Artificial intelligence has changed the speed, scale and complexity of cyber risk. Attackers can use AI to automate reconnaissance, discover exposures, develop convincing social engineering campaigns and accelerate parts of the attack process. At the same time, organisations are deploying generative and agentic AI systems that can access sensitive data, business applications and critical workflows.

Knowing your security maturity level means understanding whether your people, processes and technology can prevent, detect, respond to and recover from these threats. It also means establishing whether your organisation can adopt AI without creating unmanaged risks of its own.

A security programme that appeared mature two years ago may no longer be equipped for what comes next.

What is security maturity?

Security maturity measures how consistently and effectively an organisation manages cybersecurity risk. It looks beyond whether security tools have been purchased and examines whether controls are properly governed, implemented, tested and improved.

A mature organisation does not simply react when something goes wrong. It understands its assets, data, identities, suppliers and exposures. Security responsibilities are clearly defined, controls are regularly tested and decisions are informed by current threat intelligence and business risk.

Security maturity typically covers:

• Governance, leadership and accountability
• Risk management and regulatory alignment
• Asset, application and data visibility
• Identity and access management
• Vulnerability and exposure management
• Security monitoring and threat detection
• Incident response and recovery
• Third-party and supply-chain security
• Employee awareness and security culture
• AI governance and AI system security

The important question is not whether an organisation has security controls. It is whether those controls work together effectively under real-world pressure.

How has AI changed cybersecurity?

AI has changed both sides of the cybersecurity equation. It is giving attackers new ways to operate while offering defenders opportunities to analyse information and respond faster.

The UK’s National Cyber Security Centre warns that AI is already helping attackers conduct vulnerability discovery and reconnaissance at greater speed and scale. Activities that once required considerable time and expertise can increasingly be accelerated or partially automated, reducing the time defenders have to identify and contain an attack. Read the NCSC’s assessment of AI and cyber defence.

AI is not making established attack methods disappear. It is making many of them faster, cheaper and more accessible.

More convincing social engineering

Generative AI can produce credible phishing emails, messages, documents and websites without the spelling mistakes or awkward phrasing that previously exposed many campaigns. Attackers can rapidly tailor content to a particular employee, supplier, executive or organisation.

AI-generated audio and video also make impersonation more convincing. Employees can no longer rely solely on how professional a message looks or whether a caller sounds like someone they know.

Faster discovery of exposures

AI can help attackers process large amounts of publicly available information, analyse technical documentation and identify potential weaknesses across an organisation’s external attack surface.

This makes exposure windows increasingly important. A vulnerability or misconfiguration that remains unnoticed for weeks may now be identified and targeted much sooner.

Increased attack automation

AI can assist with scripting, malware development and parts of the attack chain. Less experienced attackers may use it to complete tasks that previously required specialist knowledge, while established groups can use it to increase the scale of their operations.

Attackers still require a route into the organisation, but AI can help them find and exploit that route more efficiently.

A new AI attack surface

AI is not only changing external threats. It is introducing new internal risks.

Employees may enter sensitive information into public AI tools without approval. Business teams may connect AI applications to corporate data before security teams have reviewed them. AI models can be exposed to prompt injection, data poisoning, insecure integrations and excessive permissions.

Agentic AI increases this risk further. Unlike a conventional chatbot that generates an answer, an AI agent may be able to access applications, retrieve data, create files, send communications or initiate business processes. If that agent is compromised, manipulated or incorrectly configured, the consequences can extend well beyond an inaccurate response.

Why existing security controls may not be enough

Traditional security controls remain essential, but AI is exposing weaknesses in how they are governed and connected.

An organisation may have endpoint protection but lack reliable visibility of unmanaged devices. It may use multi-factor authentication while allowing excessive privileges to accumulate. It may scan for vulnerabilities but struggle to identify which exposures create a viable attack path to critical assets.

AI adoption creates similar gaps. The organisation may have a general acceptable use policy but no specific rules for AI. It may approve an AI platform without understanding where prompts, uploaded documents and generated outputs are stored. It may create AI agents without applying the same identity and access controls expected of human users.

Security maturity in the AI era therefore depends on two related capabilities:

• Defending the organisation against AI-enabled attacks
• Securing the organisation’s own use of AI

Ignoring either side leaves a significant gap.

What are the levels of cybersecurity maturity?

Although different frameworks use different terminology, most organisations can recognise themselves within five broad stages.

Level 1: Reactive

Security activity is largely driven by incidents, audits or urgent compliance deadlines. Asset visibility is incomplete, responsibilities are unclear and different tools operate independently. AI use may already be taking place without central oversight.

At this stage, the organisation does not have a reliable view of its exposure or its ability to respond.

Level 2: Foundational

Basic security controls are in place, including endpoint protection, backups, vulnerability scanning and multi-factor authentication. Policies exist, but implementation may be inconsistent and testing remains limited.

AI may be included in acceptable use policies, but the organisation has not yet established a complete inventory of AI tools, models, agents and integrations.

Level 3: Managed

Security processes are documented, measured and aligned with business risk. The organisation maintains better visibility of assets, identities, data and suppliers. Incident response plans are tested and security monitoring covers critical systems.

AI adoption follows an approval process, with defined ownership, risk assessments and controls around data, access and third-party platforms.

Level 4: Adaptive

Threat intelligence, exposure data, asset criticality and identity risk are combined to guide decisions. The organisation continuously validates its controls and prioritises remediation according to the attack paths that present the greatest business risk.

AI is used carefully within security operations to improve analysis, prioritisation and response, with human oversight maintained for important decisions.

Level 5: AI-Ready and Resilient

Security is embedded into business strategy, technology design and AI adoption. Controls adapt as threats and systems change. AI models and agents are governed throughout their lifecycle, and access is based on least privilege.

The organisation regularly tests whether its people, processes and technology can withstand AI-enabled attacks. Lessons from exercises, incidents and threat intelligence are used to continually improve resilience.

Reaching this level does not mean the organisation is invulnerable. It means it can identify change, respond effectively and recover without having to rebuild its security programme during a crisis.

How can you assess your security maturity?

A cybersecurity maturity assessment should begin with evidence, not assumptions. Policies and architecture diagrams are useful, but they do not prove that controls are implemented correctly or will operate effectively during an attack.

A meaningful assessment should answer several questions.

Do you know what you are protecting?

Your organisation should have an accurate inventory of assets, applications, cloud services, identities, sensitive data and external connections. This should now include AI platforms, embedded AI features, machine identities, models, agents and the systems they can access.

If employees can adopt AI tools faster than the organisation can identify them, shadow AI becomes a visibility and data-security problem.

Do you know where you are exposed?

Traditional vulnerability counts provide only part of the picture. Organisations need to understand which weaknesses are externally exposed, exploitable and connected to critical systems or privileged identities.

Mature security programmes prioritise exposures based on business context and possible attack paths rather than attempting to treat every technical finding equally.

Are identities properly controlled?

AI is increasing the number of non-human identities operating within corporate environments. Service accounts, APIs, automated workflows and AI agents may all require access to sensitive systems.

Every identity should have a clear owner, defined purpose and minimum necessary permissions. Access must be reviewed as systems, roles and AI use cases change.

Can you detect an attack quickly enough?

AI-assisted attacks can progress faster, which makes detection speed increasingly important. Organisations should know whether their monitoring covers endpoints, identities, networks, cloud environments, applications and critical data.

Alerts must also be connected to a response process. Generating more alerts does not improve maturity if the security team cannot investigate and act on them quickly.

Have you tested your response?

An incident response plan should be exercised against realistic scenarios. These might include an AI-generated business email compromise campaign, the theft of privileged credentials, a compromised AI integration or an agent taking unintended actions.

Testing helps identify unclear responsibilities, technical limitations and decision-making delays before they affect a real incident.

Is AI properly governed?

The organisation should know:

• Which AI tools and systems are currently being used
• What data those systems can access
• Where information is processed and retained
• Which individuals or teams own each AI use case
• What permissions have been granted to AI agents
• How outputs and automated actions are validated
• How AI-related incidents will be detected and reported
• Whether suppliers meet security, privacy and compliance requirements

The NIST AI Risk Management Framework provides a useful structure for governing AI risk, but it must be connected to the organisation’s wider cybersecurity programme.

The common signs of low security maturity

An organisation may need to reassess its security maturity if:

• It cannot produce an accurate asset and data inventory
• Vulnerabilities are prioritised solely by severity score
• Security responsibilities are unclear across departments
• AI tools are being adopted without security approval
• Employees use public AI platforms to process business information
• AI agents have broad or permanent access to corporate systems
• Incident response plans have not been tested recently
• Security monitoring does not cover cloud, identity or AI activity
• Third-party access and supplier risk are poorly understood
• Senior leaders receive technical statistics rather than meaningful risk information

These gaps rarely exist in isolation. For example, poor asset visibility weakens exposure management, detection, incident response and regulatory compliance at the same time.

How Integrity360 can help improve your security maturity

Integrity360 helps organisations assess their current security position, identify the most significant gaps and build a practical roadmap for improvement.

Our experts examine security across people, processes and technology, helping you understand not only which controls are present, but whether they are appropriately designed, implemented and tested.

Cyber Risk and Maturity Assessments

Integrity360 can assess your existing security capabilities against recognised frameworks, regulatory requirements and business objectives. The result is a clear view of current maturity, priority gaps and the steps required to reach the desired level.

AI Security and Governance

Our specialists help organisations understand the security implications of AI adoption, establish appropriate governance and protect the data, identities and systems connected to AI tools.

This includes assessing AI use cases, reviewing access and data flows, identifying shadow AI and ensuring innovation does not move faster than the organisation’s ability to manage risk.

Threat Exposure Management

Integrity360 helps organisations move beyond vulnerability counting by identifying and prioritising exposures that could realistically be weaponised. Asset criticality, external exposure, identity risk, attack paths and available threat intelligence can be brought together to guide remediation.

Cybersecurity Testing

Penetration testing, application security testing, configuration reviews and other assurance activities can validate whether controls resist realistic attack techniques. Testing is particularly important as AI accelerates vulnerability discovery and reduces the time between identification and exploitation.

Identity Security

Through our specialist Identity practice, Integrity360 can help organisations strengthen Identity and Access Management, Identity Governance and Administration, Privileged Access Management and Customer Identity and Access Management.

This is increasingly important as AI agents and machine identities expand the number of entities capable of accessing sensitive data and initiating actions.

Managed Detection and Response

Integrity360’s Managed Detection and Response services provide continuous monitoring, expert-led investigation and rapid response. These capabilities help organisations detect suspicious activity across complex environments and respond before an intrusion becomes a major incident.

Incident Response and Preparedness

Our Incident Response specialists help organisations develop, review and exercise response plans. Incident Response Retainers also provide rapid access to experienced support during a crisis, reducing delays when every minute matters.

Integrity360 can help you:

• Assess your current cybersecurity and AI security maturity
• Identify gaps across governance, technology and operations
• Understand how AI has changed your exposure
• Prioritise improvements according to business risk
• Test whether your existing controls work
• Strengthen detection, response and recovery
• Adopt AI securely and responsibly
• Build a measurable, long-term security roadmap

Security Maturity Is Not a One-Off Score

A maturity assessment should not produce a score that is filed away and forgotten. It should establish a baseline, create clear priorities and make progress measurable.

AI has made this continuous approach more important. Threat capabilities are changing, business teams are adopting new platforms and AI agents are creating identities and connections that may not have existed a few months earlier.

The question is no longer simply whether your organisation has good cybersecurity. It is whether your security programme can keep adapting as AI changes the systems you use, the exposures you face and the speed at which attackers operate.

Do you know your current security maturity level? Speak to Integrity360 to assess your security posture, identify priority gaps and build an AI-ready cybersecurity strategy.

Frequently Asked Questions

What is a cybersecurity maturity assessment?

A cybersecurity maturity assessment evaluates how effectively an organisation manages cyber risk across governance, people, processes and technology. It identifies existing capabilities, control gaps and priority improvements.

How has AI changed cybersecurity maturity?

AI has increased the speed and scale of attacks while introducing new risks through AI tools, models and agents. Mature organisations must defend against AI-enabled threats and govern their own use of AI.

What is AI security maturity?

AI security maturity measures how effectively an organisation identifies, governs and protects its AI systems. It includes visibility, data protection, access control, model security, human oversight, monitoring and incident response.

How often should security maturity be assessed?

Organisations should conduct a formal assessment regularly and repeat it following significant changes, such as acquisitions, cloud migrations, major incidents or large-scale AI adoption. Progress against the resulting roadmap should be monitored continuously.

What is the difference between cybersecurity maturity and compliance?

Compliance demonstrates that an organisation meets specific requirements at a particular time. Security maturity considers whether controls are effective, repeatable, integrated and able to adapt to changing threats. An organisation can be compliant without being highly mature.

How can Integrity360 assess our security maturity?

Integrity360 combines risk assessments, governance reviews, cybersecurity testing and technical analysis to evaluate existing capabilities. Our specialists then develop a prioritised roadmap aligned with your risks, regulatory obligations and business objectives.