AI in Network Security for Finance

Jack Donabie, Strategic Network Advisor | Estimated Read Time: 11 minutes

Financial institutions face relentless cyber threats, rising regulatory demands, and a shortage of skilled security professionals. AI is changing how the sector approaches network security, offering faster threat detection, smarter fraud prevention, and automated response capabilities that traditional tools simply cannot match. This article explores the practical applications of AI in network security for finance, the benefits it delivers, and the challenges organisations need to navigate.

Why financial services is leading AI adoption in network security

If you work in IT or security within financial services, you already know the pressure - 58% of large UK financial services firms suffered at least one supply chain cyberattack in 2024, with 23% hit three or more times. Cyber attacks are relentless, regulatory expectations keep rising, and your team is expected to do more with the same resources. Traditional security tools that served the industry well for years are struggling to keep pace.

This is why AI-driven network security has moved from emerging technology to strategic priority across banking, insurance, and wealth management. According to IBM's Cost of a Data Breach Report 2025, financial services is the costliest UK sector for breaches, with organisations facing an average of £5.74 million per incident, significantly above the global average.

The appeal of AI is straightforward. Unlike rule-based systems that only catch what they've been programmed to look for, AI can analyse vast datasets in real time, spot patterns that human analysts would miss, and adapt as threats evolve. For financial institutions handling sensitive data and high-value transactions around the clock, that capability gap matters.

The conversation has moved on from whether to adopt AI. The question now is how to do it securely and effectively.

How AI is being applied to network security in financial services

Threat detection and behavioural analysis

Traditional threat detection matches incoming traffic against known attack signatures. The problem is obvious: if an attack is new or sufficiently modified, it gets through.

The scale of this challenge is particularly acute in UK financial services. Ransomware incidents reported to the FCA doubled in the first half of 2023, with ransomware accounting for 31% of all cyber incidents - up from just 11% in the same period the previous year. Traditional signature-based systems struggle to detect these evolving threats before they encrypt critical systems.

AI takes a different approach. Machine learning models establish what normal looks like across your network, from user access patterns and data transfer volumes to application usage and device connections. When something deviates from that baseline, it gets flagged.

In practice, this means catching things that signature-based tools would miss:

• Login attempts from locations that don't match an employee's usual patterns

• Data access volumes that might signal exfiltration

• Lateral movement across network segments suggesting an attacker has gained a foothold

• Subtle traffic changes that often precede ransomware deployment

The NCSC has highlighted that AI-enabled tools enhance the speed and accuracy of threat detection, with analysis suggesting AI can dramatically reduce the time between threat emergence and identification compared to manual processes. That speed difference changes what's possible in terms of response.

Fraud prevention and transaction monitoring

Fraud prevention is one of the most mature AI applications in financial services. Machine learning models analyse transactions across multiple dimensions simultaneously, spotting anomalies that might indicate fraudulent activity.

These systems weigh up factors including:

• Transaction amounts against historical patterns

• Timing and frequency of activity

• Geographic consistency between cardholder and transaction origin

• Device fingerprinting and session behaviour

• Network pathway analysis to identify compromised connections

What makes AI particularly valuable here is its ability to learn. Traditional fraud systems generate endless false positives, frustrating customers and burying fraud teams in unnecessary work. AI models refine themselves through feedback, getting more accurate over time rather than more annoying.

UK Finance's Annual Fraud Report 2025 reported total fraud losses of £1.17 billion in 2024, with unauthorised fraud across payment cards, remote banking, and cheques accounting for £722 million of that figure. Organisations using AI-driven fraud prevention have reported detection improvements of 20-30% while simultaneously cutting false positive rates by similar margins. That combination of better detection and less noise is rare in security tooling.

Automated incident response and orchestration

When an incident happens, speed determines damage. AI enables automated response through Security Orchestration, Automation, and Response (SOAR) platforms that execute predefined playbooks without waiting for human intervention.

Picture a scenario where AI detects potential credential theft. An automated response might:

• Immediately isolate the affected user account

• Block network access from the suspicious source IP

• Trigger multi-factor authentication re-verification

• Alert the security team with contextual information

• Begin forensic log collection for investigation

All of this happens in seconds. Manual triage and response would take hours. For organisations running 24/7 trading platforms or customer services, that time difference can prevent significant financial and reputational damage.

Vulnerability management and predictive security

AI also changes how financial organisations handle vulnerabilities. Rather than treating every CVE with equal urgency, AI models prioritise based on:

• Actual exploitability within your specific environment

• Network topology and exposure levels

• Historical attack patterns targeting similar vulnerabilities

• Business criticality of affected systems

This risk-based approach means security teams focus effort where it will actually make a difference, rather than chasing an endless list of theoretical risks.

Benefits of AI-driven network security for financial institutions

Improved resilience against sophisticated attacks

Financial services faces threat actors that most industries don't: nation-state groups, organised crime operations, and attack methodologies that evolve constantly. AI provides defensive capability that can match this sophistication, learning and adapting alongside the threats - a large factor in why 74% of large UK businesses experienced a cyber breach or attack in 2025.

The ability to detect novel attacks based on behaviour rather than signatures is particularly valuable. Zero-day exploits and custom malware that would sail past traditional defences become visible when they cause unusual network activity.

Faster response times and reduced dwell time

Dwell time, the gap between initial compromise and detection, remains one of the most important security metrics. According to Mandiant's M-Trends 2025, global median dwell time has dropped to around 11 days, though broader industry reports measuring detection and containment across all incident types still show significantly longer averages. AI-driven detection compresses this further, enabling response while attackers are still in reconnaissance rather than after they've taken what they came for.

Regulatory alignment and audit support

Financial services operate under constant regulatory scrutiny, from FCA operational resilience requirements to PRA expectations on cyber risk. AI systems generate comprehensive audit trails that support compliance and evidence-based reporting.

Automated log aggregation, anomaly documentation, and incident timeline reconstruction make it far easier to demonstrate security controls to regulators. For organisations juggling GDPR, PCI DSS, and sector-specific requirements, this automated evidence gathering reduces compliance overhead considerably.

Operational efficiency and resource optimisation

The cybersecurity skills shortage hits financial services hard. AI allows existing teams to work more effectively by automating routine analysis and surfacing genuine threats for human attention. Instead of reviewing thousands of alerts daily, analysts can focus on complex investigations that need human judgement.

The goal is letting skilled analysts do skilled work rather than drowning in noise.

Challenges and considerations for AI adoption in network security

Data quality and model accuracy

AI is only as good as the data it learns from. For financial institutions, this creates real challenges:

• Historical data may contain biases that skew model accuracy

• Network environments change constantly, requiring ongoing retraining

• Insufficient data volume leads to unreliable predictions

• Data silos across business units limit what AI can actually see

Solving these problems requires proper investment in data governance and continuous model validation. You need clear metrics for AI performance and feedback loops that let models improve over time.

Explainability and regulatory scrutiny

Regulators increasingly want to understand how automated decisions get made. Black-box AI that can't explain why it flagged a transaction or blocked a connection creates regulatory risk.

Financial institutions should prioritise AI solutions with explainability features, providing clear reasoning that can be presented to auditors and regulators. This matters most where AI decisions affect customer-facing services or access controls.

Integration with existing infrastructure

Most financial organisations run complex hybrid environments with legacy systems, cloud platforms, and third-party integrations. AI security solutions need to work effectively across all of this.

Key integration considerations include:

• Compatibility with existing SIEM and SOC tooling

• API connectivity to cloud security services

• Support for legacy protocols common in financial systems

• Scalability to handle transaction volumes during peak periods

Adversarial AI and evolving threats

Attackers are adopting AI too. Machine learning helps them craft more convincing phishing content, identify vulnerabilities faster, and evade AI-based defences.

This creates an ongoing arms race. Defensive AI must continuously evolve to counter AI-enhanced attacks. Financial institutions should work with security partners who actively research adversarial techniques and update their solutions accordingly.

Skills and organisational readiness

49% of UK businesses have basic cyber skills gaps, with the average cost of a significant cyberattack reaching £195,000. Deploying AI in network security requires skills that many organisations lack internally. Data science expertise, machine learning operations (MLOps) knowledge, and the ability to tune and validate models are all necessary for effective deployment.

Options include building capabilities in-house, partnering with managed security providers, or adopting platform-based solutions that handle the underlying complexity. The right choice depends on your organisation's size, risk appetite, and existing capabilities.

Practical steps for adopting AI-driven network security

Start with clear use cases

Rather than attempting broad AI deployment, identify specific problems where AI can address known gaps. Common starting points include:

• Enhancing fraud detection for specific transaction types

• Improving threat detection for privileged user accounts

• Automating response to common incident categories

• Prioritising vulnerability remediation efforts

Success with focused deployments builds confidence and generates data to inform wider rollout.

Ensure data foundations are solid

AI effectiveness depends entirely on data quality. Before deploying, assess whether:

• Relevant network telemetry is being captured

• Data retention policies support model training requirements

• Data sources integrate properly for comprehensive analysis

• Governance frameworks ensure appropriate data use

Investment in observability and data infrastructure pays off across every AI security initiative.

Plan for human-AI collaboration

AI augments human expertise rather than replacing it. Design workflows that combine AI speed with human judgement:

• AI handles initial detection and triage

• Human analysts investigate flagged incidents

• Feedback from investigations improves AI accuracy

• Humans make final decisions on significant actions

This collaborative approach gets the best from both.

Choose partners with relevant expertise

Financial services has unique requirements around compliance, data sovereignty, and operational resilience. Work with security partners who understand these demands and can demonstrate relevant experience.

Questions worth asking:

• How do they handle data residency requirements?

• What compliance certifications do they hold?

• Can they provide references from similar financial services organisations?

• How do they approach model explainability and audit support?

What this means for your network security strategy

AI is shifting network security in financial services from reactive to proactive. Organisations that get adoption right gain real advantages in threat detection, incident response, and operational efficiency.

But adoption needs careful thought. Data quality, integration complexity, regulatory requirements, and skills availability all affect outcomes. Rushing deployment without addressing these fundamentals creates new problems rather than solving existing ones.

The organisations seeing the greatest benefit treat AI as part of broader security transformation. They combine AI capabilities with solid network architecture, comprehensive observability, and expert support.

For financial institutions reviewing their security posture, the path forward starts with honest assessment of current capabilities, identification of specific use cases where AI adds genuine value, and partnership with providers who understand what the sector demands.

How Cloud Gateway supports AI-driven network security

Cloud Gateway's SASE-as-a-Service platform provides the foundation for AI-driven network security in financial services. Our unified approach to connectivity, security, and observability delivers the comprehensive telemetry that AI solutions need to work effectively.

Our UK-based infrastructure supports data sovereignty requirements, while our compliance credentials, including ISO 27001, PCI DSS, and Cyber Essentials Plus, align with financial services regulatory expectations. Whether you're implementing AI-enhanced threat detection, automated incident response, or intelligent access controls, our platform provides the secure, observable network environment these capabilities require.

Contact us to discuss how AI-driven network security can strengthen your organisation's defences.