AI cybersecurity: GenAI attacks and blockchain defense shift

This post is a guest contribution by George Siosi Samuels, managing director at Faiā. See how Faiā is committed to staying at the forefront of technological advancements here.

TL;DR: Production AI agents now execute actions across enterprise systems using natural language. This creates attack vectors traditional security wasn’t designed for—prompt injection, jailbreaks, and reasoning chains that bypass perimeter controls. The solution pairs AI’s adaptive detection with blockchain’s immutable proof: ledger-anchored audit trails, attested agent identities, and verifiable execution that travels across systems.

• LLMs face silent exploits
• Proof-driven AI
• AI firewalls and on-chain trace
• Selective on-chain security
• Conclusion

Recognize the new AI attack surface

Production of large language models (LLMs) and agent frameworks moved from pilots to real workflows in the last 12–18 months. That created a class of threats that traditional controls weren’t designed for.

Prompt injection now reads like the new social engineering. Malicious inputs can override model or agent instructions. They quietly chain actions across connected tools. In one real demonstration I covered, a booby‑trapped calendar invite embedded instructions. It led a ChatGPT‑linked agent to sift private mailboxes. The agent attempted exfiltration. No malware required. Just words interpreted as executable code.

Enterprise security leaders are noticing. Recent guidance for securing the artificial intelligence (AI)‑powered enterprise highlights three persistent themes. Data leakage from oversharing. Emerging threats like prompt injection and jailbreaks. Compliance pressure as agentic AI takes actions across systems. Surveys cited in that guidance report stark numbers. 80% of leaders list data leakage as a top concern. 88% worry about the manipulation of AI systems.

Operationally, the blast radius grows with “over‑permissioned” agents and multi‑connector platforms. The weakness is the lack of inspection for malicious reasoning chains. Untrusted content flows into AI tools with no scrutiny. Academic and practitioner literature in late 2025 underscores rising exploit frequency. Filter‑based defenses struggle, especially for plugins and third‑party chat layers.

Why blockchain belongs in the conversation—pragmatically

These are the properties we actually need in production now: tamper‑evident logs, portable attestations, and verifiable execution. AI is probabilistic and adaptive. You compensate with evidence that can travel across systems.

A pragmatic pattern set is emerging.

First, ledger‑anchored audit trails. Record prompts, tool calls, model versions, policy IDs, and hashes as immutable events. In incident reviews, signed lineage shortens mean‑time‑to‑explain. It eliminates “can’t reproduce” gaps. Microsoft’s (NASDAQ: MSFT) enterprise guidance emphasizes extending detection and response to AI inputs and outputs. Anchoring evidence for accountability aligns with ledger‑backed provenance.

In conversations with enterprise clients at Faiā, the question I hear most is about replay capability. A healthcare client piloted ledger-anchored prompts. When their AI misclassified a patient note, the signed trail let them replay the exact model version, input, and policy ruleset in under 10 minutes. Their SIEM couldn’t do that.

Independent industry analyses suggest that proactive AI‑security controls reduce incident response costs by 60–70% versus reactive approaches. Input validation, output filtering, privilege minimization, and real‑time monitoring all contribute. Pairing AI detection with verifiable evidence strengthens the case.

AI gives you adaptive detection. Blockchain gives you durable proof. Pair them.

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A tighter, narrative playbook (fewer bullets, more receipts)

Start with connector hygiene. Map where agents can act. Reduce scopes. Remove unused tools.

Insert an AI firewall or prompt proxy. Normalize and sanitize inputs. Constrain tool calls. Log every decision point.

Then anchor one sensitive workflow to an immutable log. Incident response. Regulated code changes. High‑stakes customer communications. Include hashes and version IDs. The point isn’t ideology. It’s replayability. When incidents occur, a signed lineage enables you to answer critical questions—what the agent saw, which rules fired, which version ran, and who approved the write.

Leaders who pilot this stack report different post‑mortems. Less finger‑pointing. Faster mean‑time‑to‑explain. Fewer governance gaps between teams. External surveys and papers in 2025 document a measurable rise in prompt‑injection attempts. This reinforces the need for provenance and cross‑system integrity rather than filter‑only strategies.

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What to watch next

Two frictions are real: throughput and privacy.

Logging everything can add latency under load. Sensitive prompts may contain regulated data. Teams are responding with selective disclosure. Hashing plus off‑chain storage. Layer‑2 patterns to keep performance in bounds. Non‑repudiation still delivers when it matters.

The direction is clear. Pair fast adaptation with stable accountability. The internet scaled on that trade‑off. AI security will, too.

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Key insight

Trust became programmable the moment AI needed to explain itself. Enterprises that pair adaptive models with immutable logs won’t just defend better. They’ll audit faster. Govern tighter. Ship with receipts.

In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI.

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Watch: Demonstrating the potential of blockchain’s fusion with AI