AI Threat Map v2.1 Navigating AI Threats & Tradeoffs for AI Adoption

Here’s a conversation I often have with executives and privacy leaders outside the AI bubble:

"We’re still deciding whether we want to use AI.”

What they really mean is that they’re paralyzed about their GenAI strategy without realizing AI is already in their digital ecosystem. Whether it’s CRM recommendations, productivity tools, or third-party SaaS platforms, AI is already operating inside their environment.

The brutal truth is it’s no longer a decision, it's survival. Organizations without GenAI aren’t just behind, they’re at a strategic disadvantage against both adversaries and competitors. It's like showing up to a gunfight with a butter knife you're not just unprepared, you're dangerously outmatched.

The Upside: AI Is Driving Real Cybersecurity Maturity

The silver lining in this digital disruption is GenAI has become the catalyst forcing organizations to finally adopt the mature cybersecurity practices that IT and security leaders have been advocating for years.

Your AI Strategy Can't Wait Another Day

Three critical pillars:

🔹 Asset Management Know your digital terrain intimately. That means a current Software Bill of Materials (SBOM), full visibility into your AI and non-AI systems, and understanding your third- and fourth-party dependencies. (Remember the Log4j chaos?)

🔹 Identity Management User accounts, service accounts, privileged roles, non-human identities (NHIs), every identity is a potential attack vector. Secure and monitor all of them.

🔹 Logging & Monitoring Modern attacks move fast, especially with AI in the hands of adversaries. Without real-time logging and threat detection, you’re flying blind in a snowstorm. Follow Thomas Roccia and his Nova Project for more insight on logging and monitoring GenAI.

Do This First

• Update Your Incident Response Plan:  AI-powered threats are real, fast, and adaptive. Make sure your response plan accounts for AI-driven attacks and failure scenarios. 
• Revise Your Third-Party Risk Assessments Every new vendor or contract renewal should include updated questions about AI use, training data exposure, model risks, and how AI is being integrated into their products or services.

Reactive to Resilient

Education and Experimentation GenAI is not just another IT tool, it's a new paradigm. There’s a learning curve. Embrace it. Start by reading Co-Intelligence by Ethan Mollick for a foundational understanding of how humans and AI can collaborate effectively.Excellent First GenAI Book Ethan Mollick Co-Intelligence

Create Safe Experimentation Zones Identify low-risk, low-investment areas where you can experiment without threatening critical business operations. These testing grounds let your teams learn, fail fast, and iterate with minimal risk.

Conduct AI Adversarial Red Teaming GenAI introduces entirely new attack surfaces, prompt injection, rag poisoning, web injection, and  model manipulation. Traditional security testing won’t catch these. You need specialized AI red teaming to uncover where you’re exposed. Excellent book by Philip A. Dursey Red Teaming AI

Use the AI Threat Map v2.1 The AI Threat Map v2.1 offers a structured way to identify, prioritize, and address threats  systematically.

The AI Threat Map v 2.1

The updated version has eight categories of threats. Understanding these categories empowers organizations to make informed, balanced decisions about building, buying, or securing AI systems.

1. Threat from AI Models: AI models can cause harm by adversarial use, or bad output. 

• Example: Use of AI by an adversary, language models generating harmful advice, hallucinating facts, or producing toxic outputs.
• Real-World Risk: Deepfakes, automated fraud tools (e.g., WormGPT), hallucination squatting, misinformation campaigns, private data used in training.

2. Threat Using AI Models: Due to security vulnerabilities in the supply chain, weakness in capability of the AI System. A new and alarming threat is scheming, reward hacking, and direct adversarial attacks enhance traditional attacks with novel methods.

• Example: Prompt injection used to hijack chatbots or autonomous agents.
• Real-World Risk: Supply chain corruption, indirect injection, improper output handling.

3. Threat to AI Models compromise of the AI systems 

• Example: Poisoned training data or stolen fine-tuned weights.
• Real-World Risk: Model inversion, prompt leakage, unbounded consumption.

4. AI Legal & Regulatory Threat AI use could trigger complex and evolving legal obligations:

• Example: Inaccurate resume screening violating hiring laws.
• Real-World Risk: Non-compliance with the EU AI Act, U.S. state biometric privacy laws, or trademark violations.

5. Threats NOT Using AI Avoiding AI can have a negative organizational impact

• Example: Defending against adversarial AI with legacy tools.
• Real-World Risk: Operational inefficiencies, loss of market competitiveness, increased human error.

6. Threat of AI Dependency Overreliance on AI can reduce human capability or have operational impact if the AI System is not available.

• Example: Depending on AI to detect fraud, while attackers adapt faster than models.
• Real-World Risk: Power centralization, behavioral dependency, black-box opacity.

7. Threat Not Understanding AI Models Lack of AI System knowledge may cause poor deployments, lead to misuse,  and exposure.

• Example: Assuming AI decisions are deterministic or interpretable when they are not.
• Real-World Risk: Misaligned expectations, forcing GenAI into outdated workflows, technical debt.

8. AI Investment Threats (New in v2.1) Organizations face financial limits in deploying and securing AI:

• Example: Underfunding threat assessments, vendor lock in, surprise rate hikes skipping red teaming, or failing to account for retraining costs.
• Real-World Risk: Security gaps, reputational damage, and regulatory penalties.

The recent Cursor price debaucle is a recent example of AI Investment Threats and how surprise pricing changes can erode trust. On June 16, 2025, the company abruptly redefined what ‘unlimited use’ meant under its $20/month Pro plan catching many users off guard. The result? Significant sticker shock and widespread frustration over unexpected charges.

Looking Ahead: The Future of AI Security

GenAI is both curse and cure. The AI landscape is evolving at breakneck speed. New capabilities as well as new threats emerge daily, and defensive strategies that work today might be obsolete tomorrow. The only feasible way to defend AI is with AI. adopting a resilient threat informed strategy, and monitoring the organizations for anything that moves them into a red zone.

The key is building adaptive security that can evolve with the threat landscape.

#AIsecurity #GenAI #EnterpriseRisk #CISO #AIgovernance #Cybersecurity #ResponsibleAI #RiskManagement #SplxAI #Leadership #Innovation

Sander Schulhoff Harriet Farlow Krishna Sankar Philip A. Dursey Kevin Rank, MBA Haiyang L. Barry Hurd Trey Blalock Fabrizio Cilli Josh Jeffrey Mike Shema Dutch Schwartz Sabrina Caplis Rock Lambros