AI-enabled threats: a briefing for the Board

The spread of generative models has not simply multiplied the tools available to defenders; it has commoditised offensive capability once reserved for specialists. The impact is visible on three fronts: (i) a lower barrier to entry for malware and social engineering; (ii) new vectors aimed directly at AI applications—prompt injection, jailbreaks, and data exfiltration via connected tools; and (iii) a shift in risk from infrastructure alone to the automated decision flow itself. Leading authorities and technical communities have already codified taxonomies and guidance; the task now is to operationalise them without resorting to ad-hoc fixes.

I. The new asymmetry: offensive power at marginal cost

Organised crime and opportunistic actors are using AI to scale, accelerate and increase plausibility. Multilingual, highly credible messages, voice cloning and deepfakes refine social engineering, while code assistants produce polymorphic variants that degrade signature-based controls. Recent European analyses of organised crime in the AI era corroborate this picture.

II. The attack surface of AI applications

LLM-based applications expose logic-level vulnerabilities that do not map neatly to traditional defects. Community guidance (e.g., OWASP for LLMs) highlights prompt injection and jailbreaks as top risks: malicious instructions embedded in user inputs or documents that induce models to ignore policy, reveal secrets or call tools in unsafe ways. Frameworks such as MITRE ATLAS extend the view across the lifecycle: data poisoning, model extraction and inference-time manipulation.

III. Managerial implications: from the SOC to the P&L

The point of impact is not purely technical. When an AI agent influences pricing, credit, supply chain or customer operations, the combination of plausibility (more convincing messages) and speed (end-to-end automation) converts risk into expected loss: more successful fraud, more rework, more downtime. For Boards, this demands a shift in lens: measure AI as a service with commitments (quality, latency, escalation rate), not as a permanent lab experiment.

IV. Assurance principles (codified, not improvised)

Joint guidance from national cyber authorities and emerging profiles for generative AI converge on design rules: security by design; clear trust boundaries for inputs; mediation of tool access with least privilege, rate limiting and data masking; signed traceability of data and decisions; and routine adversarial red teaming. These are not abstract “good practices”: they define verifiable, auditable requirements relevant to due diligence, insurance and compliance.

V. Defending against AI-enabled threats: the AI control plane

A credible operating model clearly separates model and governance. Between enterprise systems and LLMs sits a control plane that: applies executable policies before and after inference; brokers use of APIs/DB/RPA via whitelists and scoped permissions; observes the decision flow with signed logs and replay/shadow mode; enables adaptive human-in-the-loop for high-impact or low-confidence cases; and exposes kill-switch and rollback at the use-case level. The benefits are twofold: net risk reduction and portability over time (models can change without re-authoring governance). Threats and countermeasures map cleanly to recognised taxonomies (e.g., ATLAS, OWASP).

VI. What the organisation should evidence now

A mature Board can reasonably require three artefacts, all verifiable: (1) a map of surfaces where AI touches money and data (untrusted inputs, invoked tools, exposed secrets); (2) specific SLOs for AI services in production (quality, latency, escalation, out-of-policy coverage); (3) a recurring test plan including adversarial red teaming and data-lineage audits. Together, these assets—map, SLOs, plan—turn AI from diffuse risk into a governed capability.

Conclusion

AI-enabled threats are not a passing anomaly; they are the competitive baseline for the coming years. The leadership task is not to “pick the best model”, but to institute a robust assurance regime that makes AI use reliable, inspectable and insurable. Uniquon supports the transition from theory to operating standard: a model-independent control plane, metrics tied to P&L, and compliance aligned to international references. This is how AI stops amplifying risk and starts shaping durable advantage—safely.