AI Cybersecurity: Spotting Spyware on Smartphones

Ronald Deibert boards a flight without a phone or laptop, lands in the US, and buys fresh devices at an Apple Store before heading to meetings. That’s not paranoia. It’s operational security from someone who assumes he’s being watched minute by minute.

Deibert leads the Citizen Lab, a University of Toronto-based research group that has spent two decades exposing digital espionage and commercial spyware. Their work is a useful gut-check for anyone building or buying AI-powered digital services in the United States: the same technical ecosystem that enables personalization, automation, and smarter customer experiences also enables surveillance at scale.

This matters for our AI in Cybersecurity series because the real dividing line isn’t “AI vs. no AI.” It’s whether AI systems are deployed with clear purpose limits, auditability, and accountability—or whether they become opaque tools that quietly expand monitoring and control.

Spyware is a business—and smartphones are the beachhead

Modern phone spyware succeeds because it combines commercial incentives with technical asymmetry. Attackers need one working exploit path; defenders need to block thousands.

Citizen Lab’s investigations helped popularize an uncomfortable truth: much of today’s most intrusive surveillance isn’t improvised by nation-states from scratch. It’s bought. The lab’s reporting has tied commercial spyware to attacks on journalists, dissidents, human-rights defenders, and political figures. They were also among the first to show how surveillance can spill across borders—targeting exiles and diaspora communities whose “home country” politics follow them via their devices.

Smartphones are uniquely valuable targets because they’re:

• Always-on identity tokens (calls, texts, email, messaging apps)
• Location beacons (GPS, Wi‑Fi, cell triangulation)
• Microphones and cameras in your pocket
• Multi-factor keys for your bank, workplace, and government portals

What “being compromised” actually means in 2025

Phone compromise is often silent and persistent. The most dangerous spyware aims to avoid obvious symptoms—no popups, no ransomware splash screen. Instead, it focuses on:

• Message interception (including encrypted app metadata and backups)
• Contact graph mapping (who you talk to and when)
• Location history reconstruction
• Credential theft (sessions and tokens, not just passwords)
• Microphone/camera activation

AI doesn’t have to be inside the spyware to make it worse. AI makes targeting cheaper: automated social engineering, faster reconnaissance, and better victim selection.

Where AI fits: privacy threats and privacy defenses

AI expands both the attack surface and the defense toolbox. If you’re a US-based digital service provider, that’s not philosophical—it changes how you design products, handle data, and communicate trust.

On the threat side, AI is now routinely used for:

• Spearphishing at scale: highly tailored messages that sound like a colleague, vendor, or family member
• Deepfake voice/video prompts: social engineering to bypass helpdesks or convince targets to install “security updates”
• Automated open-source intelligence (OSINT): quickly assembling a target’s habits, devices, coworkers, travel, and interests
• Credential stuffing optimization: prioritizing likely-success login attempts based on patterns

On the defense side, AI is genuinely useful when it’s applied to anomaly detection and response automation—not as a magical accuracy machine, but as a way to reduce time-to-detect.

Here’s what works in practice:

• Behavioral baselining: flagging abnormal sign-in location changes, device fingerprint shifts, and unusual token refresh patterns
• Mobile threat defense (MTD) signals + AI triage: correlating app installs, network anomalies, and OS indicators to rank risk
• Natural-language security copilots (carefully constrained): summarizing alerts, suggesting playbooks, and generating incident updates without exposing sensitive customer data

A healthy AI security posture isn’t “collect everything.” It’s “collect the minimum, prove you used it responsibly, and delete it on schedule.”

Trust is the product: how ethical AI differs from surveillance tech

Most companies get this wrong: they treat “AI features” as a branding layer and privacy as a compliance checkbox. That’s backwards. If your service touches identity, payments, health, education, or location—even indirectly—trust is a core feature.

Citizen Lab’s work highlights a contrast worth stating plainly:

• Surveillance systems optimize for invisible collection and control.
• Ethical AI digital services optimize for user agency and verifiable boundaries.

Practical design rules for AI-powered digital services

If you’re building AI into customer communication, content workflows, support automation, or fraud prevention, these rules keep you out of the “spyware-adjacent” danger zone:

1. Purpose limitation (write it down): Define what the model is allowed to do and what it’s explicitly not allowed to do.
2. Data minimization: If you don’t need precise location, don’t collect it. If you don’t need microphone access, don’t request it.
3. User-visible controls: Give users a simple way to view, export, and delete sensitive data used for personalization.
4. Short retention by default: Reduce what can be stolen later.
5. Audit trails for sensitive actions: Log when models access customer records, generate decisions, or trigger escalations.
6. Human-in-the-loop for high-risk outcomes: Account lockouts, identity verification, payment holds, and safety reports deserve review.

These are not “nice-to-haves.” They’re how you prevent legitimate AI automation from drifting into creepy monitoring.

What US organizations should copy from Citizen Lab’s playbook

The Citizen Lab model is effective because it treats digital threats as a public-interest problem, not only a technical puzzle. Even if your company isn’t investigating spyware, you can adopt the same mindset.

1) Assume you’re a target—then design accordingly

Deibert traveling without devices is extreme for most people, but the principle scales:

• Separate admin access from daily-use devices
• Use hardware security keys for privileged accounts
• Keep mobile OS updates on aggressive schedules
• Restrict sideloading and unknown profiles on managed phones

2) Build security tooling for real humans, not ideal users

Citizen Lab researchers contributed to tools like Security Planner because most guidance is either too technical or too vague.

For AI-powered services, “usable security” looks like:

• Short explanations in plain language (“Why we flagged this login”)
• Fewer blanket warnings, more specific risk signals
• Step-by-step recovery flows that don’t require perfect memory or spare devices

3) Treat transparency as an operational requirement

If your AI helps detect fraud, moderate content, or route support tickets, publish the boundaries:

• What inputs the model uses
• What it never uses (for example, “we don’t read your private messages”)
• How long data is retained
• How users can appeal decisions

People don’t need a 40-page whitepaper. They need clarity.

AI in cybersecurity on mobile: a realistic detection stack

You won’t “AI your way” out of spyware risk with one product. The practical approach is layered defense with AI used where it’s strongest: correlation and prioritization.

A layered approach that actually reduces risk

1. Endpoint basics (non-negotiable): OS updates, secure boot, full-disk encryption, strong device passcodes.
2. Account protection: phishing-resistant MFA, impossible-travel detection, token/session management.
3. Network visibility: DNS filtering, TLS inspection where appropriate (and lawful), and anomaly alerts for suspicious destinations.
4. Mobile Threat Defense (MTD): checks for malicious profiles, risky apps, jailbreak/root indicators, suspicious accessibility abuse.
5. AI-assisted SOC workflows: deduplicate alerts, correlate across identity + endpoint + network, and generate incident timelines.

“People also ask” questions (answered plainly)

Can AI detect spyware on a smartphone? AI can help triage signals (odd network patterns, risky app behaviors, unusual account activity), but it won’t reliably detect high-end spyware alone. Layered controls plus rapid response is the winning combo.

Is commercial spyware only used by authoritarian regimes? No. The supply chain is commercial, and deployments can expand into domestic contexts. That’s why governance and oversight matter as much as technical defenses.

What’s the biggest mistake companies make with AI and privacy? Over-collecting data “just in case.” Large datasets don’t automatically create better outcomes; they do create bigger breach impact.

What to do next: a short action plan for teams

If you’re responsible for AI-powered digital services in the US—product, security, compliance, or customer experience—here are practical next steps you can run in January.

1. Inventory your AI inputs: list every data source feeding models (CRM fields, support tickets, device info, location, voice recordings).
2. Rank them by sensitivity: identity, finance, health, minors, precise location, and private communications belong at the top.
3. Set hard retention limits: choose a number (30/60/90 days) and implement deletion automation.
4. Add “abuse testing” to AI QA: test how systems behave if an insider misuses access or if an attacker prompts for sensitive data.
5. Write a user-facing transparency note: one page, plain language, updated quarterly.

If you do only one thing: stop treating privacy and security as separate workstreams. In an AI-enabled service, they’re the same system.

The bigger point for AI in cybersecurity

Citizen Lab exists because someone has to do the tedious work of proving what’s happening on real devices in the real world. Their investigations are a reminder that digital services don’t become “safe” because the interface looks friendly or the brand is trusted.

AI can absolutely power legitimate, ethical, and transparent digital services in the United States—fraud prevention, smarter support, safer platforms, faster incident response. But the direction depends on choices: what you collect, what you log, what you explain, and what you refuse to build.

If surveillance is the cautionary tale, trust is the product strategy. When your next AI feature request comes in, ask one question before you ship: can we prove this helps the user more than it helps someone watching them?