Quantum Readiness: The Questions CISOs Must Ask Now

Most enterprises still treat quantum risk like a “future problem.” That’s a mistake—because quantum methods are already in your environment, just not in the form people expect.

In late 2025, a pattern I keep seeing is this: engineering teams adopt quantum-inspired optimization or simulation libraries inside familiar tooling (Python, MATLAB, GPU stacks). Results improve. Timelines shrink. Security teams never get a heads-up that a new computational approach—often designed to migrate to external quantum compute later—is now part of the production workflow.

This matters for the AI in Cybersecurity conversation because quantum readiness is fundamentally a visibility and control problem. And visibility and control are exactly where AI can help: continuously mapping crypto use, detecting anomalous compute/data paths, and enforcing policy at scale when “what’s running” changes faster than humans can track.

Quantum risk isn’t only about encryption—it’s about visibility

Quantum security planning usually starts and ends with “post-quantum cryptography (PQC).” Yes, PQC is urgent. But the more immediate operational risk is simpler: security teams don’t reliably know when quantum-inspired software enters production workflows.

Quantum-inspired tools can look like ordinary numerical libraries, optimization modules, or simulation add-ons. They integrate cleanly into existing pipelines, which is great for adoption—and terrible for governance. If you can’t identify where these components run, you can’t:

• Assess sensitive data exposure (inputs, intermediate states, outputs)
• Validate integrity (model drift, tampering, poisoned parameters)
• Prepare for the shift from on-prem compute to external quantum services

The operational shift CISOs miss

The near-term change isn’t “you bought a quantum computer.” It’s this:

Workloads built today to run on CPUs/GPUs are being architected to move to quantum processors tomorrow.

That future move often implies new connectivity patterns, new vendors, new key management assumptions, and different audit surfaces. If your program only tracks “apps” and “infrastructure,” you’ll miss “computational methods” as a category of risk.

AI-driven security operations can bridge that gap by classifying workloads based on behavior and dependencies, not just package names.

The encryption clock is real—plan around data lifetime, not hype

The cryptographic threat from quantum computing isn’t speculative math. The practical question is timing and exposure windows.

A useful way to think about it: your risk is proportional to how long your data must remain confidential.

• If you store customer PII with regulatory retention requirements, you may need confidentiality for years.
• If you protect defense, aerospace, critical infrastructure, or proprietary R&D, the confidentiality window may be a decade or more.

That’s where the “harvest now, decrypt later” threat becomes operational. An attacker doesn’t need a quantum computer today to create damage later. They only need to steal ciphertext now and wait.

Three encryption paths—and the one most enterprises should bet on

Security leaders tend to hear three options:

1. Stay on current crypto until quantum arrives. This is the “we’ll handle it later” plan. It’s also how you end up with emergency migrations.
2. Quantum-based encryption (using quantum properties). Promising, but it introduces deployment constraints and still has a long road for broad enterprise use.
3. Post-quantum cryptography (PQC) designed to resist both classical and quantum attacks. This is the most realistic enterprise pathway for the next several years.

My stance: PQC is the default plan for enterprises. Not because it’s easy—it isn’t—but because it’s the most compatible with how real systems evolve: gradually, with mixed environments, and lots of legacy.

The quantum questions CISOs should be asking—plus what “good” looks like

CISOs don’t need to become quantum physicists. They need a tighter set of questions that force clarity across security, engineering, and compliance.

1) “Where is quantum-inspired code running right now?”

Answer first: If you can’t inventory it, you can’t secure it.

Ask for an inventory that goes beyond “applications” and includes:

• Optimization/simulation libraries used in production pipelines
• Specialized GPU workloads used for modeling and scheduling
• Third-party solvers embedded in engineering tools
• Any workloads labeled “quantum,” “QAOA,” “annealing,” “Ising,” or “quantum-inspired” in documentation or configuration

What good looks like: a living inventory connected to CI/CD, SBOM data, and runtime telemetry—not a spreadsheet updated once a year.

Where AI helps: use AI to correlate build artifacts, package metadata, and runtime behavior to flag likely quantum-method workloads, even when teams didn’t label them clearly.

2) “What data touches those workflows—and what’s the confidentiality horizon?”

Answer first: Data classification needs a time dimension.

For each workflow, you want to know:

• Inputs (raw datasets, design parameters, operational data)
• Intermediate artifacts (checkpoint files, cached matrices, derived features)
• Outputs (simulation results, optimized schedules, designs)
• Retention and sharing (who gets it, where it lives, how long it persists)

Then add the question most teams skip: How long would this data be valuable to an adversary?

• 30 days? 1 year? 10+ years?

What good looks like: policies that trigger stronger controls for long-life secrets (tokenization, tighter access, encryption upgrades, restricted egress).

Where AI helps: anomaly detection on data movement—especially when intermediate artifacts start leaving expected networks or appear in unexpected storage.

3) “If this moves to external quantum compute, what changes in our trust model?”

Answer first: External compute changes your threat model more than the math does.

Even organizations that insist on in-house data centers may face a reality: early quantum processors are likely accessed via specialized facilities. That implies:

• New network paths (private links, gateways, API-based submission)
• New identity surfaces (non-human identities, service accounts, tokens)
• New logging and forensics requirements

What good looks like: a pre-approved reference architecture for “external compute workloads,” with:

• Zero trust access controls
• Strong egress governance
• Mandatory logging to your SIEM
• Documented incident response playbooks

Where AI helps: automated policy enforcement and alerting when workload egress patterns deviate from the approved architecture.

4) “What cryptography is in use today—and how fast can we swap it?”

Answer first: Crypto agility is the real objective; PQC is the catalyst.

Instead of asking “Are we PQC-ready?” ask:

• Where do we use RSA/ECC and in what modes?
• Which systems are hard-coded vs centrally managed?
• Which vendors can’t upgrade quickly?
• What breaks if we rotate algorithms and keys?

What good looks like: a crypto inventory tied to services and data flows, plus a migration backlog with owners, timelines, and rollback plans.

Where AI helps: AI can speed up crypto discovery by scanning configurations, code repositories, and traffic patterns to identify cryptographic handshakes and libraries in use.

5) “How do we validate correctness and detect tampering in these pipelines?”

Answer first: Quantum-inspired workflows can create new integrity risks, not just confidentiality risks.

When optimization and simulation outputs drive high-stakes decisions—manufacturing tolerances, supply chain schedules, energy distribution—you need to consider:

• Output manipulation (altering results to influence decisions)
• Model/parameter poisoning (tampering with solver settings)
• Dependency compromise (malicious library updates)

What good looks like: validation gates, reproducibility checks, and signed artifacts for critical runs.

Where AI helps: behavioral baselining for solver workloads—flagging runs that are statistically unusual in runtime, resource usage, or output distribution.

A practical 90-day quantum readiness plan (that doesn’t derail everything)

Most security programs don’t fail because they didn’t care. They fail because the plan was too abstract to execute.

Here’s a 90-day sequence that works without waiting for a “quantum strategy deck.”

Days 1–30: Build the inventory and name owners

• Stand up a crypto inventory initiative (services, certificates, key stores, libraries)
• Identify top 10 workflows in engineering/analytics that rely on optimization/simulation
• Assign a business owner + security owner for each workflow
• Add a procurement question: “Does this include quantum-inspired methods or a future quantum compute path?”

Days 31–60: Threat model the “external compute” future

• Draft a reference architecture for external compute access
• Standardize non-human identity controls (issuance, rotation, least privilege)
• Define logging requirements (what must be captured for forensics)
• Run a tabletop exercise: “Vendor quantum service outage + suspected data exfiltration”

Days 61–90: Start crypto agility work where it’s easiest

• Prioritize quick wins: internal services, API gateways, managed TLS endpoints
• Replace hard-coded crypto with centrally managed libraries where possible
• Document systems that cannot migrate quickly and create compensating controls

Throughout these 90 days, the most valuable AI investment isn’t a “quantum detector.” It’s AI-assisted visibility: asset discovery, crypto discovery, and anomaly detection tuned to compute/data movement.

People also ask: what should CISOs do about quantum right now?

“Do we need a quantum computer strategy?”

You need a quantum security strategy, not a hardware strategy. Focus on inventory, governance, crypto agility, and external compute trust models.

“Is post-quantum cryptography required immediately?”

Not everywhere at once. Prioritize based on data lifetime and exposure. But don’t wait to start discovery and planning—migration lead time is the real constraint.

“How does AI in cybersecurity help with quantum readiness?”

AI helps where humans can’t keep up: continuously mapping cryptography usage, detecting anomalous workload behavior, and automating policy enforcement across fast-changing environments.

What to do next: make quantum readiness measurable

If quantum readiness stays vague, it will be deprioritized behind the next incident, audit, or vendor fire drill. Treat it like any other security program: define controls, metrics, and owners.

Start with three measurable outcomes:

1. Crypto inventory coverage: percentage of business-critical services with known algorithms, key stores, and rotation ownership
2. External compute readiness: reference architecture approved and tested with at least one pilot workflow
3. AI-driven anomaly detection: baselines for critical optimization/simulation pipelines, with alerting wired into SecOps

Quantum threats and AI in cybersecurity are converging into one reality: security teams are being asked to defend systems whose behavior changes faster than manual governance can track. The teams that win will be the ones that treat quantum readiness as a visibility and automation problem—starting now.

Where would quantum exposure hurt you most: long-life secrets, high-stakes engineering outputs, or external compute dependency?