Tannu Jiwnani is a cybersecurity leader with a passion for resilience and community, focusing on incident response, IAM, and threat detection.
Artificial intelligence (AI) transforms business operations by streamlining tasks, automating decision-making, and generating insights quickly. However, this increase in AI usage led to the distinction between officially approved and well-signed AI tools (safe AI) and unauthorized, unsupervised use (Shadow AI). Security leaders need to address this growth difference.
With many employees accessing powerful AI tools through a web browser, it is important to understand the risks and responsibilities associated with AI adoption. This article explains what Shadow AI is, its distinction with Safe AI, and the role of security teams in maintaining enterprise security, compliance and trust during AI adoption.
Landscape definition: ShadowAI Vs. Safe AI
Shadow AI includes AI tools such as Public LLMS, Generated Image Tools, or Custom Machine Learning Models without it, and security or compliance approvals. Examples include marketing with content using CHATGPT, developers using GitHub Copilot, or an analyst uploading customer data to an external AI tool. These actions, although well-intentioned, can pose serious risks.
SAFE AI refers to reviewed AI tools managed by a security and compliance team with appropriate access control, logging and policies. This includes Enterprise LLM with privacy controls (such as Azure Openai or ChatGpt Enterprise), as well as internal development models using MLOPS and vendor tools under Data Processing Agreements (DPA).
Why Shadow AI is Growing
AI adoption is faster than most organizations can control. Here’s the reason behind the surge in ShadowAI:
• Speed and convenience: Employees often don’t need to install, allowing instant access to AI tools online.
•Productivity Pressure: Teams are rewarded for speed, not compliance. AI helps you hit KPIs faster.
• Lack of consciousness: Many employees are unaware that using external AI tools can violate security or compliance policies.
• Governance delay: Organizations often struggle to update their policies quickly enough to keep up with AI innovation.
The risk of shadow ai
The use of fraudulent or uncontrolled AI tools poses a significant risk to your organization. One of the main concerns is data leakage. When employees upload sensitive, unique or regulated information to a public AI model, they may inadvertently publish important data. Even if your provider claims to not store your input, there is rarely a corporate grade guarantee for data privacy.
There are also substantial intellectual property risks. Many AI tools can be retained or learned from the data they process. This means that you may be able to share your source code, business strategy, or sensitive workflows.
Compliance is another area of vulnerability. AI tools can process data in ways that violate regulations such as GDPR, HIPAA, and industry-specific standards. The lack of logging and auditing trajectory makes compliance almost impossible.
The quality and integrity of the output from Shadow AI tools is also unreliable. These tools may rely on outdated or biased training data, and may rely on inconsistent, neglected outcomes that can distort decision-making, introduce bias, or damage brand reputation.
Finally, Shadow AI creates gaps in incident response. Its use is usually outside the authorized security system and security system, so activity is often not recorded or monitored. This makes it difficult for security teams to detect, investigate, or respond to data misuse or violations in a timely manner.
What does a safe AI look like?
Secure AI tools are secure by designing core protections such as transport and rest encryption, strict access control, audit records, and core protection that clears data retention policies to prevent unauthorized storage. It also provides features such as prioritizing privacy, guaranteeing data residency, the ability to opt out of model training, editing anonymization or sensitive input, and fine-tuning on sanitized datasets.
Effective AI use complies with clear organizational policies that outline approved tools and platforms, define acceptable use cases, establish data processing requirements, and specify violation escalation procedures. SAFE AI is also integrated into a broader risk management framework. This means that it is factored into threat models, tabletop exercises, third-party risk assessments, and routine audits to ensure continuous monitoring.
Building a shadow AI response strategy
Given the inevitability of Shadow AI, security teams need to take a proactive stance.
1. Discovering Shadow AI Usage: Leverage browser telemetry, Data Loss Prevention (DLP), Cloud Access Security Broker (CASB) tools to detect the use of public AI tools. Conduct employee surveys and interviews to understand where and why AI is being used.
2. Education and Enabling: Training employees on the risks of Shadow AI. We offer safe and approved alternatives. Encourage a culture of responsible experimentation.
3. Embed Governance into Access: Embed AI Usage Policy into onboarding. For sensitive roles, use Just-in-time access or AI usage authorization.
4. Includes Legal and Compliance: Create workflows to quickly evaluate new AI tools. Keep a record system of all approved tools.
5. Update Incident Response Playbook: Add AI-specific incident types (e.g., rapid leakage, model misuse). Train incident response teams to detect, triage, and respond to AI-related incidents.
The future of AI governance
As AI capabilities evolve, frameworks for safe use need to adapt as well. Prepared for the future, security leaders predict AI integration across all departments, establish cross-functional AI risk committees, assert vendor transparency regarding model behavior, and clarify responsibility between users, builders and security teams.
The ultimate goal is not to block AI. It is to enable safe and consistent use and ensure it is consistent with the values and responsibilities of the organization.
Turn the tide
The emergence of Shadow AI is a clear warning. AI is useful, sought after, and shows that it is already deeply embedded in daily work practices. It also indicates that security policies and practices require urgent updates.
By investing in secure AI strategies, security leaders can turn AI from hidden risks into trusted resources. The future of Enterprise AI is not about controls or restrictions. It’s about trust, transparency, and transformation. Security teams must obtain initiatives and lead this change.
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