AI-Orchestrated Attacks: The New Reality for Banks, Fintech, and Crypto

The New Reality for Banks, Fintech, and Crypto

In late 2025, Anthropic published a report that quietly became one of the most important cybersecurity documents of the year. For the first time, a real cyber-espionage campaign was observed where an AI agent carried out up to 80% of the technical steps—reconnaissance, vulnerability discovery, lateral movement, data structuring, and preliminary analysis. 

This wasn’t a lab test or a controlled simulation. 

It was a real operation, executed by a state-sponsored group, where the human operator only provided goals. The AI handled the rest. 

And here is the uncomfortable truth: no matter how many restrictions AI providers impose, this trend will not be stopped. Even if large vendors lock down their frontier models, attackers will simply: 

• use open-source models, 

• deploy local GPU clusters, 

• fork unprotected models, 

• receive resources from states or private sponsors. 

The technology is already too widespread to “put back in the box.” 

This means one thing: within the next 6–18 months, semi-autonomous and fully autonomous AI-powered attacks will become increasingly common—especially in the financial services sector. 

 

What Makes an AI Attack Different

Why Banks, Fintech, and Crypto Are Exposed 

Financial systems are high-value, trusted, data-rich environments. They are also highly interconnected, which means a single weak integration or vendor can cascade into a sector-wide problem. Let’s break down how different parts of the ecosystem are affected. 

1. Banks operate with massive attack surfaces: core banking systems, mobile & online apps, fraud detection engines, card processing flows, legacy IT, partner integrations, cloud workloads, internal automation pipelines. An AI agent can probe this surface far faster than any red-team. 

It can: iterate through recon scenarios automatically, test inconsistencies in business processes, explore internal workflows for bypass logic, attack multiple environments at once. Even if a bank’s security posture is mature, volume alone becomes a challenge. 

2. Fintech & Payment Service Providers (PSPs)

Fintech companies already face high levels of automated abuse.  This isn’t theoretical—there are data points we can use. 

• A global fintech operator reported ~30,000 failed login attempts per day during credential-stuffing waves. (Source: https://www.fintechfutures.com/challenger-banks/case-study-fintech-neobank-slashes-atos-by-75-with-arkose-labs ) 

• Fraud rates in neobanks are roughly double compared to traditional banks. (Source: https://www.unit21.ai/blog/neobank-fraud ) 

• Several analyses show that neobanks face a short “customer lifecycle” (≈340 days vs 500+ in traditional banking), which correlates with higher fraud risk and higher exposure to automated attacks. (Source: https://paymentexpert.com/2025/05/28/neobank-growth-fuels-payment-failures ) 

With AI, this will escalate: API abuse at scale, recursive testing of business logic, automated exploitation of weak AML/KYC controls, generation of realistic synthetic identities, targeted load attacks on fraud systems, and automated account takeover attempts. Fintech moves fast—attackers will move faster. 

3. Crypto Exchanges, Custodians, and Brokers

The crypto ecosystem is especially exposed: transparent infrastructure, predictable financial logic, public on-chain data, broad attack surfaces across custodians, RPC providers, wallets, bridges, L2s. AI can: 

• perform automated smart-contract auditing, 
• detect MEV-sensitive patterns, 
• generate exploit variants, 
• identify weak multisig setups,
• test edge cases in withdrawal logic,
• analyse trader behaviour at scale. 

AI brings the kind of precision and automation that attackers previously lacked. 

4. DeFi and Web3 Platforms

DeFi has an additional problem: everything—code, logic, dependencies—is fully public. 

 An AI agent can: identify vulnerabilities, simulate attacks, validate economic manipulation strategies,  test oracle dependencies, search for mispriced pools, execute “micro-attacks” at scale. This will shift DeFi security from “manual audits + bug bounties” to continuous, AI-driven analysis systems. 

What We Observe Across Banks, Fintech, and Crypto (Q-Sec Threat Intelligence POV) 

Across our financial-sector clients, we’re already seeing the early signs of AI-assisted campaigns: 

• multi-vector probing done in parallel across on-prem and cloud 

• automated business-logic testing targeting payment flows 

• faster lateral movement attempts using automated hypothesis generation 

• significant growth in synthetic-identity testing against onboarding flows 

These patterns don’t resemble traditional human-led intrusion attempts. Their scale and structure match the behaviour of autonomous or semi-autonomous agents. 

Why Traditional SOC Won’t Keep Up 

For years, SOCs were built around the same core assumptions: analysts would triage alerts manually, correlation rules would catch deviations, signatures would flag known malware, and a small team would react to incidents as they surfaced.
That model is no longer sustainable.

Modern attackers are operating at machine speed. Automated reconnaissance, AI-driven vulnerability discovery, and autonomous lateral movement mean that a SOC built on manual processes simply cannot keep pace. Every delay compounds risk.

Even well-resourced financial institutions struggle to maintain a fully staffed 24/7 SOC with enough telemetry coverage and analytical capacity to match AI-enabled attackers. This is why many organisations are shifting toward AI-enhanced managed SOC models (such as Q-SOC): it’s increasingly the only practical way to scale detection and response.

What SOCs Need Now: Scale, Intelligence, Autonomy

The next-generation SOC isn’t defined by a single tool or platform — it’s defined by capability. Three, specifically: the ability to collect more, analyse faster, and automate everything that doesn’t require human judgement.

1. Data-Driven: Telemetry at Real Scale

AI detection only works if the model has something meaningful to work with. Thin telemetry creates blind spots; partial logging makes correlation useless.

A modern SOC needs broad and deep data sources, including:

• enriched endpoint telemetry (EDR/XDR-level detail),
• full API logging for cloud and SaaS,
• identity-centric signals (Entra ID, Okta, IAM),
• east-west network flow and DNS patterns,
• behavioural baselines across users and assets,
• on-chain activity (for crypto environments).

If the pipeline can’t ingest and normalise this volume, the AI layer will miss the subtle patterns that matter.

2. AI-Assisted: Model-Driven Analysis at Scale

This isn’t about adding a chatbot to a SOC dashboard.
It’s about offloading the analytic workload that humans cannot realistically handle.

Model-driven SOC assists with:

• correlating large signal sets,
• analysing anomalies in context,
• ranking and prioritising alerts,
• extracting patterns across endpoints, identities, networks,
• linking events across systems and environments.

The goal is simple: humans investigate attacks, not events. AI handles the noise — the millions of events per day that bury traditional SOC teams.

3. Autonomous-Ready: Automate the Low-Value Work

Autonomy doesn’t eliminate analysts. It eliminates the parts of the job that don’t require them.

AI should handle:

• enrichment and context gathering,
• hypothesis generation (“what else would the attacker touch?”),
• the first steps of investigation,
• stitching related events into a single incident,
• executing safe, low-risk response actions.

Analysts stay in control of decisions, containment steps, and strategic improvements. Machines take the bulk of the mechanical tasks. This is the core principle behind the emerging AI-SOC model.

AI SOC as a Service: Why the Market Is Moving This Way 

Over the last two years, the gap between attacker capability and defender capability has widened — mostly because attackers now automate what defenders still handle manually. Fintech, crypto platforms, and mid-size banks see this gap more clearly than anyone else. They face the same threat landscape as the largest financial institutions, yet very few can realistically operate:

• a true 24/7 SOC with L2/L3 analysts,
• a dedicated engineering team to maintain telemetry pipelines,
• ML engineers to build and tune detection models,
• continuous threat intelligence research across multiple domains.

The problem isn’t strategy. It’s scale. If you can’t afford the infrastructure and people required for modern detection and response, you fall behind — even if your governance is solid. This is why AI-SOC-as-a-Service is becoming the default operating model: it provides the capabilities that smaller teams can’t realistically build on their own. What organisations gain is not “outsourcing,” but access to: centralised, high-volume telemetry ingestion, large shared detection models trained on broader datasets, autonomous investigation pipelines that triage what humans shouldn’t, automated pattern and anomaly recognition across multiple environments, cross-customer intelligence that updates models in near-real time, human review only in the places where judgement actually matters.

None of this is a trend. It’s simply the only sustainable way to defend against AI-enabled attackers when your internal resources are finite.

Regulation Will Tighten — Faster Than Most Teams Expect

Regulators in the EU, UK, US, and APAC are already signalling that AI used in critical financial functions will face stricter governance. We’re heading toward a world where AI is regulated with the same intensity as other systemic infrastructure. Here’s what’s coming — and sooner than most predict.

1. AI Resilience Requirements

Think of this as DORA, but for models. Expect requirements such as: logging of model inputs and outputs, mandatory transparency about model versions, notifications for material model updates, reproducibility of outcomes, independent validation of model safety and robustness.

2. Fallback Models & Multi-Vendor Controls

Single-model dependency will be treated like single-region cloud dependency: unacceptable. Banks will need fallback models, independent validation paths, and multi-vendor strategies.

3. Vendor Audits for AI Providers

The same way cloud suppliers went through deep regulatory audits a decade ago, AI providers will be expected to prove: data isolation, training-data governance, incident reporting, model-lifecycle controls. The audits will be deeper than what we’ve seen with cloud.

4. AI Explainability for High-Risk Decisions

Anything touching systemic risk will fall under explainability rules: credit scoring, AML/CTF detection, onboarding and identity verification, trading controls, payment and transaction monitoring. Black-box reasoning will not be an acceptable excuse.

5. AI Red Teaming Requirements

Regulators will require periodic testing of systems against AI-enabled attackers. This will include simulation of model-driven exploitation, adversarial inputs, and automated lateral movement.

Crypto-Specific Expectations Are Already Emerging

Crypto environments will face an additional layer of AI-focused scrutiny. Likely requirements include: AI-driven smart contract vulnerability analysis, on-chain anomaly detection for wallets, protocols, and bridges, automated liquidation-risk modelling for lending protocols, monitoring around multi-party computation (MPC) key management. The direction of travel is obvious: AI will be treated as critical financial infrastructure — with all the governance, testing, and auditability that implies.

What Financial Companies Should Be Doing Right Now To Prepare

Over the next 12–24 months, AI will change how attacks unfold — and how regulators expect you to defend against them. The organisations that adapt early will stay ahead. The ones that wait for formal regulatory guidance will end up rebuilding their entire detection and governance stack under pressure. Here’s what financial companies should prioritise now, before the gap becomes unmanageable.

1. Expand Telemetry Before You Expand AI

If your SOC has blind spots, your AI detection layer will inherit them. Most teams try to “add AI” to an existing setup, only to realise the models don’t have enough signal to learn from. The fix is simple but non-negotiable: collect more and richer telemetry — endpoints, identity, API logs, network flow, behaviour profiles. Without this, every downstream component remains guesswork.

2. Move Away From Static Rules Toward Behaviour

AI-driven attacks won’t match signature patterns, and rule packs written for yesterday’s TTPs won’t detect automated lateral movement or AI-orchestrated reconnaissance. Behaviour-based detection — combining identity patterns, access anomalies, privilege misuse, sequence deviations — is the only model that survives against adaptive adversaries.

3. Add Independent Validation to Every AI Decision

Never trust an AI decision without a second layer of controls. This applies equally to your own models and to vendor models embedded in your tooling. For high-risk workflows, introduce independent verification steps, reproducibility checks, and deterministic guardrails. A single model failure should never propagate into a production decision.

4. Avoid Single-Model Dependence

Model lock-in is becoming a systemic risk. If your fraud detection, AML, identity verification, or monitoring relies on a single model, you are one vendor outage — or one model drift event — away from operational disruption. Multi-model strategies, fallback paths, and independent scoring layers are going to be mandatory anyway. Start building them early.

5. Apply Layered (“Onion”) Defense to AI Workflows

We’ve spent years building layered defenses for networks and applications. Now we have to do the same for AI-driven workflows: filter and validate prompts, inspect and log outputs, monitor decision chains, restrict automated actions by risk level. Most AI failures start not with malicious inputs, but with unchecked internal automation.

6. Red Team Against AI Agents — Not Just Human Attackers

Testing your environment against human adversaries is no longer enough. You need to understand how AI agents behave when given time, compute, and a target. This will become a formal regulatory requirement, but even before that, it’s simply good security engineering. An AI agent will explore your environment differently — and often more thoroughly — than any human consultant.

7. Do the Work Before Regulators Force You To

It is far easier to build these practices proactively than to retrofit them because an auditor demands evidence. Most organisations start with a structured readiness review to map blind spots in their telemetry, automation, and operational resilience. A targeted assessment — such as a NIS2 Readiness Check or a focused security audit — usually exposes the exact areas where AI-driven threats would cause the most damage.

 

Conclusion: AI Doesn’t Change the Nature of Attacks — It Changes What’s Possible 

AI-enabled attacks aren’t “future challenges.” They’re already here, documented, and evolving fast. Banks, fintech companies, exchanges, custodians, DeFi protocols—all of them operate in environments where automation, data, and digital workflows are core to the business. 

Attackers will use AI to scale, accelerate, and optimize their operations. The only sustainable answer is to respond with the same level of automation and intelligence. 

A modern SOC must be a hybrid: 

AI handles the noise. 

Humans handle the judgment. 

As attacks become automated, defense must become smarter. And the companies that start building AI-enabled security today will be the ones that survive the next wave of threats.