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TRUVACE RECORD VERSION
record: TRV-2026-0124
version: 3
kind: sources_changed
reason: Source set updated
timestamp: 2026-09-19T06:54:33.624797Z
status: published
lens: p_space
sector: labor
headline: Cybersecurity and Artificial Intelligence in the Asia Pacific: Threats Defenses and Governance Recommendations
dek: This study examines interactions between artificial intelligence and cybersecurity across the Asia-Pacific, with emphasis on ASEAN governance, technical attack vectors and defensive trajectories. It synthesizes classifications of offensive and adversarial AI, detailing techniques such as data poisoning, model extraction, evasion attacks, adversarial perturbations and AI-driven social engineering, and highlights how agentic AI architectures expand attack surfaces via persistent memory, tool integrations and dynam…
gain_title: (none)
problem_title: AI-enabled offensive techniques increase cybersecurity risk in Asia-Pacific by using data poisoning, model extraction and AI-driven social engineering, with agentic AI expanding attack surfaces
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: AI-enabled offensive techniques increase cybersecurity risk in Asia-Pacific by using data poisoning, model extraction and AI-driven social engineering, with agentic AI expanding attack surfaces
problem_evidence: data poisoning, model extraction, evasion attacks, adversarial perturbations and AI-driven social engineering | agentic AI architectures expand attack surfaces via persistent memory, tool integrations and dynamic privilege use
quick_read: By July 2026 this peer-reviewed synthesis examined how artificial intelligence reshapes cybersecurity in the Asia-Pacific, focusing on ASEAN. It catalogued offensive methods such as data poisoning and model extraction and defensive uses of machine learning and deep learning for anomaly detection and incident prioritization, alongside technical controls like sandboxing and identity controls.

Dual-use dynamics matter because the same AI capabilities that improve detection also create new attack surfaces through agentic memory and tool use, while uneven adoption of standards across ASEAN creates governance gaps. Uncertainty remains about effectiveness across varied national capacities and about durability of defenses given vulnerability to adversarial manipulation.
limitation: Defensive AI mechanisms are themselves vulnerable to adversarial manipulation and regional governance shows capacity gradients that limit uniform adoption
tag: Evidence-backed problem
key_points: Study classifies offensive AI techniques including data poisoning, model extraction, evasion attacks and AI-driven social engineering | Agentic AI architectures expand attack surfaces via persistent memory, tool integrations and dynamic privilege use | Defensive AI evaluated for anomaly detection and incident prioritization but noted as vulnerable to adversarial manipulation | Regional survey finds heterogeneous adoption of NIST CSF, ISO 27001 and COBIT across ASEAN with capacity gradients
rundown: Article synthesizes technical attack vectors including data poisoning, model extraction, evasion attacks and adversarial perturbations, and notes agentic AI risks from persistent memory and tool integrations. Mitigations proposed include adversarial training, sandboxing of agent tools, strict identity and access controls, memory integrity checks and continuous model auditing.

Governance analysis covers ASEAN adoption of NIST CSF, ISO 27001 and COBIT, proposes ASEAN-specific framework profiles, a regional certification entity and cross-border guides aligned with the ASEAN Digital Masterplan 2025, plus non-binding guidance via an ASEAN AI working group and coordinated CERT collaboration with emphasis on human oversight and explainability.
sources:
- peer_reviewed | Discover Applied Sciences | https://doi.org/10.1007/s42452-025-06773-0 | 2025-06-01
- peer_reviewed | INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH | https://doi.org/10.56975/ijedr.v14i3.308737 | 2026-07-01
- peer_reviewed | Mobile Information Systems | https://doi.org/10.1155/2020/6535834 | 2020-04-07
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