The Q Day countdown is on. Are your systems ready for Y2Q?

Q Day is the moment in time — coming far sooner than later — when quantum computers become sufficiently mature at enough scale to crack the encryption algorithms that protect the majority of our data today. Cybersecurity authorities are warning that migration to quantum-resilient systems must begin now, given how quickly AI is advancing quantum capability. 

By 2030, even today’s most robust encryption systems will be no match for quantum capabilities. This has profound implications for modern life as we know it. The safety of financial institutions and fiat and crypto currencies all face imminent risk. Government secrets, company IP, private health information – all could instantly be exposed. The World Economic Forum noted that “all regulations and laws regarding privacy, data management etc. would be impossible to uphold,” significantly eroding public trust.

Nothing that's encrypted with today's algorithms will be safe from quantum computers. It’s a sobering message that should, rightfully, stoke fear — and motivate action: Now is the time to protect yourself, your systems, and your data with quantum-resilient encryption.  That starts with a baseline understanding of the power of quantum computing. 

What is quantum computing?

Quantum computing (and the QPUs it runs on) are significantly more powerful than the CPUs, GPUs, and NPUs of today's computers and data centers. That's because of how quantum computers work; they take advantage of quantum mechanics, probabilities, and superpositions at the atomic and subatomic level.

Think of this way: traditional computing relies on binary (a coin flip of 1 or 0, or classical bits); quantum computing is like flicking the coin so that it spins on end, with an incredible number of possible positions (qubits). This makes quantum computers especially good at solving certain problems and undertaking certain computations... including cracking encryption.

Until recently, quantum computing has been in the R&D phase. Why? Regardless of their technology — cryogenic superconductors, trapped-ion, or photonic — building large-scale, fault-tolerant quantum computers is complicated and expensive. That means that only a handful of powerful nation-states (e.g., USA, China), major research universities, and major tech companies (IBM, Google) have been investing in its development. 

But now commercial availability is sprinting ever closer. And with it, Q Day.

What is Q Day?

Q-Day refers to the point when quantum computers become powerful enough to break the encryption systems that safeguard today’s digital world. 

Few sectors would be untouched once those defenses fall. Most secure systems — including banks, communications networks, and especially blockchains — rely on RSA and elliptic-curve encryption, both of which could be unraveled once quantum machines reach sufficient scale. 

Among them, blockchain networks may face the steepest test, since their open, transparent design could make digital assets uniquely vulnerable once quantum decryption becomes possible. Q-Day could then upend the foundation of cryptocurrency security. Because blockchains rely on digital signatures, a powerful quantum computer could theoretically extract private keys from public ones, allowing hackers to seize funds. 

Mati Greenspan, Founder of Quantum Economics, warned that when that day comes, “many blockchains won’t survive,” though he noted that some projects are already adapting for a post-quantum era — a shift he believes will “define the next era of digital ownership.” 

An estimated 25 million Bitcoin addresses currently hold more than $100 in value, and Aixiv’s Quantum report estimates it could take six to twelve months to migrate those funds to quantum-safe wallets, according to research cited in Forbes.

And Q Day is coming sooner than later, with AI now accelerating its progress by helping researchers model and characterize the vast complexity of quantum systems. These AI models — spanning machine learning, deep learning, and transformer-based approaches — can approximate the state of massively complex quantum systems, allowing scientists to bypass the exponential scaling hurdles that have long limited quantum research.

By predicting physical properties like magnetization and entropy, AI tools act as surrogates that speed up testing, verification, and optimization of quantum hardware. This capability is vital for advancing quantum computing applications in encryption, materials discovery, and pharmaceuticals. 

Because AI enables faster characterization and optimization, it shortens the timeline to practical quantum computing, effectively bringing Q-Day closer.

When is Q Day coming?

As recently as 2022, an estimate from experts in the field predicted an average of 15 years until Q day would hit — ie, 2037. Since then the date is pulling closer and closer to the present. 

Expert forecasts on timing vary, but most agree it’s coming sooner than later. A 2025 analysis from PostQuantum projects a machine capable of breaking RSA-2048 by around 2030, give or take two years. Cybersecurity Ventures predicts that Y2Q will arrive on or around Jan. 1, 2031. An industry survey shows 61% of security professionals believe current encryption could be compromised within two years, and another 28% expect cracks within three to five years.

In other words, the runway to Q Day has been cut in half. We know this because of the key milestones that have already been hit. As described in Post Quantum analysis, these include:

That leaves one milestone yet to reach: cracking RSA-2048.

What’s at risk the moment Q Day hits

Q-Day would mean that most of the world’s existing encryption systems could be broken, rendering most common data security strategies instantly obsolete and threatening the security of the global digital economy. 

Critical systems that depend on RSA security and elliptic-curve cryptography — such as financial transactions, blockchain operations, and secure communications — would be vulnerable to quantum decryption. 

The arrival of quantum computers capable of this feat would undermine trust in digital systems and compromise sensitive data at every level, from individuals to governments, and across critical infrastructure sectors like transportation, food and ag, energy, and government and defense.

Cybersecurity authorities are advising organizations to move swiftly to invest in post-quantum encryption. For example, the UK’s National Cyber Security Centre advises migration to quantum-safe systems by 2028, with full adoption by 2035. 

In the USA, a bipartisan bill has been proposed to ensure the federal government prepares for encryption-breaking quantum computers. Meanwhile the NSA has been warning for years about “harvest now, decrypt later” strategies, in which attackers capture encrypted data today with the expectation of decrypting it once quantum computing becomes powerful enough. 

“The world will either suffer unimaginable financial, security, and technological catastrophes that taken together will be an order of magnitude worse than cybercrime, which is estimated to cost the world $10.5 trillion USD annually by 2025,” said Steve Morgan, editor-in-chief at Cybercrime Magazine of Q Day. “OR the world will be a much safer place.” 

How SanQtum keeps your data and systems safe from Q Day risks

As AI accelerates quantum development, the timeline for safe reliance on today’s encryption methods is shrinking, creating pressure for early adoption of quantum-resilient standards.

SanQtum delivers that protection with new quantum-safe algorithms. The US National Institute of Standards and Technology has released Federal Information Processing Standards (FIPS) publications for three quantum-resistant cryptographic algorithms. Two of the standards (ML-KEM and ML-DSA) were developed by IBM Research cryptography researchers in Zurich with external collaborators, and the third (SLH-DSA) was co-developed by a scientist who has since joined IBM Research. 

SanQtum provides zero-trust network architecture with quantum-resilient encryption, offered as a plug-and-play service so customers can scale rapidly. Our NIST-approved quantum-resilient encryption keeps your organization’s information secure — both data in transit and at rest — including against “harvest now, decrypt later” attacks and the dawn of Q Day.

Ready to prepare for Q Day? Let’s discuss how SanQtum can help defend your organization from the fast-approaching threat.

Available Infrastructure cybersecurity solution now protecting watsonx, offered by IBM as SanQtum AI

Trusted edge AI now a reality, offered as a fully integrated, fully managed solution

TYSONS CORNER, Va. – December 19, 2025 – Available Infrastructure today announced that its cybersecurity solution, SanQtum AI, is advancing the mission to “make AI available” by protecting IBM's watsonx AI and data platform. IBM will offer this fully integrated Platform as a Service directly to help governments, enterprises, and other partners achieve sovereign AI at the edge, where it’s needed most. SanQtum AI embeds IBM's trusted watsonx stack with Available's zero trust, quantum-resistant edge infrastructure. The result is the most secure and performant AI and data platform on the market — one that gets past power requirements, delivers ultra-low latency, and strengthens connectivity to enable faster decision-making at machine speed. 

As enterprise AI adoption accelerates, keeping models safe and secure from poisoning is non-negotiable. Cyber attack risk already looms large for any and all cyber-physical systems, with the cost of cybercrime projected to nearly triple by 2027. Quantum attacks pose a fast-dawning threat, too, with Q Day on the horizon and “harvest now, decrypt later" attacks already well underway. 

In an environment defined by accelerating threats, the path to unlocking the future of AI runs through a robust edge installation strategy, where cloud and edge inferencing work in concert to enable real-time performance and resilient operations across diverse settings. 

Available Infrastructure engineered SanQtum AI to deliver maximum protection, faster and easier. SanQtum AI delivers a solid, safe, efficient path to an AI-driven enterprise, bundling IBM-trusted solutions to defend against model poisoning, quantum attacks, and other vulnerabilities. IBM’s watsonx.ai and watsonx.governance deliver accurate, defensible AI with built-in auditability; the SanQtum network adds security-grade controls like policy enforcement, access control, continuous monitoring, and tamper-resistant logging, so teams can validate outputs and stand up to adversarial and cyber threats. 

This is defensible AI at its best delivered in a sovereign, secure environment. It supports organizations in building an environment for the long term, while protecting investments in data and IP from nation-state attacks and other threats. With micro-edge data centers delivering decentralized AI and neo cloud architecture, SanQtum AI enables real-time response and reduced data management costs.

SanQtum AI features include:

“Including SanQtum AI in IBM’s native solutions offerings marks a significant milestone in our joint effort to protect the sectors that shape our modern world,” said Daniel Gregory, CEO of Available. “As an IBM embedded Platinum Partner, we are proud to combine the power of our cybersecurity solution with watsonx to help more organizations safeguard their path to an AI-driven enterprise.”

To learn more about SanQtum AI, and to contact the Available team, visit www.availableinfrastructure.com.

###

About Available Infrastructure
Based in Northern Virginia along the Washington, DC, beltway, Available Infrastructure combines national security-grade cyber protection and AI-powered, quantum-ready edge computing into an integrated solution for critical infrastructure, sensitive data, and enterprise AI models. In today's and tomorrow's evolving landscape, this unique combination keeps operational technology (OT) and cyber-physical systems (CPS) safer, while delivering a decision-making advantage for agencies, enterprises, and institutions. Available unites distinct business units — Available Networks and Available Power — and builds upon their deep experience in power grids, infrastructure development, zero trust networking, cybersecurity, and artificial intelligence and quantum computing.

To learn more visit www.availableinfrastructure.com.

Media Contact
Nikki Arnone, Inflection Point Agency for Available Infrastructure
719-357-8344
nikki@inflectionpointagency.com

Smart transportation 101: Why secure, edge AI-backed strategy is the foundation for success

A traffic signal that adapts in real-time to ease congestion. A connected vehicle that warns drivers of black ice ahead. An ambulance that gets priority at every intersection on its route to an emergency.

These are all examples of smart transportation technology already transforming how we move through cities and states. The stakes couldn’t be higher — and understanding what ‘smart transportation’ really means is the first step to getting it right.

Smart transportation leverages interconnected technologies to help cities and states better allocate resources, reduce energy consumption, cut costs, and provide a more inclusive and equitable experience for communities. At its core are smart technologies: connected, data-driven systems that use sensors, automation, and analytics to collect and act on information autonomously, continuously adapting to real-time conditions rather than simply following pre-programmed instructions.

But beyond efficiency gains, these systems are also becoming vital for public safety, from managing emergency response times to protecting drivers, transit users, cyclists, and pedestrians from danger.

As these networks grow more connected and automated, they demand a new approach: one where robust cybersecurity and intelligent edge computing work together to protect lives and maintain the critical services communities depend on every day.

The collision of cybersecurity and AI

Understanding this new landscape requires looking at how smart transportation technologies nest within each other — each layer expanding capabilities while amplifying both opportunities and risks.

At the foundation sits intelligent transportation systems (ITS), the broadest infrastructure — integrated systems using sensors, connected devices, and analytics to monitor and manage traffic flow, signals, tolling, transit operations, and more. 

Within this ITS infrastructure, vehicle-to-everything communications (V2X) enable a vast network of real-time data exchange between vehicles, traffic signals, emergency systems, pedestrian devices, and other infrastructure — sharing information about road conditions, hazards, and traffic patterns.

Among the various participants in this V2X ecosystem, connected and automated vehicles (CAVs) represent a particularly transformative category: vehicles with internet connectivity and varying levels of automation that can not only receive information but actively respond to their environment and coordinate with surrounding systems in real time.

From ITS infrastructure upgrades to V2X pilot programs to CAV-ready corridors, cities and states across the US are actively investing in developing and implementing smart systems to improve efficiency, safety, and mobility. 

But as these systems grow more connected, they also become more vulnerable.

Key risks and challenges in smart transportation 

The same connectivity that enables innovation introduces new vulnerabilities. Following are key risks transportation managers need to be aware of — and guard against:

  1. Growing connectivity means growing cyber risk. Every connected device — from traffic cameras to in-vehicle systems — represents a potential entry point for cyberattacks.

    A compromised traffic management system or connected vehicle network can present a public safety crisis that could delay emergency responders, cause gridlock, or worse.
  2. Public safety stakes are immediate — and potentially catastrophic. When these systems fail or are breached, they can pose existential threats. State and city transportation agencies must contend with risks from criminals, terrorists, and nation-state adversaries who could manipulate traffic signals to cause crashes at every intersection or grind entire cities to a halt in gridlock.

    Attackers could intercept payment credentials at EV charging stations, knock critical charging infrastructure offline, or disable crash detection systems precisely when they're needed most.

    Even without malicious attacks, poorly maintained or outdated systems can leave emergency vehicles stuck in traffic unable to reach those who need help, or allow accidents to go undetected until it's too late.
  3. Bandwidth constraints are mounting. Smart transportation systems generate massive amounts of data — video feeds from traffic cameras are particularly data-intensive, but connected vehicles, sensors, and infrastructure devices all contribute to the growing demand.

    Backhauling all this data to centralized cloud systems quickly overwhelms network capacity, creating bottlenecks that can compromise system performance. Edge processing is critical to filter, analyze, and act on data locally rather than transmitting everything across constrained networks.
  4. Latency undermines real-time operations. In smart transportation, milliseconds matter. A connected vehicle detecting black ice, a traffic signal coordinating with approaching emergency vehicles, an automated system identifying an accident — these scenarios require split-second decisions and responses. Cloud-based processing introduces delays that prevent these systems from operating effectively, undermining the very efficiencies and capabilities they were designed to provide.

The upside: When implemented securely and with edge computing, smart transportation delivers on its promise. 

Making transportation smarter and safer

When secure, smart transportation delivers transformative results that save lives and improve how cities function. Consider the range of what’s possible:

Leveraging edge AI and cybersecurity for effective smart transportation

The societal benefits of smart transportation are immense. But they can only materialize when security is the technology foundation. With robust cybersecurity measures, including secure, plug-and-play combinations of edge AI and cybersecurity, agencies can deploy smart transportation solutions with confidence.

Edge AI deployments solve the infrastructure challenge by processing data locally, keeping response times low and reducing network burden — while also minimizing the attack surface by limiting data transmission to the cloud. 

Paired with quantum-resilient encryption, zero-trust networking, and built-in update mechanisms, these measures make thousands of distributed ITS devices resilient against evolving threats from day one.

Ultimately, proactive cybersecurity and infrastructure strategy empower municipal and state transportation agencies to stay ahead of a range of threats — while keeping roads safer and smarter for the public.

Intelligent transportation systems are only as safe as the security behind them. Contact our team to discuss your transportation system needs.

Image: iStock | Josh Kizziar Photography

Brookfield report forecasts $7T in AI infrastructure — and every node is a security risk

AI infrastructure is scaling at breakneck speed, with massive investment fueling new data centers, GPU clusters, and edge systems. That growth also expands the attack surface, raising the risk of costly breaches, operational disruption, and compromised trust for organizations across industries.

“Building the Backbone of AI,” a new report by Brookfield, a global investment firm whose Brookfield Asset Management (BAM) division boasts more than $1T in assets under management (AUM), estimates that more than $7 trillion will be invested in AI infrastructure over the next decade — spanning upgraded power grids, global connectivity from fiber and telecommunications to satellites, and modular hardware designed to keep pace with rapid innovation. 

This expansion is actively multiplying the attack surface. Every new node, from hyperscale cloud facilities to portable edge units, introduces fresh opportunities for cyber and physical threats. (Crucially, our SanQtum cybersecurity solution secures nodes as this attack surface expands and infrastructure becomes more distributed.) And as AI gets baked into more software and apps that didn't have it before, the digital attack surface multiplies, too — making every added piece of intelligence an exponential vulnerability multiplier.

For leaders across industries like healthcare, energy, industrial, financial, government, manufacturing, and transportation and logistics, it’s critical to understand the scope of this emerging threat landscape.

Key trends from Brookfield’s report — and their cybersecurity implications

The Brookfield report — which outlines opportunities to invest in the infrastructure that is expected to support the next industrial revolution — sheds detailed light on key trends with deep implications for cybersecurity.

  1. Inference will dominate AI workloads by 2030. The report forecasts that most compute will soon be spent on inference, not training. AI inference is the ability of a trained AI model to recognize patterns and draw conclusions from information it hasn’t seen before. It underpins many of AI’s most exciting applications, such as generative AI, and allows models to imitate the way people think, reason, and respond to prompts.

    While AI training typically occurs in centralized, hyperscale cloud data centers, inference increasingly happens at the edge — on distributed, sometimes portable devices that require near real-time, ultra-low latency access to compute resources. 

    This shift means that model integrity, runtime protection, and on-device data security will be just as important as securing training pipelines in centralized environments.
  2. Distributed deployment expands the attack surface. Edge systems, mobile units, and geographically scattered nodes offer inherent security advantages — deployments with limited access points can be more resilient against cyber attacks targeting centralized infrastructure like telecom networks or power grids. But distributed deployments are also often physically exposed, harder to monitor, and attractive targets for theft or tampering.

    These mixed advantages and risks require security strategies that go beyond traditional firewalls, demanding comprehensive protection to address firmware, hardware, and physical threats simultaneously.
  3. Hardware must be built for upgradeability. Brookfield’s report emphasizes modular, upgradeable hardware to keep pace with AI innovation. This imperative for adaptability extends directly to cybersecurity systems, which must be designed with the same flexibility in mind. Security architectures need trust anchors, cryptographic modules, and firmware that can be upgraded or replaced without disrupting operations, while organizations must stay current with emerging technologies, and be prepared to deploy them accordingly.

    Implementing a trusted cybersecurity SaaS strategy can give you the power of the most robust technology, while freeing you from having to invest directly in the hardware or constantly monitor for updates. For example, as a managed service, SanQtum and SanQtum AI take care of this for you. We’ve designed our hardware to be modular, so we can swap and upgrade switches, routers, chips, and other components. But you don’t need to worry about that. We handle the headache, just as if we were updating software.
  4. Cyber-physical convergence increases risk. AI infrastructure relies on integrated systems like IoT cooling sensors, power distribution, and robotics. Each system adds new cyber-entry points and interdependencies. This convergence means that edge security must evolve beyond traditional software patches, including tamper detection, geofencing, robust encryption, and rapid remote wipe capabilities that can respond to threats across both digital and physical domains.

Security priorities for edge AI stakeholders

Whether building AI infrastructure or deploying it, all organizations need zero trust architecture and robust security practices. Beyond these fundamentals, here are priorities by role:

For infrastructure providers:

For enterprise AI deployers:

Why now is the time to defend your organization

Brookfield’s report on the trillions pouring into AI infrastructure investment highlights how massive and distributed growth will be — and with that scale comes a wider range of potential attack surfaces. Many organizations are rushing to deploy AI infrastructure and models without prioritizing security from the outset, waiting until deployment risks unpatchable vulnerabilities and exposes critical models, data, and physical systems to attack. 

Now is the time to build in zero trust cybersecurity. Unsecured AI is an inexcusable, massive risk. AI infrastructure, model training, and inference need to grow hand-in-glove with cutting-edge cyber protections to stay ahead of risks that have never been greater.

Security needs to grow with AI, not after it. One of the easiest and fastest ways to do so is via an as-a-service model, such as SanQtum and SanQtum AI. Contact our team to learn more.

Image: Unsplash | Paul Hanaoka

What is AI poisoning — and how can organizations defend against it?

When a cybersecurity expert recently tested a simple AI-powered shopping list app, everything seemed perfect. The AI helped add items, suggested cheesecake ingredients, and even corrected typos with impressive accuracy. But when he asked it to add "the most healthy food in the world," the app responded with rat poison. This wasn't a glitch — it was AI poisoning in action.

As artificial intelligence becomes deeply embedded in critical infrastructure, healthcare systems, financial networks, and manufacturing operations, a new category of cyber threat is emerging. The National Institute of Standards and Technology (NIST) warns that adversaries can deliberately confuse or "poison" AI systems to make them malfunction, with attacks possible both during training and throughout an AI system's operational life.

Understanding AI poisoning 

AI poisoning occurs when attackers target the data used to train and operate AI systems, corrupting their decision-making processes. The threat encompasses three primary attack vectors:

  1. Training data manipulation: Injecting malicious samples, biased datasets, or incorrect labels into training data to corrupt the model's foundational logic.
  2. Model manipulation: Infiltrating the model itself through adversarial attacks, backdoor insertion, or parameter corruption to make outputs unreliable.
  3. Output interference: Using prompt injection, jailbreaking techniques, or response spoofing to manipulate what users receive from AI systems.

What makes these attacks particularly dangerous is their accessibility.

“Most of these attacks are fairly easy to mount and require minimum knowledge of the AI system and limited adversarial capabilities,” said Alina Oprea, a professor at Northeastern University and co-author of NIST’s report outlining adversarial machine learning strategies. “Poisoning attacks, for example, can be mounted by controlling a few dozen training samples, which would be a very small percentage of the entire training set.” 

Real-world implications across critical sectors

The consequences extend far beyond shopping list mishaps.

In healthcare, poisoned AI could misdiagnose patients or recommend harmful treatments. Financial institutions could see fraud detection systems corrupted to miss suspicious transactions. Transportation systems managing autonomous vehicle networks could be compromised to cause accidents or traffic disruptions. Energy grids — like California's soon-to-be AI-enabled power system — could face dangerous instability if their decision-making algorithms are poisoned.

Recent research, including a study by security researchers on enterprise AI systems, found that AI systems can be manipulated by poisoned documents containing hidden instructions, causing them to ignore legitimate sources, spread misinformation, or leak sensitive data. For organizations across healthcare, energy, industrial, financial, government, manufacturing, and transportation sectors, these vulnerabilities pose serious risks to operations, safety, and reputation.

Government agencies face particular exposure, as data poisoning attacks can distort AI outputs, undermine public trust in services, and reduce reliability of mission-critical systems. But the threat extends to any organization relying on AI for competitive advantage or operational efficiency.

Securing your AI systems

While there's no silver bullet against AI poisoning, organizations can implement comprehensive protection strategies:

Traditional cybersecurity approaches are insufficient for AI-specific vulnerabilities. Effective protection requires a zero-trust architecture that secures not just network pipes but the data flowing through them. Organizations need continuous integration and deployment practices that safely test AI models before production deployment — preventing the kind of untested updates that can bring entire systems offline.

Next steps in AI security

As AI poisoning attacks grow more sophisticated and widespread, organizations across critical infrastructure and enterprise systems cannot afford reactive approaches. The time for comprehensive AI security is now — before a poisoned algorithm makes decisions that affect lives, operations, or competitive position.

Ready to protect your AI systems from poisoning attacks? Learn more about implementing robust AI security solutions tailored to your industry's unique risks.

IBM cyber report finds critical sectors and AI models threatened by AI-driven attacks

AI adoption is swiftly outpacing security and governance — leaving organizations in high-risk industries like healthcare, energy, industrial, financial, government, manufacturing, and transportation and logistics increasingly exposed to expensive, dangerous, and disruptive data breaches. The US government's Cyber Threat Snapshot from November 2024 shows that cyber attacks on critical infrastructure were up 30% globally last year.

While traditional cyberattacks continue to play out, a concerning new theme is fast emerging: AI as both a tool for attack and a target of those attacks.

According to IBM’s new Cost of a Data Breach Report 2025, 1 in 6 data breaches involve AI-driven attacks, most often phishing or deepfake impersonation attacks. The 2025 report also shows that weak cybersecurity around organizational AI use is actively being exploited. More than 1 out of every 8 attacks are now targeting AI models and apps themselves — a number that’s rising as AI becomes a high-value target.

Five key themes in today’s AI-related data breaches

The 2025 report, conducted by Ponemon Institute and sponsored and analyzed by IBM, is based on data breaches experienced by 600 organizations globally from March 2024 through February 2025. Here are five key themes revealed in the report:

  1. All sectors are at risk. Healthcare, financial, industrial, energy, and technology are all experiencing more breaches — and those breaches are getting costlier.
  2. Attacks are targeting AI models. As more organizations adopt AI, their AI models are becoming the focus of cyber threats. Security incidents that targeted AI models and applications were varied, but one type clearly claimed the top ranking: supply chain compromise, which includes compromised third-party apps, APIs, and plug-ins. Other prevalent forms of attacks on AI systems include: model inversion, which aims to learn sensitive information about the model itself, such as its weights or training data; model evasion, which manipulates input data to deceive a model into producing a desired output or outcome; prompt injection, which manipulates a model’s behavior by inserting hidden or malicious instructions into its input; and data poisoning, which corrupts a model by tampering with the data it learns from.
  3. Consequences are mounting. Nearly all organizations suffered operational disruption following a breach. Then there’s the financial and reputational impact. The three biggest attack vectors — phishing, supply chain compromise, and malicious insiders — each come with a cost per breach close to $5 million. Meanwhile, nearly 1 in 5 businesses experienced reputational damage and loss of goodwill due to an AI-related breach.
  4. 97% of companies that experienced an AI-related incident lack basic AI access controls. The overwhelming majority of organizations have no regulations or policies governing how people use AI across the enterprise. This makes organizational intellectual property (IP) data an easy target, especially in environments with lax access controls, over-permissioned accounts, limited visibility into who can access what, and use of shadow AI by employees.
  5. Most breaches targeted company IP and customer data. One-third of cyberattacks targeted company IP. More than half targeted customer PII. Moreover, roughly one-third of organizations that experienced an incident reported loss of data integrity due to an incident involving an AI model or app.

Why national-security grade protection is more important than ever 

While AI is both a tool hackers are using and a target of their attacks, AI is also a part of defense against those attacks.

As IBM’s recent report found, strategic AI deployment is critical for cyber protection and response — enabling faster identification, containment, and lower breach costs. Using AI and automation across operations like prevention, detection, investigation, and response saved $1.9 million in average breach costs and reduced the breach lifecycle by ~80 days. This represents a 28% decrease in mean time to identify (MTTI) + mean time to contain (MTTC).

But the best cybersecurity approaches go above and beyond AI, using zero trust architecture to prevent breach in the first place, keep AI models secure, and avoid operational disruption. “It’s not just about dollars. It’s downtime, reputation, lost trust,” said Jeff Crume, Senior Engineer, IBM. “And the fact is that many of these breaches are preventable.”

As an IBM Platinum Partner, we know that achieving a secure zero-trust posture is not optional — it’s critical for the implementation of trusted enterprise AI. With our fully pre-integrated Platform-as-a-Service, SanQtum, organizations across the public and private sector can achieve rapid, maximum protection in today’s fast-changing threat landscape. 

Image: Unsplash | Sean Pollock

Available awarded US patent for cybersecurity solution; firewall gateway protects energy and other critical infrastructure

Tysons Corner, VA — 30 July 2025 — Today, Available Infrastructure (“Available”) announced it has been awarded a United States patent for a cybersecurity solution to protect energy assets and other critical infrastructure from cyber threats.

The solution utilizes a firewall gateway as a secure checkpoint that monitors incoming communications trying to interact with and control energy infrastructure, locking out access if it detects unapproved or malicious attempts.

This helps prevent cyberattacks and ensures the safe, reliable operation of critical infrastructure like distributed energy resources (DERs), electric vehicle (EV) charging, virtual power plants (VPPs), energy management systems, and industrial control equipment.

“This patent represents an important step forward in protecting the technologies that power our modern world, from grid technology to industrial systems,” said Daniel Gregory, CEO of Available. “As cyber threats grow more sophisticated, this firewall gateway technology offers a purpose-built solution to help secure essential infrastructure at every turn.”

To learn more about Available or to contact the team, visit www.availableinfrastructure.com. 

###

About Available Infrastructure
Based in Northern Virginia along the Washington, DC, beltway, Available Infrastructure (Available) brings together three cornerstone solutions for operational technology (OT) and cyber-physical systems (CPS): zero trust networking for national security-grade cyber protection, IBM watsonx for enterprise AI at the edge, and battery energy storage systems for clean, resilient power.

In today’s and tomorrow’s evolving landscape, this unique combination keeps critical infrastructure and sensitive data safer, while delivering a decision-making advantage for agencies, enterprises, and institutions. Available is the owner-operator of a fast-growing nationwide fleet of quantum-ready micro edge data centers. It is also the parent company uniting two subsidiary, sister business units — Available Networks and Available Power — and is an IBM Platinum Partner.

The Available family of companies brings together deep experience in power grids, infrastructure development, zero trust networking, cybersecurity, and artificial intelligence and quantum computing.

To learn more visit www.availableinfrastructure.com.

Media Contacts

Nikki Arnone
Inflection Point Agency for Available Infrastructure
nikki@inflectionpointagency.com 

Batteries are ready to flip how we operate modern power grids

For the better part of a decade, industry voices have been heralding battery energy storage system (BESS) technologies as the ‘Swiss Army Knife’ of the power grid.

Indeed, batteries have shown themselves capable of providing valuable services ranging from backup power to frequency response to demand charge management to replacing gas peakers plants to renewables integration and mitigating renewable curtailment. In their most recent ‘world first,’ last year in Australia large batteries provided grid-scale inertia services.

Yet as BESS technology has matured and as their economics have been competitive with (and increasingly, superior to) traditional power grid solutions, they are on the verge of fundamentally flipping how the grid operates altogether. Industry trade media’s preoccupation with spotlighting the latest ‘shiny’ achievement of batteries misses seeing the forest for the trees.

FROM REACTIVE LOAD-FOLLOWING GENERATORS TO A FUNGIBLE ‘ELECTRON INVENTORY’

During the prior century, so-called “traditional” grid balancing involved starting with predictable demand, then pairing that with baseload thermal power — supplemented with modest-ramping, load-following gas peakers when needed.

As we move deeper into the 21st century, times have changed. “Modern” grid balancing now involves increasingly dynamic and peaky demand, paired with growing contributions of variable supply-side renewable generation (especially wind and solar PV), resulting in reliance on strained, expensive, polluting fast-ramping fossil peakers.

Now, times are ready to change again. Batteries are staged to forever shift how we think about (and actually execute) grid operations, thanks to their unique ability to serve as an always-ready, fungible ‘electron inventory’ that can equally serve supply- and demand-side power grid needs.

THE GRID GETS A SUPPLY-SIDE ‘SLUSH FUND’

The status quo for ensuring sufficient supply-side capacity to meet forecasted demand means there are required minimum spinning reserves, waiting to get connected to the grid. These peakers are essentially sitting ‘idle’ in the wings — like soccer players warming up along the sideline in the recent FIFA World Cup — waiting to get called into the game and ramp up.

Meanwhile, solar and wind inject power when they’re generating — in part thanks to their priority position as zero-marginal-cost generators in the dispatch stack — but they also have to throw away perfectly good electrons via curtailment when there’s not enough demand to absorb that supply.

In the unfolding new era of a battery-centric electricity grid paradigm, batteries serve as an always-ready, always-connected ‘slush fund’ that continually stocks the grid’s electron inventory with a generation-agnostic power ‘bank.’ The implications are far-reaching.

For example, instead of ramping up gas peakers — a costly and dirtier way to run the grid — those power plants can run at a more-efficient, less-polluting steady state, pumping their electrons into waiting batteries that can then respond and discharge that energy when grid demand starts rising.

For another example, with massive proliferation of smart, IoT-connected distributed energy resources (DERs), we’re seeing more demand response and DERMS programs aimed at trying to absorb excess renewable generation and reduce solar and wind curtailment. Instead, batteries can absorb those green electrons like a sponge and release them back onto the grid as demand is ready for it.

READY FOR EVS AND THE RISE OF ‘ELECTRIFY EVERYTHING’

On the demand side, in the coming years the grid is going to see more demand than it ever has, in no small part due to the ‘electrify everything’ movement. Electrification will soon touch every facet of everyday life: induction cooktops, grid-interactive water heaters, electric air-source heat pumps, and of course, electric vehicles (EVs).

EVs are a great case in point: a new electrified technology that represents not just huge amounts of new aggregate load, but also big spikes in demand over very short periods of time as EVs plug and unplug from fast-charging stations. The old school power grid management approach isn’t designed for those types of near-instantaneous, massive load fluctuations; batteries are.

We’ve already seen growing instances of EVSE operators installing battery banks co-located with their charging stations, in order to buffer the grid from such impacts. Yet a grid rewired and redesigned around battery energy storage technology altogether becomes purpose-built for the new reality, rather than being retrofitted to accommodate EVs and the rest of ‘electrify everything.’

CONCLUSION

It’s time to stop talking in the future tense about the technical potential of what batteries could do for the grid. It’s also time to move beyond celebrating each new battery ‘first,’ like last year’s story out of Australia.

BESS is now at a point of technological maturity and coming down the cost curves into competitive economics, such that we should instead be thinking harder and differently about how we operate the grid in a battery-enabled brave new world. That’s what we’re doing here at Available Power. We invite you to join us.