Artificial intelligence is reshaping cybersecurity from both sides of the conflict. Security teams are using AI to analyze alerts, identify unusual behavior, and respond faster. At the same time, attackers are using the same technology to create more convincing deception, accelerate reconnaissance, and experiment with new forms of malware.
In 2026, the most important change is not that AI has replaced cybersecurity professionals. It has not. The deeper change is that AI is becoming a force multiplier: it increases the speed, scale, and reach of both defenders and attackers. Organizations that understand this shift can gain an advantage, while those that treat AI as simply another software tool may introduce risks they cannot easily see.
AI is helping defenders make sense of overwhelming data
Modern networks generate enormous volumes of information from endpoints, cloud platforms, identity systems, email gateways, and applications. Human analysts cannot examine every event individually. AI-assisted security tools can help by finding patterns, connecting related signals, prioritizing alerts, and summarizing an incident for investigation.
This can reduce time spent on repetitive triage and allow experienced analysts to focus on decisions that require context and judgment. AI can also support threat hunting, malware analysis, vulnerability prioritization, and the creation of detection rules. Used carefully, it becomes a practical assistant rather than an autonomous replacement for a security team.
The word carefully matters. An AI-generated conclusion can be incomplete or wrong, especially when the underlying data is poor. High-impact actions—such as isolating systems, disabling accounts, or blocking business processes—should remain observable, reversible, and subject to appropriate human approval.
Social engineering is becoming faster and more convincing
Phishing has always relied on human trust. Generative AI makes it easier to produce polished messages, translate them into multiple languages, imitate a particular writing style, and customize a lure using publicly available information about a company or employee.
Microsoft’s 2025 Cyber Signals report on AI-powered deception described how attackers use AI tools to build detailed target profiles and create convincing fraudulent content. The result is not necessarily a completely new kind of crime. Instead, familiar scams can be produced more quickly, cheaply, and at greater scale.
Deepfake audio and video add another layer of risk. A message that appears to come from an executive, vendor, or family member may no longer be trustworthy simply because the voice or face seems familiar. Organizations need verification procedures that do not depend on appearance alone, particularly for payments, password resets, confidential files, and changes to account access.
Attackers are experimenting with AI-assisted malware
Threat actors are also exploring ways to use AI during technical attacks. Google Threat Intelligence reported experimental malware called PROMPTFLUX that queried an AI service for obfuscation techniques intended to help it evade static detection. Google emphasized that the observed sample was experimental and did not demonstrate the ability to compromise a victim by itself, but the research shows the direction in which attacker experimentation is moving.
Microsoft later documented SesameOp, a backdoor that misused a legitimate AI service as a covert command-and-control channel. The case did not involve an AI model autonomously conducting an attack. It demonstrated something more immediate: attackers can abuse trusted AI infrastructure as part of conventional malware operations.
These examples suggest that defenders should monitor how AI services are accessed from their environments. Unexpected API calls, unusual service accounts, unapproved AI tools, and abnormal data transfers may become increasingly important investigative signals.
AI systems create their own attack surface
AI is not only a security tool or an attacker tool. It is also a system that must be secured. Models, training data, prompts, plugins, external tools, retrieval databases, and user permissions can all create paths for abuse.
The National Institute of Standards and Technology’s Adversarial Machine Learning taxonomy organizes important risks such as evasion, data poisoning, privacy attacks, and generative-AI misuse. These risks can affect different stages of an AI system’s life cycle. For example, an attacker might manipulate data used by a model, craft inputs that cause unsafe behavior, attempt to extract sensitive information, or misuse a model’s legitimate capabilities.
This means an organization cannot secure an AI application by looking only at the model. It must also protect the surrounding data, identities, APIs, software dependencies, and business processes.
Cybersecurity strategy is becoming AI-aware
NIST’s developing Cyber AI Profile frames the challenge around three connected priorities: securing AI system components, using AI to improve cyber defense, and thwarting AI-enabled attacks. That is a useful way to think about the subject because no single product can address all three.
Collaboration will also matter. The Cybersecurity and Infrastructure Security Agency’s AI Cybersecurity Collaboration Playbook encourages structured information sharing about AI-related incidents and vulnerabilities. As technologies change quickly, defenders benefit when lessons learned by one organization can improve resilience across many others.
Seven practical actions organizations can take
- Inventory AI use. Identify approved and unapproved AI tools, the data they access, their owners, and the business processes they influence.
- Strengthen identity security. Use phishing-resistant multifactor authentication where possible, limit privileges, and review service accounts and API credentials.
- Verify sensitive requests through a second channel. Payments, credential changes, and confidential disclosures should not rely only on email, voice, or video.
- Protect AI data and connections. Control access to training data, retrieval systems, prompts, plugins, and external tools. Treat inputs and generated outputs as potentially untrusted.
- Keep humans responsible for high-impact decisions. Define which automated actions require review, and make important actions traceable and reversible.
- Monitor AI activity. Log model access, API usage, tool calls, administrative changes, and unusual data movement so that incidents can be investigated.
- Update incident-response plans. Practice scenarios involving deepfake impersonation, compromised AI credentials, poisoned data, and malicious use of connected AI agents.
The human advantage still matters
AI will continue to change the economics of cybersecurity. It can help defenders process information faster, but it can also help attackers generate content and test ideas at a scale that was previously difficult to achieve.
The strongest response is not to avoid AI or to trust it without question. It is to combine useful automation with sound security fundamentals: clear ownership, strong identity controls, protected data, careful monitoring, practiced response procedures, and informed human judgment. In 2026, organizations that build those foundations will be better prepared for both the opportunities and the risks that AI brings.
Sources and further reading
- NIST: Adversarial Machine Learning—A Taxonomy and Terminology of Attacks and Mitigations
- NIST: Cybersecurity Framework Profile for Artificial Intelligence
- CISA: AI Cybersecurity Collaboration Playbook
- Google Threat Intelligence: Advances in Threat Actor Usage of AI Tools
- Microsoft: AI-Powered Deception
