The Risk of Starting Quantum Migration Without Cryptographic Discovery

By QuAi Security Labs  |  8 min read  |  Post-Quantum Cryptography · Crypto Agility · Migration Strategy

 

Most organisations underestimate the size of their cryptographic footprint. Starting a quantum migration without a complete cryptographic inventory is the single most expensive mistake an enterprise can make — and the most common one.

 

The migration problem no one wants to discuss

Post-quantum cryptography migration has reached the top of virtually every enterprise security roadmap. NIST finalised its first three PQC standards in August 2024. Governments have issued mandates. Analysts are publishing timelines. Boards are asking questions.

And in response, many organisations are doing exactly the wrong thing: they are picking a PQC algorithm, buying a vendor's migration toolkit, and starting to migrate without first understanding what they are actually migrating.

Cryptography is not centralised in the way most security teams assume. It is not sitting in one PKI server waiting to be upgraded. It is embedded in APIs, hardcoded into application code, baked into firmware, negotiated dynamically by TLS libraries, used by databases for at-rest encryption, leveraged by DevOps pipelines for code signing, and referenced by dozens of third-party integrations many of which the security team has never audited.

Many organizations are not prepared to achieve migration because of the lack of visibility and not having the right technologies. (Ponemon Institute, 2024)

 

What cryptographic discovery actually reveals

When organisations deploy comprehensive cryptographic discovery tools for the first time, the results are almost always a surprise. A typical enterprise with 5,000 employees and a moderately complex cloud environment will discover:

 

•        Thousands of TLS certificates across internal and external services, many approaching expiration

•        SSH keys distributed across servers with no centralised inventory or rotation policy

•        Hardcoded cryptographic keys and secrets in application source code repositories

•        Legacy cryptographic algorithms (MD5, SHA-1, RSA-1024) still in active use in production systems

•        Third-party dependencies using outdated cryptographic libraries that will not support PQC standards

•        Cloud storage encryption configurations that vary wildly across business units

•        API endpoints using weak or misconfigured TLS that would be exploitable by a sufficiently resourced adversary

 

None of these items appear on a standard asset inventory. None are caught by conventional vulnerability scanners. And every single one of them represents a migration task that must be completed before quantum migration can be declared finished.

The harvest-now-decrypt-later threat changes the timeline

The conventional wisdom on quantum timelines suggests that cryptographically relevant quantum computers are 5-8 years away. This has led many security leaders to treat PQC migration as a medium-term planning exercise rather than an urgent priority.

The harvest-now-decrypt-later (HNDL) attack model invalidates this reasoning entirely. Nation-state adversaries and sophisticated criminal organisations do not need quantum computers to be a present threat, they need only to capture and store encrypted data today, then decrypt it when the quantum hardware arrives. For any data whose confidentiality needs to extend 10 or more years into the future, eg. patient health records, classified government communications, intellectual property, long-term financial contracts, the window for protection is already closed if encryption has not been upgraded.

If your data is sensitive for more than 10 years, you are already in the harvest window. The migration deadline is not when quantum computers arrive, it is NOW.

 

Why migration without discovery fails in practice

Unknown assets create hidden gaps

A migration project that does not begin with comprehensive discovery will invariably leave cryptographic assets behind. The security team migrates the systems they know about and considers the project complete. The undiscovered assets, the hardcoded key in the legacy billing application, the RSA-1024 certificate on the partner API endpoint, the SHA-1 signing key for the firmware update server remain vulnerable indefinitely. The organisation has spent significant resources on migration and believes it is protected when it is not.

Prioritisation becomes arbitrary without inventory data

Effective migration requires prioritising systems by the sensitivity of the data they protect and the likelihood of being targeted. Without a comprehensive inventory, prioritisation defaults to institutional familiarity, teams migrate the systems they work with every day, not necessarily the systems that are most at risk. Critical assets that are rarely touched day-to-day often end up at the bottom of the queue.

Third-party and supply chain dependencies create blockers

Many of the cryptographic assets in an enterprise environment are not owned by the enterprise they are provided by software vendors, cloud providers, hardware manufacturers, and technology partners. A comprehensive discovery process identifies these dependencies early, allowing the organisation to begin vendor conversations and upgrade cycles well in advance. Without this visibility, supply chain dependencies surface as blockers mid-migration, causing delays and cost overruns.

Compliance demonstrations become impossible

NIST SP 800-207 (Zero Trust Architecture), CISA's PQC guidance, the EU Cyber Resilience Act, and emerging financial sector regulations all require organisations to demonstrate that they have inventoried their cryptographic assets and have a documented migration plan. An organisation that began migrating before completing discovery cannot produce the inventory documentation that regulators will require. It has to restart the discovery process after the fact, at additional cost and delay.

The right sequence: discover, then migrate

The correct approach to quantum migration is a three-phase process in which discovery is not just the first step, but an ongoing capability that persists throughout and after migration:

 

•        Phase 1 — Complete cryptographic inventory: deploy automated discovery across all infrastructure layers external-facing services, internal networks, APIs, cloud environments, endpoints, OT/IoT systems, and source code repositories. Build a structured cryptographic Bill of Materials (CBOM) that captures every algorithm, key length, certificate, and protocol in use

•        Phase 2 — Risk-stratified migration planning: use the CBOM to identify which assets are most vulnerable (weak algorithms, short key lengths, systems handling sensitive long-lived data) and which carry the most regulatory exposure. Build a migration plan ordered by risk, not by convenience

•        Phase 3 — Continuous post-migration monitoring: cryptographic assets do not stand still. New deployments introduce new vulnerabilities. Third-party updates can regress migrated systems. Post-migration monitoring ensures that the gains made in the migration are not silently eroded over time

 

What crypto agility means in practice

The goal of a well-executed quantum migration is not merely to arrive at a PQC-compliant state, it is to build crypto agility into the organisation's infrastructure. Crypto agility means that when the next cryptographic standard changes (and it will change again, as it always has), the organisation can respond quickly because it has the visibility and tooling to identify affected systems and execute controlled transitions.

Organisations that complete migration without building discovery and monitoring capabilities will find themselves in the same position in five years that they are in today, uncertain about what they have, uncertain about its vulnerability, and facing an urgent scramble to migrate before a deadline.

Conclusion

The quantum migration challenge is real and the timelines are shorter than they appear. But the greatest risk is not that organisations start too late but it is that they start uninformed. Discovery is not a preliminary step that can be skipped to accelerate the timeline; it is the foundation that makes the timeline achievable.

Flying blind through a cryptographic migration is not faster. It is slower, more expensive, and more dangerous than building the visibility that makes migration both efficient and durable.

 

Ready to take action? QuAi Security Labs helps enterprises discover, inventory, and migrate their cryptographic infrastructure to quantum-safe standards while securing every AI component in your environment. Visit https://www.quaisecurity.com to request a demo or book a quantum readiness assessment.

The Shadow AI Crisis: Why Your Organisation's Biggest Security Threat Is the AI You Don't Know About

 

Shadow AI is the new shadow IT — except the blast radius is significantly larger. When employees deploy rogue AI models, the risks extend from data leakage to model poisoning to regulatory non-compliance, often without any security team visibility.

The problem no one is talking about openly

In 2017, the security industry spent years warning organisations about shadow IT — employees spinning up unauthorised cloud instances, SaaS tools, and personal devices outside the visibility of IT. Most enterprises eventually built governance frameworks to address it. Then came AI.

Today, the velocity at which AI is being embedded into enterprise workflows dwarfs anything shadow IT ever produced. Developers are integrating third-party AI APIs into production pipelines without security review. Marketing teams are feeding customer data into generative AI tools. Finance analysts are running sensitive forecasts through models hosted on external servers. And almost none of it is being tracked.

The 2024 IBM Cost of a Data Breach report found that breaches involving AI systems cost an average of 18% more than non-AI breaches, while remediation time was 25% longer. Yet most organisations still have no formal inventory of the AI models running across their infrastructure.

What exactly is shadow AI?

Shadow AI refers to any artificial intelligence model, tool, pipeline, or component that is deployed or used within an organisation without the explicit knowledge, approval, or monitoring of the security and IT governance functions. It exists on a spectrum:

 

•        Consumer-grade AI tools used for work tasks (ChatGPT, Claude, Gemini) with sensitive data pasted in

•        Third-party AI APIs integrated directly into internal applications by development teams

•        Open-source models (Llama, Mistral, Falcon) self-hosted on cloud instances outside standard deployment pipelines

•        AI-powered features embedded invisibly in SaaS platforms the company already uses

•        Fine-tuned or custom models trained on proprietary data sets without data governance oversight

 

The challenge is not that employees are acting maliciously. They are trying to be productive. The problem is that every undocumented AI component is an unmonitored attack surface.

The five security risks that keep CISOs awake

1. Data leakage at inference time

When an employee pastes customer PII, financial projections, or source code into an external AI tool, that data may be retained, logged, or used for model training by the vendor. Even with enterprise agreements in place, the data has left the perimeter. Without shadow AI discovery, security teams cannot know how frequently this is happening or which datasets are most at risk.

2. Model tampering and supply chain poisoning

Open-source models downloaded from public repositories may have been tampered with before publication. A backdoored model embedded in a production ML pipeline is extraordinarily difficult to detect through standard security monitoring. The model behaves correctly in normal operation but produces manipulated outputs under specific trigger conditions — a technique researchers call a trojan attack.

3. Vector database exposure

Many AI applications use retrieval-augmented generation (RAG) architectures that rely on vector databases storing embeddings of proprietary documents. These databases are frequently misconfigured, lacking encryption and access controls appropriate for the sensitivity of the underlying data. Shadow AI deployments almost never receive a security architecture review, making vector database exposure one of the fastest-growing enterprise risk categories.

4. Regulatory non-compliance

The EU AI Act, places specific obligations on organisations deploying high-risk AI systems including requirements for human oversight, documentation, and transparency. If an AI system is undocumented because no one knew it existed, the organisation is non-compliant by definition. GDPR and CCPA intersect with AI when personal data is processed by models, and regulators are increasingly scrutinising AI data flows as part of broader privacy enforcement.

5. Privilege escalation through AI agents

The newest and most dangerous category of shadow AI risk involves AI agents, systems that can take actions, execute code, query databases, and interact with external services autonomously. An employee deploying an AI agent with access to internal APIs may, intentionally or not, grant that agent significantly more privilege than any human user would be authorised to hold. Traditional identity and access management controls are not designed for non-human agents, and the gap is being exploited.

Why traditional security tools cannot solve this

Most enterprise security stacks were designed to monitor human users and known software assets. AI models do not fit neatly into either category. A model is not a user, but it processes data. It is not traditional software, but it executes logic. It does not have a CVE, but it may carry significant risk.

Data loss prevention (DLP) tools can intercept some external AI tool usage, but they cannot inventory self-hosted models. Cloud security posture management (CSPM) tools can identify misconfigured cloud resources, but they cannot detect a model that has been tampered with at the weights level. 

What organisations need is a purpose-built AI discovery and inventory capability. One that continuously scans infrastructure for AI components, builds a comprehensive AI Bill of Materials (AI-BOM), assesses each component's risk profile, and provides the monitoring continuity to detect changes over time.

 

An AI-BOM is the foundation of AI security governance. Without knowing what AI exists in your environment, you cannot protect it, govern it, or demonstrate compliance with emerging AI regulations.

 

Building a shadow AI governance programme: where to start

Organisations that are serious about addressing shadow AI risk should approach the problem in three phases:

 

•        Discovery first: deploy automated scanning across Data Centre/cloud environments, API traffic, and development pipelines to build a complete inventory of all AI components, models, and data flows

•        Risk stratification: classify each discovered AI component by data sensitivity, deployment context, access privileges, and compliance relevance, not all shadow AI carries equal risk

•        Continuous monitoring: shadow AI is not a one-time audit problem; new models and integrations appear continuously, and the inventory must update in real time

•        Policy and controls: establish clear enterprise policies for AI procurement, deployment, and data handling, and integrate AI security reviews into existing software development and procurement workflows

 

The organisations that get ahead of shadow AI will not be the ones with the most restrictive policies, they will be the ones with the clearest visibility. You cannot govern what you cannot see.

Conclusion

Shadow AI is not a future risk. It is present in virtually every organisation running enterprise software today. The question is not whether it exists in your environment. It almost certainly does but whether you have the visibility to identify it, assess it, and act on it before a regulator or an adversary does it for you.

The organisations building the strongest AI security postures in 2026 are not waiting for an incident to justify investment in AI discovery. They are treating AI inventory as a foundational security capability; the same way they treated asset inventory in an earlier era.

 

Ready to take action? QuAi Security Labs helps enterprises discover, inventory while securing every AI component in your environment. Visit https://www.quaisecurity.com to request a demo

Quantum Computing: A Looming Disruption to Cybersecurity

Demystifying Quantum threat to security and privacy

As quantum computing inches closer to practical reality, this paper dives deep into its dual role as both a threat and a solution to cybersecurity. It systematically reviews how quantum technologies could break modern encryption while also offering new paradigms for securing digital systems.

The Threat Landscape

• Shor’s Algorithm could render RSA and ECC obsolete, exposing sensitive data to future decryption.
• The Harvest Now, Decrypt Later strategy is already in play—adversaries are stockpiling encrypted data for quantum-powered breaches.
• Quantum-enabled cyberattacks may bypass current defenses, especially in critical infrastructure and IoT ecosystems.

Emerging Defenses

• Post-Quantum Cryptography (PQC): Algorithms based on lattice problems and hash functions are being standardized to resist quantum attacks.
• Quantum Key Distribution (QKD): Uses quantum mechanics to detect eavesdropping and ensure secure key exchange.
• Hybrid Encryption Models: Combining classical and quantum-safe methods to build transitional resilience.

Strategic Takeaways

• Businesses must inventory cryptographic assets and assess quantum risk exposure.
• Crypto-agility is key—systems should be adaptable to new algorithms as standards evolve.
• Education and collaboration across industries will be vital to navigate the quantum shift.

 

Quantum computing posing a challenge to businesses

 

Quantum Computing posing a Challenge to Businesses

This paper explores how quantum computing, once a theoretical concept, is now emerging as a disruptive force across industries. It highlights both the threats and opportunities quantum technologies present to businesses, especially in areas like cryptography, optimization, and data analysis.

Key Challenges

• Cryptographic Vulnerability: Quantum algorithms like Shor’s can break widely used encryption methods (RSA, ECC), posing a serious threat to data security.
• Talent Shortage: There’s a lack of quantum-literate professionals, making it hard for businesses to adopt and integrate quantum solutions.
• Infrastructure Readiness: Most companies lack the hardware, software, and strategic frameworks to support quantum computing.

Business Opportunities

• Quantum-as-a-Service (QaaS): Cloud-based quantum platforms are democratizing access, allowing companies to experiment without owning quantum hardware.
• Optimization & Simulation: Industries like logistics, finance, and pharmaceuticals can benefit from quantum-enhanced modeling and decision-making.
• AI Acceleration: Quantum computing can supercharge machine learning algorithms, enabling faster and more accurate predictions.
• Material Discovery & Drug Design: Quantum simulations can model molecular interactions at unprecedented precision, revolutionizing R&D.

Strategic Recommendations

• Invest in Quantum Literacy: Upskill teams and collaborate with academic institutions to build internal capabilities.
• Rethink Security: Begin transitioning to post-quantum cryptography to future-proof sensitive data.
• Explore Partnerships: Engage with quantum startups and service providers to stay ahead of the curve.
• Monitor Regulatory Developments: Stay informed about evolving standards and compliance requirements.

Conclusion

Quantum computing is not just a technological leap—it’s a strategic imperative. Businesses that proactively adapt will gain a competitive edge, while those that delay risk falling behind in a rapidly transforming digital landscape.