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How AI is changing the way attacks work
AI is best understood as an accelerator for parts of an intrusion, not as a wholly new category of attacker. The UK NCSC’s assessment, “Impact of AI on cyber threat from now to 2027,” says threat actors are already using AI to improve victim reconnaissance, vulnerability research and exploit development, social engineering, basic malware generation, and the processing of stolen data. Its main near-term expectation is more frequent and impactful intrusions as existing tactics improve.
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The technology can help with tasks such as finding useful information about a target, adapting a message to a recipient, or handling information gathered during an intrusion. That can improve speed, scale, customization, or efficiency. The underlying weaknesses may still be familiar: an unpatched server, exposed remote service, compromised account, or successful deception.
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Microsoft’s Digital Defense Report 2025 describes AI-automated phishing and multi-stage attack chains, while also noting that many attacks continue to exploit known gaps in web assets and remote services. This is Microsoft’s account based on its own telemetry and response work, not a universal measure of attack activity.
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Two connected attack surfaces: conventional systems and AI deployments
There are two related but distinct risks: attackers can use AI against ordinary enterprise systems, and they can attack AI systems or misuse their connections to company systems. As organizations connect AI tools to internal data, applications, and operational technology, those connections can create new paths to sensitive information or actions.
| What is targeted | Examples of attack activity | Why it matters to defenders |
|---|---|---|
| Traditional enterprise systems | AI-assisted reconnaissance, social engineering, vulnerability research, exploit development, and basic malware generation. | Keep ordinary controls—patching, identity protection, monitoring, and incident response—effective; AI can make existing tactics more efficient. |
| AI models, data, integrations, and dependencies | Direct or indirect prompt injection, software vulnerabilities, data poisoning, insecure data handling, and supply-chain compromise. | Assess what the AI system can access or change, what data it processes, and which external services and components it relies on. |
The UK NCSC identifies prompt injection, software flaws, and supply-chain attacks as ways to exploit AI systems or use them as a route into wider systems. Its leadership guidance also flags data poisoning and risks that arise when AI tools pass information to third-party applications. These concerns overlap with AI-assisted attacks on conventional systems, but they are not the same claim: an attacker can use AI to target a company without attacking its AI deployment.
How much of an attack can AI automate?
Automation is growing at the level of individual tasks, but the available assessments do not support describing advanced cyber attacks as generally autonomous from start to finish. The UK NCSC expects skilled actors to automate more elements—such as finding and exploiting vulnerabilities, modifying malware, and adjusting supporting infrastructure to evade detection—while remaining involved in advanced operations. It assesses fully automated end-to-end advanced attacks as unlikely through 2027.
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The NCSC also says the most capable actors are currently best placed to use AI’s full potential, while commercially available and open-source tools can give other groups a lift. This is a forward-looking threat assessment, not a census of all attackers or proof that every organization is facing AI-enabled activity. The NCSC warns that a lag in defensive measures could leave critical systems more vulnerable by 2027; that is a conditional risk, not a prediction that compromise is inevitable.
Specific vendor observations illustrate possible developments without establishing their prevalence. On 11 May 2026, Google Threat Intelligence Group (GTIG) reported a criminal actor using a zero-day exploit that GTIG believed had been developed with AI. GTIG also described AI-accelerated malware and infrastructure development, malware that can use a model to interpret system state and generate commands, and attacks against AI environments and software dependencies. The exploit’s AI authorship is GTIG’s belief, not a confirmed fact beyond doubt.
What the numbers do—and do not—show
Published figures have different geographies, datasets, and definitions. They should not be combined into a global estimate of AI-driven attacks or treated as evidence that AI caused the activity measured.
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| Reported figure | Scope and attribution | What it does not establish |
|---|---|---|
| 18% increase | New Zealand’s NCSC reported an increase in criminal or financially motivated incidents classified as potentially of national significance in New Zealand last year, in its 2026 Cyber Threat Report. | This is not an AI-attributed increase and does not describe incidents worldwide. |
| 78% | Microsoft’s 2026 Digital Defense Report says 78% of observed attack techniques against critical infrastructure used cloud identity abuse. | This is Microsoft’s observation for that scope; it does not show that AI caused those techniques or represent all attacks against critical infrastructure. |
The UK NCSC assessment and GTIG’s report provide judgments and examples rather than a comparable global count of AI-attributed attacks. The sources cited here do not establish a harmonized global prevalence figure or a causal estimate of AI’s effect on successful intrusions.
Defenses to prioritize
AI changes the speed and shape of some threats, but it does not make foundational security less important. The practical response is to reduce opportunities for intrusion, limit the damage a compromised account or tool can cause, and build security into AI deployments rather than treating it as a later add-on.
1. Reduce exposure to known vulnerabilities
- Maintain an accurate inventory of internet-facing assets, software, and dependencies so teams know what needs attention.
- Prioritize known exploited and externally exposed vulnerabilities, then shorten the interval between disclosure and remediation.
- Track whether patches and mitigations have actually reached affected systems; a policy or ticket alone does not remove exposure.
The UK NCSC warns that AI could increase exploitation of known, unpatched flaws. It says the time between vulnerability disclosure and exploitation has already shrunk to days and could fall further. The warning makes remediation speed a priority; it does not supply a single deadline that is safe for every organization or vulnerability.
2. Strengthen identity and protect sensitive information
- Use strong identity controls, limit privileged access to what each role requires, and avoid credential reuse.
- Review which AI tools and connected applications can access confidential data, and restrict access to what each use case needs.
- Protect information as it moves between AI systems and third-party services; understand how those services handle the data.
The NCSC identifies weak identity management, poor encryption, and extensive data collection as risks in AI integration. Limiting access helps contain both account compromise and unintended disclosure through connected tools.
3. Secure AI systems throughout their lifecycle
The joint NCSC/CISA and international-agency “Guidelines for secure AI system development” organize security across design, development, deployment, and operation. Apply that lifecycle approach to the system actually being built or adopted:
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- Development: manage data, model, and software supply chains; review components and dependencies for security risks.
- Deployment: protect the model and its infrastructure, limit access, and ensure connected tools have only the permissions they need.
- Operation: maintain logging, monitoring, update management, information sharing, and incident processes after launch.
4. Treat prompt injection and outputs as security concerns
Prompt injection can cause unintended behavior or confidential-data disclosure, particularly where a system accepts untrusted input or can trigger actions. Validate inputs and outputs, constrain what connected systems are allowed to do, and consider whether content from users, documents, or other sources could steer the system into disclosing information or taking an unintended action. Do not treat a model’s response as a substitute for authorization checks in the systems it can reach.
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5. Correlate signals and prepare to respond
Microsoft’s 2026 Digital Defense Report argues that correlating telemetry across endpoint, identity, cloud, application, email, and network systems can reveal patterns missed by siloed analysis. Organizations should make sure relevant teams can investigate across those sources and know how to escalate suspicious activity.
Leadership has a role as well as security teams. Assign clear responsibility for AI and machine-learning security, identify critical assets and the likely impact of disruption, assess supplier security, and prepare incident plans for AI-related events. The New Zealand NCSC’s 2026 report carries the Director-General’s message: “My message to leaders is that cyber security needs your attention now more than ever”.
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