policy, configure basic DLP rules, block the riskiest AI domains.

Detection capability, 1 to 3 months: SIEM alert integration, SOC team training, baselining normal behaviour.

Full maturity, 6 to 12 months: endpoint agents, identity provider integration for user-level controls, automated incident response workflows, continuous monitoring.

Most organisations see measurable risk reduction within the first 90 days if they prioritise ruthlessly.

Lead with business risk, not technical controls. Frame it as protecting competitive advantage, not buying more security tools. A concrete scenario lands harder than any slide: right now, any employee can paste your product roadmap into a public AI tool, and you would not know until a competitor announces the same features.

Then quantify the exposure. If 68% of organisations have Shadow AI and you employ 500 people, that is potentially 340 people with unsupervised access to AI tools. Price the breach using IBM's $4.88M average and a $100K investment in controls starts to look reasonable.