AI and quantum computing are often treated as separate security conversations. AI belongs to today’s threat landscape, where attackers are already using automation to move faster, scale campaigns, and make familiar attacks harder to detect. Quantum belongs to a future that can feel more abstract, defined by large-scale systems powerful enough to challenge the cryptography that protects sensitive data. For security leaders, however, these are not disconnected problems. They are part of one continuous preparedness challenge.
Neither threat waits for an organization to finish preparing for the other. AI is increasing pressure now, while quantum changes the risk equation for encrypted data later. Together, they make one point clear: organizations need better visibility into their environments and the ability to update security controls, architectures, and cryptographic protections before urgency becomes crisis.
AI Raises the Pressure Today
AI is already reshaping the defender’s job. Attackers can use it to generate convincing phishing lures, adapt social engineering attempts, accelerate reconnaissance, and automate activity that once required more manual effort. Identity, endpoint protection, monitoring, response, and user awareness (the fundamentals) still matter, but they now have to operate under greater speed, scale, and pressure.
That makes AI risk a question of operational readiness. Can a security team see what is happening across its environment, distinguish normal activity from suspicious behavior, and respond before a campaign spreads across users, devices, applications, or data stores? Those present-tense questions are already shaping security programs.
Quantum Changes the Risk Equation Tomorrow
Quantum introduces a different kind of risk. It is not about making today’s phishing email more persuasive or today’s malware campaign easier to automate. It is about the cryptographic foundations that protect confidential data, transactions, software updates, identities, and communications. A sufficiently powerful quantum computer could undermine widely used public-key cryptography, creating risk for data that must stay confidential for years.
This is where “harvest now, decrypt later” becomes important. An adversary does not have to break encrypted information today to make it valuable. They can collect protected data now, store it, and wait for future quantum capability to make decryption possible. The White House has framed this risk directly, warning that ongoing cyber activity creates the possibility of adversaries collecting information now and decrypting it later once large-scale quantum computers are operational.
For some data, that future risk may be low. But intellectual property, regulated information, sensitive communications, financial records, critical infrastructure data, and government or defense-related material can have long confidentiality timelines. The value of the data may outlast the strength of the cryptography protecting it today, making quantum readiness a planning issue now.
The timeline is uncertain, but the direction is not. Researchers connected to the Harvard Quantum Initiative have described useful quantum computing as moving faster than many earlier expectations, with progress in fault tolerance bringing early forms of large-scale systems closer into view. Organizations should not treat quantum disruption as immediate, but they should stop treating it as comfortably distant.
The convergence between AI and quantum is not that AI is shortening the timeline for breaking today’s encryption. These threats operate differently. AI accelerates attacks that already work and increases the scale at which adversaries can execute them. Quantum threatens the cryptography itself, later, once capable systems exist. The connection is preparedness: both force organizations to understand what they have, where risk exists, and how quickly they can adapt.
That is where preparedness becomes practical. The organizations that will be best positioned for both AI-driven threats and post-quantum risk are the ones that can translate awareness into action: mapping where risk lives, deciding what matters most, and building enough flexibility to update controls before disruption forces the issue.
Building Readiness Through Visibility and Agility
Preparing now starts with visibility. Security teams cannot protect what they cannot see, and they cannot modernize what they have not identified. In the AI context, visibility means understanding users, devices, applications, identities, data flows, and behaviors well enough to spot suspicious activity and respond quickly. In the quantum context, visibility means understanding where cryptography is used across systems, products, services, certificates, protocols, applications, and third-party dependencies.
From there, preparedness depends on the ability to change. This is where crypto-agility becomes essential. At a high level, crypto-agility is the ability to identify, replace, and update cryptographic algorithms, keys, certificates, libraries, and protocols without rebuilding entire systems from scratch. It is not just a technical feature. It is a discipline that combines architecture, inventory, governance, vendor coordination, policy, testing, and operational planning.
Crypto-agility matters because migration will not happen all at once. Organizations will need to prioritize. Some systems protect short-lived data and may be lower risk. Others protect information with long confidentiality lifespans or support critical business processes and may need earlier attention. Some cryptography is obvious and centrally managed. Some is buried in legacy applications, embedded devices, supplier products, or integrations that no single team fully owns.
AI readiness follows a similar pattern. The organizations best positioned to respond are not simply the ones adopting the newest security tools. They are the ones building adaptive programs: clear ownership, well-understood assets, integrated detection and response, tested processes, and practical governance around how technology is used. The same operating muscle that helps a team manage AI-driven pressure can help it manage cryptographic transition: know the environment, identify what matters most, make changes deliberately, and keep improving.
“Organizations don’t need to predict the future perfectly. They need the visibility and agility to adapt when it arrives.” –Kimberlee Coombes, Sr. Inside Solution Architect at Connection
Preparing for AI-driven attacks and post-quantum risk should not be treated as two separate modernization efforts. Both depend on the same security discipline: visibility into what you have, clarity on what matters most, and the flexibility to change before the threat landscape forces your hand.
Organizations do not need to solve every AI and quantum challenge immediately. They do need to build capabilities that make future decisions easier: improving asset and cryptographic visibility, identifying data with long-term confidentiality requirements, understanding dependencies, evaluating vendor readiness, and aligning security planning with business risk.
As part of our month-long discussion of AI and cybersecurity for Cybersecurity Awareness Month, you can view our recent discussion in a webinar titled AI + Quantum: The Security Threats You Can’t Afford to Ignore. Our Security Center of Excellence team brings both halves together, treating near-term AI security controls and long-term quantum readiness as one framework, rather than two separate projects, applied to the security posture you already have.
For now, the most important shift is mindset. AI and quantum may arrive on different timelines, and they may threaten different parts of the security stack. But they point to the same requirement: adaptable security programs built on visibility, prioritization, and the ability to change. The future will not wait for perfect readiness. The work starts with becoming ready enough to move with it.
