AI is helping attackers and defenders work faster, but it has not made core security practices obsolete. Recent reporting describes AI-assisted reconnaissance, social engineering, phishing and malware development; the same reports find that known vulnerabilities, weak identity defenses and other human or systemic failures remain central to successful intrusions.
Does AI make cyberattacks faster?
It can make parts of an operation more efficient. Google Cloud’s M-Trends 2026 says threat actors increasingly use AI for productivity in reconnaissance, social engineering and malware development. Microsoft’s Digital Defense Report 2025 also describes AI-automated phishing and multi-stage attack chains. These reports support a practical conclusion: AI can assist established attack methods, rather than replacing them with an entirely new category of intrusion.
AI is not only an attacker’s tool. Microsoft writes that adversaries and defenders use AI to make operations more effective and efficient, making the technology both a cybersecurity risk and a tool. The value or danger depends on how it is used and secured.
Are hackers using AI to break into systems?
Yes, but evidence of AI use should not be confused with evidence that AI directly caused most breaches. Mandiant’s investigations of targeted attacks from Jan. 1 through Dec. 31, 2025, found that threat actors were using AI operationally. Google Cloud says it did not consider 2025 the year in which breaches were directly the result of AI; in its investigations, the vast majority of successful intrusions still stemmed from fundamental human and systemic failures.
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That finding describes Mandiant’s investigated cases, not every incident worldwide. Microsoft’s report covers its own threat-intelligence observations from July 2024 through June 2025. Neither dataset should be treated as a census of all cyberattacks.
Why do known weaknesses still matter?
Attackers can benefit from faster research or more convincing lures, but they still need an opening. Microsoft says most threats in its reporting targeted known security gaps, including web assets and remote services. In Mandiant’s targeted-attack investigations for 2025, exploits were the most common initial infection vector.
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| Finding | What it means | Scope |
|---|---|---|
| 32% of initial infection vectors were exploits | Exploits were the most common initial vector in the investigation set. | Google Cloud/Mandiant, M-Trends 2026; targeted-attack investigations from Jan. 1–Dec. 31, 2025. |
| 11% were voice phishing; 6% were email phishing | Voice phishing ranked second among observed initial vectors; email phishing was lower in this dataset. | Google Cloud/Mandiant, M-Trends 2026; targeted-attack investigations from Jan. 1–Dec. 31, 2025. |
| Most reported threats targeted known security gaps | Exposure management and timely remediation remain relevant even as AI is adopted. | Microsoft Digital Defense Report 2025; Microsoft threat-intelligence reporting. |
These figures describe different reporting sets and should not be combined. They do not establish the relative frequency of attack methods across the entire internet.
Why are identity and social engineering still important?
Technical defenses can be undermined when an attacker obtains valid credentials or persuades a person to approve an action. In Microsoft’s observed identity attack dataset, 97% of identity attacks were password-spray attacks; that is not 97% of all cyberattacks. In Mandiant’s 2025 investigation set, voice phishing accounted for 11% of initial infection vectors and was the second most common vector.
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For organizations and individuals, strong authentication is a direct response to identity-focused attacks. Microsoft’s 2025 report summary says phishing-resistant multifactor authentication can stop over 99% of identity-based attacks. This is Microsoft’s efficacy claim, not a guarantee against every account compromise or other attack type. A FIDO2-compatible hardware security key can be one way to use phishing-resistant MFA, where the account and device support it; check compatibility before buying or enrolling one.
Do basic cybersecurity practices still work against AI-assisted attacks?
Yes. Baseline controls address the weaknesses attackers continue to exploit, whether their work is AI-assisted or not. NIST notes that many AI security risks are common to software development and deployment: confidentiality, integrity, availability, and the security of supporting software and hardware still matter. Its guidance also identifies AI-specific concerns that require additional attention.
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- BUILT TO LAST: Made from tough, waterproof, and crush-resistant materials. Manufactured in Sweden and programmed in the USA with the highest security standards.
For organizations
- Inventory identities, endpoints, applications, internet-facing assets and AI components so exposed systems and access paths are visible.
- Prioritize remediation of known exploitable weaknesses, especially on internet-facing systems and remote services.
- Protect accounts with strong authentication, including phishing-resistant MFA where feasible, and monitor identity behavior for suspicious activity.
- Prepare incident response and recovery processes, including protected backups and the dependencies needed to restore identity systems and infrastructure.
- Track practical measures such as MFA coverage, patch latency and incident response time. Microsoft explicitly recommends these kinds of measures; they help show whether safeguards are operating, not just documented.
For individuals
- Use a unique, strong password for each account. A password manager may help manage unique credentials, but no specific product is assessed here.
- Enable MFA, preferring a phishing-resistant option when the service supports it.
- Treat unexpected requests to disclose credentials, approve a sign-in or transfer information with caution, including requests made by voice.
What extra risks come with AI systems?
Securing an AI deployment requires ordinary security controls plus a deliberate review of the model’s own attack surface. NIST’s AI security and resilience guidance notes that existing cybersecurity frameworks do not fully cover AI-specific threats such as evasion, model extraction, membership inference and attacks on availability. These risks do not replace familiar concerns; they sit alongside them.
For an AI service, include the model and its surrounding systems in the security inventory. Review who can supply inputs, see outputs, change or access training data, and invoke connected tools or services. Test whether unauthorized inputs or permissions could produce harmful actions or expose information. NIST’s AI 100-2 E2025, published in March 2025, provides a taxonomy of adversarial machine-learning methods, lifecycle stages, attacker objectives and mitigations. It is technical guidance, not an immutable standard; its record notes a correction and a page error with potential updates.
How should a business decide what to fix first?
Start with exposures and controls that can give an attacker access or prevent recovery, then assess how well those controls work in practice. The following order is a practical synthesis of the recommendations in Microsoft and Google Cloud reporting, not a ranked or tested framework.
- Find exposed assets and gaps. Review internet-facing systems, remote services, identities and AI components; identify known exploitable weaknesses and assign remediation owners.
- Reduce identity risk. Measure MFA coverage, strengthen authentication against phishing and password spraying, and monitor suspicious sign-ins and identity behavior.
- Test response and recovery. Measure how quickly teams investigate alerts and contain incidents, and verify that protected backups and essential dependencies can be restored.
- Map AI-specific paths. Document model inputs, outputs, training data, permissions and connected tools; test for unauthorized access, misuse and availability risks.
These priorities address different failure modes. An AI detection tool alone cannot patch an exposed service, prevent every credential attack or make an organization’s backups recoverable.
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