The Unknowns of AI Adoption: What CISOs Must Confront Before It’s Too Late

AI isn’t just innovation—it’s an uncharted attack surface.

☕ Good morning, Security Gang - coffee cup cheers,

AI exploded into the enterprise before we fully understood its threat model. Boards want acceleration; vendors promise governance and “AI firewalls”; teams are wiring models into real workflows. But here’s the truth from the chair: our visibility into AI risk is incomplete, and the attack surface doesn’t look like yesterday’s IT.

Below is the real threat map, what the market actually offers today, and the CISO playbook to build resilience while we learn in motion.

🚨 The Real Threats of Enterprise AI

1) Prompt Injection & Indirect Prompt Injection

Not just “jailbreaks.” Modern attacks embed malicious instructions in the model’s context—web pages, emails, files, calendars—so the AI “pulls in” the trap on its own. Microsoft calls this indirect prompt injection and treats it with defense-in-depth (prevention, detection, impact mitigation) because it’s inherent to LLMs’ stochastic behavior. (Microsoft)

• Real world: June–Sept 2025 saw multiple disclosures around Copilot context attacks; SANS highlighted Microsoft patching related prompt-injection issues across M365 surfaces. (SANS Institute)

• Research case study: EchoLeak (CVE-2025-32711) described as a “zero-click prompt injection” path in Microsoft 365 Copilot—illustrating how a model can be compromised by simply loading poisoned context. (arXiv)

• Why it matters: Your AI that “reads” customer mailboxes, intranet wikis, or tickets can be turned into a data-exfil bot without malware ever touching an endpoint. Tenable warns these context attacks are a critical AI threat for enterprises adopting assistants across email and files. (Tenable®)

2) Data Poisoning (training & retrieval)

If attackers taint the data your models learn from—or the corpora they retrieve from—your outputs degrade or misbehave silently. NIST’s 2025 adversarial ML taxonomy centers poisoning along with evasion and privacy breaches as primary threat classes to deployed AI. (NIST Technical Series)

• Why it matters: In finance, health, or fraud verdicting, corruption of label sets or knowledge bases means business decisions drift before anyone notices. MITRE’s ATLAS documents real adversary techniques against AI systems and is the best shared map we have. (MITRE ATLAS)

3) Model/Weights & IP Extraction

Your tuned models (or embeddings) are crown jewels. Given enough queries or a leaky MLOps pipeline, adversaries can approximate or steal capability. OWASP’s GenAI Top-10 explicitly elevates LLM01:2025 Prompt Injection and related model-exfil risks for this reason. (OWASP Gen AI Security Project)

4) Shadow AI (unsanctioned tools, risky plugins)

Employees paste sensitive data into public or semi-public AI tools, or connect unvetted plugins that inherit access. OWASP GenAI and Microsoft warn: treat every integration and retrieval source as part of the model’s trust boundary—not an app afterthought. (OWASP Gen AI Security Project)

5) AI-Supercharged Social Engineering & BEC

Generative AI raises the floor for adversaries: polished spear-phish, CEO-stylewriting, and deepfake voice/video. FT reports AI-generated spear-phish increasingly target executives, bypassing filters and lifting breach costs; Wired chronicled the rise of “WormGPT/FraudGPT” crimeware models. (Financial Times)
Industry data shows BEC keeps climbing with GenAI assistance; even mainstream vendor analyses warn of AI-hardened phishing at scale. (Barrcuda Blog)

6) Liability & Governance Blind Spots

When AI misleads customers, you’re still on the hook. Courts held Air Canada liable for its website chatbot’s false guidance—no “the bot said it” defense. That’s a preview of enterprise AI accountability. (The Guardian)

🛠 What Exists in the Market (signal vs. noise)

Useful today (when configured well):

• GenAI “firewalls” & model monitors: detect anomalous inputs/outputs, prompt-injection patterns, data-exfil attempts (probabilistic + deterministic controls). Microsoft publicly outlines this layered approach for indirect injection. (Microsoft)

• Input/Output policy engines: PII/secret scrubbing, guardrails, content policies applied pre- and post-inference (the “DLP for AI” crowd).

• Supply-chain & MLOps security: provenance, SBOM for models/datasets, pipeline signing, repo/feature-store hardening—mapped to MITRE ATLAS tactics. (MITRE ATLAS)

• Governance frameworks: NIST AI RMF concepts operationalized in tools; OWASP GenAI Top-10 gives concrete risks/mitigations for LLM apps. (NIST Technical Series)

Gaps to watch:

• Many tools fix point risks (prompt filtering) yet miss systemic risks (poisoned retrieval sources, over-trusted connectors, weak identity around model access).

• Few products natively combine identity, egress, and retrieval hygiene—you’ll likely stitch controls across IAM, data security, and MLOps.

📉 Risks CISOs Should Keep Front-of-Mind

1. Context Integrity: Any source the model “reads” (mail, tickets, web, SharePoint) can be a prompt-injection vector. Treat those sources like code inputs, not content. (Microsoft)

2. Retrieval/Training Supply Chain: Require provenance for datasets and retrieval indexes; monitor for poison patterns. NIST/ATLAS classify poisoning as a primary operational threat. (NIST Technical Series)

3. Identity & Egress for AI: Who/what can call the model with which scopes? Where can outputs go? Tokens and connectors are the perimeter. (See how token abuse reshaped SaaS risk in 2025—same pattern applies to AI connectors.)

4. Human-in-the-Loop Failsafes: High-impact decisions (payments, account changes, code deploys) need HITL and rollback paths while the threat model matures.

5. Regulatory & Contractual Exposure: If the bot says it, you said it (see Air Canada). Bake auditability and explainability into vendor terms. (American Bar Association)

🛡 The CISO Playbook for AI Resilience

1) Inventory & Classify AI Usage (including Shadow AI)
Run an enterprise-wide discovery of models, plugins, connectors, datasets, and retrieval sources. Tag by data sensitivity and decision criticality.

2) Secure the AI Supply Chain

• Demand dataset/model provenance, checksums/signing, and an MLOps SBOM (models, training code, data sources, feature stores).

• Map threats and controls to MITRE ATLAS and NIST AML taxonomy; make this your shared language with vendors. (MITRE ATLAS)

3) Harden Identity & Egress Around Models

• Phishing-resistant MFA, least-privilege scopes, short-lived tokens for every AI connector.

• Egress controls & quotas on model outputs and retrieval exports; alert on unusual exfil patterns (yes, for AI too).

4) Defend Against Prompt/Indirect Injection

• Deterministic guards: allowlists/denylists for tool use, sensitive actions, and external sources; strip or sandbox untrusted markup.

• Probabilistic guards: anomaly detectors on inputs/outputs; content and secret classifiers.

• Source hygiene: treat emails, tickets, wikis, and web as untrusted code—sanitize or segment before the model ingests. (Microsoft’s blueprint is a useful reference.) (Microsoft)

5) Red-Team the Whole Flow

• Attack prompts, retrieval sources, tools, and decision points (payment flows, code change requests).

• Include zero-click context attacks like EchoLeak in scenarios. (arXiv)

6) Governance, Auditability, and Liability

• Adopt OWASP GenAI Top-10 controls for LLM apps; capture prompts/decisions for audit with strict retention. (OWASP Gen AI Security Project)

• Update contracts: vendor must log/retain AI decisions, report context poisoning, and accept shared liability for AI-driven harm. (Case law trend: bots don’t absolve you.) (American Bar Association)

☕ James Azar’s CISO Take

AI is this decade’s platform shift—and its attack surface is probabilistic. That means our controls must assume things will get weird: models will follow the wrong “voice,” retrieval will get polluted, and connectors will overshare. The job isn’t to halt AI; it’s to pace it—to wrap identity, egress, and provenance around it until the risk settles into patterns we can measure.

If you only buy a prompt filter, you’ll miss the breach happening in your context sources. If you only watch outputs, you’ll miss data poisoning upstream. And if you ignore liability, a regulator or court will remind you that the AI speaks for the company.

Engineer for blast-radius reduction now: tight scopes, clean inputs, observed outputs, and contracts with teeth. Then iterate. That’s how we adopt AI—and keep our resilience.

📌 Monday Checklist

• Run an AI usage inventory (models, connectors, datasets, plugins); flag shadow AI.

• Apply phishing-resistant MFA + least privilege to all AI connectors; shorten token lifetimes.

• Stand up context hygiene: sanitize/sandbox emails, tickets, wikis before retrieval.

• Enable egress quotas/alerts on AI outputs and retrieval exports.

• Schedule an AI red-team focused on indirect prompt injection and poisoned retrieval.

• Update vendor contracts for auditability and shared liability of AI behavior.

📚 Sources to Share with the Board & Builders

• Microsoft MSRC on indirect prompt injection defenses (prevention/detection/mitigation). (Microsoft)

• NIST Adversarial ML Taxonomy (data poisoning, evasion, privacy). (NIST Technical Series)

• MITRE ATLAS: real-world AI attack techniques & mitigations. (MITRE ATLAS)

• OWASP GenAI / LLM Top-10 (2025): current risks & mitigations. (OWASP Gen AI Security Project)

• SANS brief noting Copilot prompt-injection patching activity (June 2025). (SANS Institute)

• EchoLeak (2025): zero-click prompt injection case study. (arXiv)

• FT on AI-generated spear-phish targeting execs; Wired on criminal LLMs (WormGPT/FraudGPT). (Financial Times)

• Air Canada chatbot liability rulings (Guardian/Forbes/ABA). (The Guardian)