Human or AI: A CISO's guide to identifying machine threats

What AI-driven attacks look like

AI cuts costs and raises quality across the attack lifecycle. Security teams are seeing it applied to the following:

• Reconnaissance and target profiling built from public data.
• Phishing and business email compromise written in fluent, personalized prose.
• Voice and video deepfakes used for payment fraud and help desk social engineering.
• Credential stuffing and account takeover at bot scale.

The quality gain is measurable. Microsoft found that AI-assisted phishing has reached a 54% click-through rate. Traditional campaigns sat at roughly 12%. That's a 450% jump, driven by precision rather than volume.

Malware has changed, too. Google's Threat Intelligence Group documented families such as PROMPTFLUX and PROMPTSTEAL. Both query a large language model (LLM) at runtime to generate fresh obfuscation or new collection commands. Static signatures lose value against code that never looks the same twice.

Why the distinction is hard to draw

AI-enabled attackers imitate people on purpose. They pace requests, rotate residential proxies, mimic browser fingerprints and copy the rhythms of ordinary work.

Benign automation muddies the picture further. Imperva reported that bots made up 53% of web traffic in 2025 and drove 46% of account takeover incidents in financial services. Legitimate AI agents now use the same interfaces and authenticate the same way as customers.

No single signal proves machine involvement. Each method below contributes evidence, and confidence comes from stacking them.

Behavioral biometrics

Behavioral biometrics measure how a session behaves rather than what it claims to be. Sensors capture keystroke cadence, mouse curvature, touch pressure, scroll rhythm and dwell time.

Humans are inconsistent in predictable ways. Scripts and agents show either mechanical regularity or impossible precision. The method works well against account takeover, fake account creation and checkout fraud. Its weakness is replay, since advanced tooling can inject recorded human traces.

Natural language analysis

Language models leave statistical traces in the text they produce. The analysis looks at complexity, sentence-length uniformity, vocabulary spread and transition patterns. Machine prose tends to be smoother and more consistent than human writing.

The stronger control is a comparison against a known baseline. Comparing an inbound message with that sender's historical writing can quickly surface impersonation. Treat detector output as a signal rather than a verdict because these tools misfire on non-native speakers and heavily edited drafts.

Machine learning detection models

Supervised classifiers score sessions, messages and binaries by comparing them against labeled examples of human and machine activity. The value of supervised classifiers comes from combining hundreds of weak indicators that mean little in isolation.

These models need care. Ask vendors about false positive rates, retraining cadence and drift monitoring. A classifier trained in last year's tooling degrades as adversary techniques change.

Anomaly detection

Anomaly detection baselines normal behavior and flags deviation. It's the most direct counter to machine speed because AI can't hide volume and pace. Here are some useful triggers:

• 200 API calls per second from a single service account.
• Lateral movement completed in four minutes.
• Enumeration of an entire directory outside working hours.
• Rate and sequence rather than raw volume alone.

Threat intelligence correlation

Correlation turns isolated alerts into a campaign picture. Enrich local telemetry with external indicators such as newly registered domains, shared TLS fingerprints, reused hosting and known malicious LLM API endpoints.

AI-run campaigns hit many targets at once, so patterns visible across an industry often precede a local incident. Automate the enrichment so analysts see context at triage rather than during investigation.

Deepfake detection tools

Gartner names deepfakes among the four critical threats requiring urgent attention from security leaders. The firm also predicted that 30% of enterprises would stop trusting face biometric identity verification used in isolation by 2026 because of synthetic media.

Since April 2025, attackers have impersonated senior U.S. officials with AI-generated voice and text messages, the FBI warned. The goal was to steal credentials from their contacts. These attacks continue to advance in sophistication.

Detection tools examine artifacts that generation still struggles with, including blink rate, lighting consistency, facial boundary blending and spectral traces in audio. Liveness checks add a second layer. Pair any tool with an out-of-band callback for high-value requests.

Code similarity and metadata analysis

Malware carries authorship signals. Compare samples for structural similarity, error handling style, comment density and reused helper functions.

Human developers leave fingerprints such as idiosyncratic naming, debug strings and compile timestamps clustered in one time zone's working hours. AI-generated code tends toward textbook structure, uniform commenting and unusually complete exception handling. Build metadata that shows no human working rhythm deserves a closer look.

Network traffic inspection

Traffic shape reveals automation. Machine sessions produce regular beacon intervals, uniform packet sizes and parallel connection counts that no analyst could drive by hand.

Add one modern check to the organization's egress rules. Monitor outbound calls to LLM API endpoints from hosts that have no business reaching them, since runtime code generation depends on that channel.

Identity verification and email authentication

Cryptographic controls sidestep the identification problem entirely. Enforce DMARC, SPF and DKIM to cut spoofed senders. Deploy phishing-resistant, device-bound MFA, such as FIDO2 passkeys.

An AI can write a flawless pretext, but it can't produce a hardware-bound private key that it doesn't hold.

Human verification of risky actions

Some decisions should never rest on a single channel. Require independent, out-of-band confirmation for wire transfers, payroll changes, MFA resets and privileged access grants.

The FBI recommends verifying identity by calling back on a known number and agreeing on a verification phrase in advance. Verizon's "2026 Data Breach Investigations Report" found the human element present in 62% of breaches. Mobile-centric voice and text attacks achieved 40% higher click success than email phishing. Process controls remain the last reliable backstop.

Layer methods, then rehearse them

None of these methods is sufficient on its own, and adversaries adapt to any single control. Combine identity, language, behavioral and network signals so that evasion in one layer still triggers another.

Three steps make a practical starting point for CISOs and their teams:

1. Baseline what normal looks like for the organization's users, service accounts and applications.
2. Add rate and sequence detections tuned to machine speed rather than to daily volume.
3. Rehearse a deepfake scenario in the next tabletop exercise, including the callback procedure.

The goal isn't perfect attribution. It's enough confidence to act before a machine-speed intrusion becomes a breach.

Many of these defensive techniques aren't new; they're tried and true. In many cases, mitigating AI attacks is the same as mitigating human attacks. The new class of attacks isn't necessarily about AI speed; it's deepfake attacks. Many human-controlled attacks are fast but don't achieve the level of deception sophistication that an AI deepfake video or audio message can deliver. Focus on the basics and stay vigilant.

Matthew Smith is a vCISO and management consultant specializing in cybersecurity risk management and AI.