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Artificial intelligence is not replacing hackers or security analysts; it is multiplying what both can do. Attackers use generative AI to research targets, write and translate lures, troubleshoot code, create synthetic voices and images, and support parts of an intrusion. Security teams use AI to sift enormous telemetry volumes, spot suspicious behavior, discover vulnerabilities, filter phishing, and accelerate incident response. The immediate advantage goes to the side that combines automation with better data, identity controls, human review and disciplined security basics.
What has actually changed
AI changes the economics and tempo of cyber operations more than it changes the underlying attack categories. Phishing, credential theft, malware, denial-of-service attacks, data theft and fraud all predate generative models. What AI adds is inexpensive assistance at each stage: a criminal can produce more variants, localize them for different victims, test ideas quickly and maintain a plausible conversation at a scale that would previously require a large team.
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Defenders face the same volume problem from the other direction. A security operations center may have to correlate email, identity, endpoint, cloud, network and application events while an attack is unfolding. Machine learning and large language models can summarize alerts, find relationships and suggest investigations, but they do not make the underlying signals trustworthy. A wrong or manipulated model output can still send an analyst down the wrong path.
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ENISA’s EU-focused analysis of 4,875 incidents from 1 July 2024 through 30 June 2025 illustrates why scale matters. Seventy-seven percent of reported incidents were DDoS, and ENISA reported that more than 80% of observed social-engineering activity was AI-supported phishing by early 2025. Those figures describe ENISA’s incident set and period; they are not a worldwide rate for every organization.
How attackers are using AI
Reconnaissance and target research
Models can summarize public filings, job advertisements, technical documentation and social profiles into a target brief. They can translate material, explain unfamiliar technologies and suggest likely suppliers or exposed services. This does not give an attacker secret access, but it reduces the time needed to turn public information into a prioritized list of people and systems.
Phishing and social engineering
Generative systems produce fluent email, chat and SMS messages in multiple languages and tones. An attacker can create separate versions for finance staff, executives, help-desk agents or contractors, then iterate when a message is ignored. Grammar mistakes that once revealed many scams are less reliable indicators. AI can also maintain a back-and-forth conversation, making a fraudulent request appear to be a normal business exchange.
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Microsoft’s Digital Defense Report 2025 describes AI-automated phishing and multi-stage attack chains. The danger is not only the first message; a successful lure can lead to credential capture, mailbox rules, cloud-token theft and later movement through the organization.
Malware and command support
CISA warns that generative AI can lower the cost and increase the scale of malware development, including code intended to evade defenses. In practice, models are useful for explaining APIs, troubleshooting errors, adapting scripts and generating components. They can also assist command-and-control development and data-exfiltration planning, as reflected in later threat-intelligence observations. Code produced by a model still needs testing, and defenders can often detect repeated infrastructure or behavior even when the code is customized.
Denial of service and automation
AI can help operators manage lists of targets, tune traffic patterns and automate routine decisions in DDoS campaigns. It does not remove the need for compromised devices or rented infrastructure, but it can make coordination cheaper and faster. ENISA’s incident data shows DDoS remained the dominant reported incident category in its EU sample.
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Voice cloning, fake images and deepfakes
CISA identifies voice cloning, realistic fake images, counterfeit profiles and deepfakes as practical abuse cases. Microsoft reports synthetic media targeting companies and government organizations for sensitive information and financial loss. A short voice sample or convincing video call can be enough to pressure an employee into changing payment details, revealing a one-time code or bypassing a help-desk procedure.
How defenders are using the same technology
| Security task | AI contribution | Human control still required |
|---|---|---|
| Telemetry analysis | Groups related events across endpoint, identity, email, cloud and network data; summarizes likely attack chains. | Validate data quality, investigate unusual context and approve containment. |
| Phishing defense | Scores messages, detects impersonation patterns and identifies malicious links or attachments. | Confirm trusted contacts and handle ambiguous business requests. |
| Secure coding | Suggests safer code, explains dependencies and flags common weaknesses during development. | Review changes, test them and assess business impact. |
| Vulnerability discovery | Finds patterns in source, configurations and asset inventories that deserve testing. | Verify findings and prioritize remediation by exposure and exploitability. |
| Incident response | Creates timelines, drafts queries and recommends investigative steps. | Authorize disruptive actions and preserve evidence. |
Microsoft says its systems screen about 5 billion emails daily, a 2025 figure that demonstrates why automated filtering is necessary at internet scale. Filtering is not a verdict: a malicious message can look normal, and a legitimate message can be unusual. Effective programs combine model scores with authentication, reputation, user reporting and analyst review.
Google lists sifting complex telemetry, secure coding, vulnerability discovery and operational streamlining as defensive uses. These applications improve speed, but they depend on complete and correctly labeled data. A model cannot correlate an endpoint it cannot see, and it cannot reliably distinguish a compromised administrator from a legitimate one without identity and activity context.
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Who has the advantage?
There is no permanent winner. Compare both sides on six operational axes rather than asking whether AI is “for” attackers or defenders.
| Axis | Attacker opportunity | Defender requirement |
|---|---|---|
| Speed and scale | Generate and send thousands of tailored messages or code variants. | Automate triage, enrichment and containment before analysts are overwhelmed. |
| Quality and credibility | Produce fluent, localized lures and convincing synthetic media. | Validate identity, intent, payment changes and unusual requests out of band. |
| Visibility | Exploit gaps between email, identity, endpoint, cloud and model logs. | Centralize telemetry with synchronized time, reliable asset ownership and searchable history. |
| Human oversight | Use people for final persuasion and decisions that models cannot safely automate. | Require approval for high-impact actions and challenge unverified model conclusions. |
| Fundamentals | Look for exposed services, default credentials and unpatched software. | Reduce internet exposure, remove defaults, patch promptly and test recovery. |
| Collective learning | Reuse successful lures and infrastructure. | Share indicators, vulnerabilities and lessons quickly with trusted partners. |
Attackers often need only one successful interaction, while defenders must protect every exposed path. That asymmetry makes basic controls unusually valuable. AI can increase a defender’s speed, but it cannot compensate for an internet-accessible management interface with a default password or a critical system that has not been patched.
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What AI cannot safely do on its own
It does not guarantee novel attacks
The strongest public observations through January 2025 showed productivity gains rather than a universal breakthrough in autonomous exploitation. Later tracking demonstrates broader assistance across reconnaissance, lures, command-and-control and exfiltration, but “AI-assisted” does not mean a model independently planned and completed an intrusion. Keep those claims distinct when assessing risk or explaining an incident.
It can be wrong or manipulated
Large models can hallucinate facts, misread context and follow adversarial instructions embedded in data. CISA’s JCDC AI Cybersecurity Collaboration Playbook notes that AI systems’ data-driven, non-deterministic properties create risks including model poisoning, data manipulation and adversarial inputs. Treat model output as a lead, not as evidence by itself.
It can leak sensitive information
Submitting customer records, source code, credentials or incident details to an unapproved service can create a new data-exposure path. Define which models may process which data, retain prompts and outputs according to policy, and log access so investigators can determine what was shared.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical defense plan for AI-enabled attacks
- Map where AI is already used. Inventory approved assistants, security products, developer tools, integrations and model APIs. Record what data each system can read, retain or send onward.
- Harden exposed assets. Identify internet-accessible misconfigurations, remove unnecessary services, replace default credentials and patch outdated software. CISA specifically recommends these exposure-reduction measures alongside AI-specific controls.
- Strengthen identity proofing. Require phishing-resistant multifactor authentication for privileged and remote access. For payment, payroll, password-reset and mailbox-rule requests, use a previously known phone number or an in-person confirmation rather than replying to the initiating message.
- Connect the telemetry. Feed email, identity, endpoint, cloud and network events into a searchable system. Preserve model prompts, recommendations and approvals when an AI tool participates in a response, while protecting sensitive content.
- Automate low-risk actions first. Start with enrichment, duplicate-alert suppression, URL detonation and account-risk scoring. Put human approval before disabling production systems, deleting data, blocking a major partner or rotating organization-wide credentials.
- Exercise synthetic-media scenarios. Train staff to pause when a familiar voice or video requests secrecy, urgency or a financial change. Run drills for a deepfake executive call and a compromised help-desk account, then measure how quickly the request is independently verified.
- Test model security. Evaluate prompt-injection resistance, access controls, logging, output validation and data-retention settings. Include poisoning and adversarial-input tests in the same risk process used for other critical software.
- Share actionable lessons. Participate in trusted industry or government channels. CISA’s JCDC AI Cybersecurity Collaboration Playbook provides voluntary processes for sharing AI-related incidents and vulnerabilities with government, industry and international partners.
How to respond to an AI-powered phishing or deepfake attempt
When the request is still pending
- Stop the transaction or credential change; do not reply through the same channel.
- Verify the person and request using a known contact method and a second approver.
- Preserve the original message, headers, URLs, call details and any recording according to evidence policy.
- Report the event to the security team so similar messages can be searched across the organization.
When someone has already clicked or complied
- Disconnect or isolate the affected device if instructed by your response team, without destroying volatile evidence.
- Revoke exposed sessions and tokens, reset credentials from a clean device and review multifactor-authentication changes.
- Search for mailbox forwarding rules, newly registered applications, suspicious OAuth grants and unusual cloud activity.
- Check payment instructions with the relevant financial-control owner and notify banks or partners through established contacts.
- Document what happened, update detections and brief other teams before the same lure reaches them.
Building collective resilience
No organization sees the entire threat landscape. Shared indicators, vulnerability reports and incident patterns can shorten the time between one victim’s discovery and another organization’s protection. CISA’s JCDC playbook describes voluntary information-sharing processes intended to help protect organizations from emerging AI threats. Participation works best when reports include timestamps, affected assets, observed prompts or synthetic media, indicators of compromise and the controls that stopped or failed.
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What security leaders should measure
- Time to useful signal: how long from an alert to a validated, prioritized incident.
- Time to contain: how quickly a compromised identity, endpoint or cloud token is isolated.
- Verification rate: the percentage of high-risk payment, access and help-desk requests confirmed through an independent channel.
- Exposure reduction: counts of internet-facing services, default credentials and overdue critical patches.
- Model quality: false-positive and false-negative rates, drift over time and performance on adversarial tests.
- Human workload: analyst hours saved without increasing missed incidents or unauthorized automated actions.
- Learning speed: time from an internal discovery to detection deployment and trusted external sharing.
These measures keep the conversation grounded. A faster model that creates more false positives may increase fatigue; a highly accurate detector that runs after a week of data delay may miss the attack window.
The bottom line for the AI arms race
AI is making cybercrime more scalable and believable while giving security teams faster ways to see and contain attacks. Current evidence supports a capability multiplier, not a world in which machines independently replace criminals or analysts. Organizations that pair carefully governed AI with phishing-resistant identity, reduced exposure, rapid patching, connected telemetry, independent verification and practiced response will gain more defensive value than organizations that simply add another chatbot. Human judgment remains the control that turns machine speed into dependable security.
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