Four AI-Cybersecurity Risk Factors I See in the Field (with real cases)

# Four AI-Cybersecurity Risk Factors I See in the Field (with real cases) AI is now embedded in everyday operations—from copilots in CRMs to autonomous agents in IT and finance. That speed creates an uncomfortable truth: security and governance often lag behind deployment. Below are four risk factors I encounter most in mid-market projects, each paired with a real client case (anonymized) and practical controls. I’ve also included brief insights from respected sources to show how these risks fit the broader landscape. > “Secure by design means building products that reasonably protect against malicious actors—from inception to end-of-life.” # 1) AI-powered impersonation & payments fraud (BEC 2.0) **What the risk looks like** Generative models and voice cloning have industrialized social engineering. Attackers combine OSINT, internal crumbs from shadow AI tools, and real-time synthesis to trigger **high-value payment changes** or urgent “exec” approvals. Research shows deepfake-assisted fraud is already draining corporate accounts, and CISOs are warning that this is no longer a niche risk. **Case (UAE, financial services, mid-market):** A finance team received a “CFO on travel” video message asking to expedite a counterparty change. The model mimicked tone and background almost perfectly; the only giveaway was a subtle timing mismatch between audio and lips. Our playbook—“**dual-channel high-risk verification**” and **payment change cooldowns**—stopped a 7-figure wire. **Controls that worked** * **Process, not tech, first**: dual-channel callbacks for **any** payee change >$X, enforced in ERP workflow. * **Human-in-the-loop** for anomalous finance events (AI flags; humans approve). * **Media provenance checks** (watermarks where available) and simple “liveness” challenges on calls. * **Staff drills** using synthetic audio/video so teams learn the tells. > CSO reports real cases where deepfake CFO voices triggered losses—so treat voice/video as untrusted without a second factor. # 2) Shadow AI & ungoverned data flows **What the risk looks like** Teams adopt AI tools without security review, paste sensitive data into free web UIs, or connect “convenience” plugins that quietly exfiltrate files and logs. [InformationWeek](https://www.informationweek.com/cyber-resilience/what-s-real-about-ai-in-cybersecurity-) notes that AI lowers the barrier to sophisticated intrusion and data targeting; governance must catch up. **Case (Brazil, mining & processing, mid-market):** Engineers used an unmanaged prompt tool to summarize field reports containing **geological IP** and vendor pricing. Months later, a competitor bid synced suspiciously with our client’s internal estimates. We couldn’t prove leakage beyond doubt—but telemetry showed large pastes to an unvetted AI site. **Controls that worked** * **Allow-list** of AI tools with **SSO/SCIM**, **audit logs**, and a documented data **exit plan** (export & deletion on termination). * **Brokered access** (reverse proxy) to redact PII/IP in prompts; block pastes of “crown-jewel” data patterns. * **NIST AI RMF** functions—govern, map, measure, manage—embedded in risk reviews and procurement. * Quarterly “**prompt bill of materials**”: who used what model, where data went, what is retained. > “AI risks require governance with the seriousness of financial controls.” # 3) Over-permissioned AI agents & policy drift **What the risk looks like** Autonomous or semi-autonomous agents get **broad API scopes** “for convenience,” then act on stale context (hallucinations, mis-routing) with real credentials. Traditional perimeter controls don’t map cleanly to agents; **zero-trust** and **just-in-time scopes** are essential. **Case (Qatar, oil & gas services, mid-market):** A “procurement assistant” agent had persistent access to finance and vendor systems. During a noisy incident week, it auto-approved a duplicate invoice chain (no malice—just bad guardrails). Loss avoided, but the near-miss triggered a rebuild: **short-lived workload identities**, **per-tool JIT tokens**, and **session recording** for any high-risk action. **Controls that worked** * **JIT access**: tokens minted **per intent**, expiring minutes later; scopes narrowed to single systems. * **Policy-as-code** for each tool (mail, billing, source control) with **break-glass** flows and alerting. * **Traceability**: log every tool call (who/what/when/inputs/outputs) for incident reproduction. * **Quarterly recertification** of agent permissions; weekly cleanup of “temporary exceptions.” > [CIO](https://www.cio.com/article/4058010/outpacing-risk-how-ai-quantum-and-cloud-are-reshaping-data-security-today.html) warns that AI rollouts often outpace **safeguards—security** and governance must be built in from the start. > > Schneier’s reminder still applies: technology alone won’t solve security problems—**people and process** own the risk. # 4) Model reliability & “secure-by-design” gaps in the AI supply chain **What the risk looks like** Enterprises treat AI like a plug-in feature, not a **software supply chain** with models, data pipelines, vector stores, third-party plugins, and inference gateways. When accuracy, latency, or guardrails fail, the business impact is real: wrong actions, bad advice to customers, compliance drift. **Case (Poland, retail/e-commerce, mid-market):** A product-search assistant relied on a third-party reranker. A silent vendor update shifted results quality and pushed risky SKUs in a regulated category. We moved the client to **contracted quality SLOs** (factuality/precision/recall), added **canary datasets** in CI, and created an **offboarding plan**—export indices, revoke keys, verified deletion—before the next upgrade. **Controls that worked** * Treat AI like **software you ship**: SBOM for models/plugins, change control, rollbacks. * **Secure-by-design** expectations with suppliers; insist on **audit trails**, **API/webhooks**, and sandboxing on your data. * **VLQ** acceptance gates: **Value** (money saved/earned), **Lift** vs. baseline, **Quality** thresholds; if 30-day Lift <10–15%—stop. (This manages cost risk as much as security risk.) * For regulated workloads, use **defense-in-depth** on outputs (filters, policy engines) before content reaches humans or customers. > “AI is a transformative force in cybersecurity—immense opportunities and significant risks.” Use that energy, but pair it with **guardrails** and **SLOs**. # Bonus: operational fatigue as a hidden risk (South Africa, logistics) Even when controls are right, **fatigue** can break them. A logistics operator’s help-desk copilots produced solid drafts, but human reviewers skipped checks during peak season. We added **sampling + incentives**: reviewers get credit only when spot checks confirm quality; auto-blocking kicks in on drift. This lowered rework **and** reduced the risk of sending incorrect customer instructions. # Implementation checklist (what we actually deploy) **Identity & access**: short-lived **workload identities** for agents, **per-intent JIT tokens**, session recording for high-risk actions, quarterly recerts. **Data governance**: allow-listed tools with **SSO/SCIM**, **audit logs**, **export & deletion at termination**; broker prompts through a **redaction proxy**. **Supply chain**: SBOM for models/plugins, **contracted SLOs** (factuality/latency/precision/recall), **canary tests** in CI before promotion. **Process controls**: dual-channel verification for finance changes; cooldowns on high-risk approvals; tabletop exercises on **deepfake/BEC** scenarios. **Frameworks**: adapt **NIST AI RMF** (“govern, map, measure, manage”) to your size; don’t over-engineer—start with a light-weight register of AI uses, data, risks, and owners. > “At the end of the day, there is always a **human responsible** for whatever the AI does.” Build the accountability into roles and contracts, not just policies. # Why this all points to AI-security roles growing, not shrinking Across the examples—from UAE finance to Brazilian mining, from Qatar oil services to Polish retail—security work **expanded**, it didn’t contract. We needed people who could: * map business intents to safe agent policies, * translate compliance into tooling requirements (logs, SSO, export), * design JIT access and traceability, * measure VLQ outcomes so projects don’t become expensive shelfware. CIO and CSO outlets are clear: AI’s upside is real, but rollouts are outpacing safeguards. Organizations need practitioners who can stitch together **governance + engineering + product** for AI. Or as Bruce Schneier likes to say, if you think technology alone can solve your security problems, you don’t understand the problems **or** the technology. AI raises the stakes; it doesn’t remove the need for sound security practice. # About me I'm a CTO at [Pynest](https://pynest.io). His teams build and secure AI-assisted systems for mid-size enterprises across finance, energy, logistics, and retail. If you’re rolling out agents or copilots and want a pragmatic VLQ-driven approach to security, governance, and cost control, reach out. Here are a few of my recent features in major outlets: - Inc.com: https://www.inc.com/john-brandon/how-to-break-up-with-bad-technology/91237809 - InformationWeek: https://www.informationweek.com/it-leadership/it-leadership-takes-on-agi - CIO.com: https://www.cio.com/article/4033751/what-parts-of-erp-will-be-left-after-ai-takes-over.html https://www.cio.com/article/4059042/it-leaders-see-18-reduction-in-it-workforces-within-2-years.html - The Epoch Times: https://www.theepochtimes.com/article/why-more-farmers-are-turning-to-ai-machines-5898960 - CMSWire: https://www.cmswire.com/digital-experience/what-sits-at-the-center-of-the-digital-experience-stack/ # Sources * Secure by Design — CISA: https://www.cisa.gov/securebydesign * Principles and Approaches for Security-by-Design and Security-by-Default (CISA/NSA/FBI et al., PDF): https://www.cisa.gov/sites/default/files/2023-04/principles_approaches_for_security-by-design-default_508_0.pdf * Secure By Design (CISA overview, PDF): https://www.cisa.gov/sites/default/files/2023-10/SecureByDesign_1025_508c.pdf * Artificial Intelligence Risk Management Framework (AI RMF 1.0, NIST, PDF): https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf * NIST AI RMF Overview (web page): https://www.nist.gov/itl/ai-risk-management-framework * Deepfake attacks are inevitable. CISOs can’t prepare soon enough (CSO Online): https://www.csoonline.com/article/3982379/deepfake-attacks-are-inevitable-cisos-cant-prepare-soon-enough.html * AI gives superpowers to BEC attackers (CSO Online): https://www.csoonline.com/article/3995364/ai-superpowers-bec-attacks.html * Deepfakes break through as business threat (CSO Online): https://www.csoonline.com/article/3529639/deepfakes-break-through-as-business-threat.html * Arup lost $25mn in Hong Kong deepfake video conference scam (Financial Times): https://www.ft.com/content/b977e8d4-664c-4ae4-8a8e-eb93bdf785ea * What’s Real About AI in Cybersecurity? (InformationWeek): https://www.informationweek.com/cyber-resilience/what-s-real-about-ai-in-cybersecurity- * Outpacing Risk: How AI, quantum, and cloud are reshaping data security today (CIO): https://www.cio.com/article/4058010/outpacing-risk-how-ai-quantum-and-cloud-are-reshaping-data-security-today.html * AI governance gaps: Why enterprise readiness still lags behind innovation (CIO): https://www.cio.com/article/4028154/ai-governance-gaps-why-enterprise-readiness-still-lags-behind-innovation.html * Effective AI Data Governance: A Strategic Ally for Success (CMSWire): https://www.cmswire.com/customer-experience/effective-ai-data-governance-a-strategic-ally-for-success/ * A Practical Guide to AI Governance and Embedding Ethics in AI Solutions (CMSWire): https://www.cmswire.com/digital-experience/a-practical-guide-to-ai-governance-and-embedding-ethics-in-ai-solutions/ * Going Meta: A Conversation and AMA with Bruce Schneier (schneier.com): https://www.schneier.com/news/archives/2021/07/going-meta-a-conversation-and-ama-with-bruce-schneier.html