Topic starter
25/10/2026 11:00 am
TL;DR: AI is making phishing, deepfakes, adaptive malware and third-party exposure faster, more personalised and harder to detect, according to Panorays. Trust checks, delegation limits and third-party oversight need to move faster than AI-enabled attack paths.
Editorial analysis by NHI Mgmt Group, based on content published by Panorays: “Navigating AI Cybersecurity Threats: How to Protect Your Ecosystem”.
Key questions
Q: When do deepfakes become a business risk rather than a novelty?
A: They become a business risk when a voice, image or video can trigger money movement, access approval or disclosure of sensitive information.
Q: What breaks when vendor AI is added without lifecycle governance?
A: What breaks is ownership.
Q: How should security teams reduce password risk when AI can scale phishing and impersonation?
A: Security teams should focus on removing reusable credentials from the identity path, not just adding stronger verification on top of them.
Practitioner guidance
- Map AI-enabled third-party access paths Inventory vendors, agents and embedded AI features that can read, summarise, route or store your data, then classify each as a governed access path with an owner and revocation point.
- Replace callback dependence for high-risk approvals Use multi-person approval, identity-based verification and out-of-band challenge steps for payments, vendor changes and privileged requests that an attacker could impersonate.
- Scope tokens and egress for vendor integrations Limit each integration to the minimum data and action scope, then constrain outbound connections so AI features cannot freely exfiltrate prompts, logs or credentials.
Bottom line: AI makes social engineering faster, more personalised and harder to spot, which shifts the problem from message quality to trust validation.
Glossary terms: AI-generated phishing, Deepfake-Enabled Fraud, Shadow AI
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This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:15 pm
AI has turned trust abuse into an industrial process: the core change is not that phishing exists, but that it can now be generated, localised and iterated faster than most organisations can review it. That compresses the window between reconnaissance and exploitation, which makes preventive controls less valuable if they are not paired with rapid verification and response. Practitioners should treat message authenticity as a dynamic control problem, not a content problem.
A question worth separating out:
A: Zero Trust assumes the AI environment cannot be trusted by default, so every access request must be verified and constrained. Traditional implicit-trust models assume internal components are safe once inside the network boundary. For AI deployments, Zero Trust is better aligned with sandboxing, monitoring, encryption, and least-privilege access because it reduces lateral movement and limits uncontrolled interaction.
👉 Read our full editorial: AI-driven phishing and vendor exposure are widening attack surface
This post was modified 7 days ago by NHI Mgmt Group