Cybersecurity in 2026: Expert Prediction

JAKE WILLIAMS FACULTY, IANS RESEARCH & VP OF R&D, HUNTER STRATEGY explain: Enterprises will continue to struggle to mitigate risks in generative AI applications. There are definitely situations where generative AI can provide great value, but rarely within the risk tolerance of enterprises. The LLMs that underpin most agents and gen-AI solutions do not create consistent output, leading to unpredictable risk. Enterprises value repeatability, yet most LLM-enabled applications are, at best, close to correct most of the time. Enterprises today struggle to address the security risks introduced by the inconsistent output of LLMs. In 2026, we’ll see more organizations roll back their adoption of AI initiatives as they realize they can’t effectively mitigate risks, particularly those that introduce regulatory exposure. In some cases, this will result in re-scoping applications and use cases to counter that regulatory risk. In other cases, it will result in entire projects being abandoned. Smart technology leaders will perform threat modeling exercises before solution development begins to identify and eliminate risks that will be unacceptable to the organization.

GEORGE GERCHOW FACULTY, IANS RESEARCH & CHIEF SECURITY OFFICER, BEDROCK DATA:

1. AI-generated data sprawl will trigger the first major breach from forgotten “data exhaust.” The tipping point for data sprawl is already here, hiding in lower-level development environments like QA sandboxes and integration systems. In 2026, we’ll see the first major breach directly attributed to AI-generated “data exhaust” that nobody inventoried: a forgotten vector database or prompt log from an abandoned pilot, left open with customer data or secrets exposed. Organizations are multiplying derivative data faster than they can track it. The solution requires treating AI exhaust as Tier 1 data with mandatory lineage tags and time-to-live (TTL) policies at write, implementing 30-60 day default retention in lower environments, restricting access with short-lived credentials and purging orphaned artifacts monthly. Visibility must be designed in from the start, or organizations will lose control entirely.

2. “Failure to red team AI” crosses the threshold into criminal negligence territory. Adversarial AI testing will become a board-level accountability issue and a standard line item in D&O insurance policies and audit requirements. “Failure to red team AI” becomes negligent when high-risk workflows lack enforced verification. Traditional phishing drills have failed; it’s time to implement real controls. Executives must publish standing “how I will contact you” policies with approved channels and verification phrases, any request for data, credentials, funds or banking changes requires out-of-band two-factor verification with a designated approver, and deepfake-resistant procedures become mandatory. Organizations must combine this with public bug bounties targeting LLM and RAG pipelines, pre-production AI red-team gates and quarterly executive reports on findings, fixes and accountable parties. The focus must shift from training people to implementing proof-based systems.

3. AI-SPM becomes the essential security platform as human biohacking enters the threat landscape. As AI agents proliferate, AI Security Posture Management (AI-SPM) emerges as the new security platform for monitoring systems, while MCP servers become the components those agents rely on that AI-SPM must inventory, test and enforce. Meanwhile, human biohacking crosses into cyber: expect executive impersonation and access fraud blending implants or wearables with deepfake voice technology. Verification protocols must replace awareness training as the critical control. Audit focus shifts entirely to evidence: insurers and regulators will demand AI change logs, adversarial test results and decision provenance. Vendor and model supply chains become primary attack surfaces, making traceability shift from optional to essential. The metrics that matter are data-first: time to inventory new AI pipelines, artifacts expired on schedule, and cost per incident defended.

DR. DARREN WILLIAMS, FOUNDER AND CEO, BLACKFOG:

1. Shadow AI will emerge as the #1 threat to organizations. The explosive growth in AI usage represents the single greatest operational threat to organizations, putting intellectual property (IP) and customer data at serious risk. While AI adoption is growing rapidly, enterprises are increasingly exposed to risks related to data security, third‑party AI tools, shadow AI usage, and governance issues. When sensitive IP or Personally Identifiable Information (PII) is entered into unsanctioned AI systems, the data may be used for model training, stored externally, or exposed in unexpected ways, leading to compliance, IP, and reputational risk. Organizations must monitor not only sanctioned AI tools but also the growing ecosystem of “micro‑AI” extensions and plugins that can quietly extract or transmit data. A global KPMG and University of Melbourne survey of 48,340 individuals across 47 countries found that 48% of employees admitted uploading company data into public AI tools, and only 47% received formal AI training, underscoring real and growing risk of unsanctioned AI use.

2. AI will turn data exfiltration into a precision weapon. Data exfiltration is now used by 96% of all publicized ransomware attacks, and this is no accident. Encryption has become easier to defeat and requires constant engineering to stay ahead, so attackers are increasingly targeting the real prize: the data itself. The rapid evolution of AI provides powerful new tools for attackers to identify and exploit specific organizations and individuals, significantly improving the precision and effectiveness of every attack. This trend will accelerate even further in 2026, as access to even more powerful AI tools expands, and organizations continue to lack adequate monitoring and protection against data exfiltration.

3. Shadow AI will become the biggest cost amplifier in data breaches. Shadow AI is a force multiplier in terms of the risks it poses for data leakage. As employees and teams adopt unsanctioned AI tools outside of IT oversight, hidden vulnerabilities multiply, turning minor data leaks into costly breaches. Studies by IBM show that breaches involving unmanaged AI tools (“Shadow AI”) are substantially more expensive, adding an average of $670,000 or more in additional costs. The financial consequences of ungoverned AI usage are real and escalating, making the prevention of latent, AI-driven leaks a top strategic priority for organizations.

4. SIEM solutions will be replaced by prevention strategies and AI. Organizations will realize that Security Information and Event Management (SIEM) solutions often create more problems than solutions. The high costs of implementation, ongoing software maintenance, and the human capital required to operate these systems have become unsustainable. In their place, AI-based solutions that prevent attacks in real-time—such as anti data exfiltration technologies—will allow organizations to get ahead of threats rather than react after it’s too late.

KARL HOLMQVIST, FOUNDER AND CEO, LASTWALL:

1. Intensifying “Steal-Now, Decrypt-Later” Threats Spur Urgent Migration to Post-Quantum Encryption. In 2026, the timeline for quantum-enabled attacks will shrink dramatically, pressuring organizations to expedite their adoption of post-quantum cryptography (PQC). Breakthroughs in quantum computing, such as recent leaps in quantum processor power and the corresponding multi-billion dollar buildouts that are underway, underscore that a cryptography-breaking machine may arrive sooner than expected. We expect a sharp increase in quantum security spending in 2026 as deadlines for PQC migration become real and the understanding of intensifying “harvest-now, decrypt-later” espionage campaigns proliferates. Governments worldwide have launched quantum-safe initiatives and set clear timelines: for example, U.S. federal agencies face mandates to inventory and replace vulnerable encryption within the decade. With the 2024 standardization (FIPS 203) clearing the path for deployment, 2026 will see organizations scrambling to start the overhaul of their cryptographic infrastructure.

2. Critical Infrastructure Under Siege as Nations Brace for Conflict. Cyberattacks on critical infrastructure will reach new levels in 2026, fueled by global conflict preparations and rivalries. Defense experts have pegged 2027 as a potential flashpoint for great-power conflict, and nations are racing to harden their grids and supply lines in advance. This urgency is well-founded: recent discoveries revealed Chinese state-backed hackers (Volt Typhoon) pre-positioning malware in U.S. power and water networks, ostensibly to disrupt these services if tensions over Taiwan or other issues explode into open conflict. In 2026, critical systems like energy grids, water treatment facilities, healthcare networks, and telecommunications will increasingly come under stealthy intrusion and attack attempts by multiple groups of nation-state actors. We will likely see more aggressive probing of infrastructure defenses and even limited-scope sabotage to test resiliency. It is also likely that we will see disruption campaigns demonstrate capabilities in coordinated bursts.

The line between cyber espionage and acts of war is blurring: a well-timed grid takedown or pipeline sabotage can now be as crippling as a physical strike. Governments and industry will respond with unprecedented measures. Expect more intensive public-private cyber defense drills, mandatory incident reporting, and fortified network monitoring on infrastructure control systems. In some cases, authorities may even seize greater control over private networks and 2026 could see several governments nationalize or restrict telecom infrastructure to secure it against foreign interference. Overall, critical infrastructure cybersecurity will shift from an IT concern to a core element of national defense strategy.

3. Broken Trust and Reckoning After the “Wild West” of AI Deployment. The unchecked “Wild West” rush to deploy AI without proper safeguards will trigger a major security and trust reckoning in 2026. Over the past year, countless AI tools and systems rolled out with minimal oversight… and the fallout is coming due. We anticipate the first high-profile security breach caused directly by an autonomous AI agent in 2026, validating warnings that poorly governed AI can create new failure modes. Attackers are already leveraging AI as a force multiplier: classic threats like phishing are being supercharged by flawless deepfake voices and personalized automation, allowing minor vulnerabilities to chain into major breaches at machine speed. In 2026, smart organizations will rein in some of their initial AI deployments with rigorous security assessments, access controls, and real-time monitoring of AI behaviors. However some will not, and the results will be devastating. Hopefully we’ll see the rise of AI governance frameworks and possibly new laws holding companies accountable for AI-induced harm. Meanwhile, the deluge of deepfake-generated disinformation and fraud will prompt a fight for digital truth. As AI blurs the line between reality and fabrication, the concept of authenticity is emerging as the new pillar of cybersecurity. Companies will start investing in verification technologies (watermarks, provenance tracking, digital signatures) to ensure that what users see and hear is genuine.

ASDRÚBAL PICHARDO, CEO, SQUALIFY:

1. Cyber insurance will enter the age of quantified risk.
The volume of cyber insurance policies will increase by a double-digit percentage, driven by growing exposure associated with AI and a risk landscape increasingly shaped by mega-losses. Insurers are recalibrating underwriting models as AI accelerates both the scale and speed of potential breaches. Enterprises that quantify cyber risk in financial terms will gain leverage in negotiations, while those relying on qualitative assessments will face higher premiums and coverage gaps. The focus is shifting from coverage to provable resilience.

2. Cyber resilience must be centralized — or it will fail.
Multinational corporations are recognizing the necessity of centralizing cyber resilience management across their subsidiaries as regulations and threats become increasingly difficult to contain within individual entities. Fragmented governance models will collapse under the weight of overlapping mandates and complex supply chains. Centralized visibility into risk posture, incident data, and financial exposure is becoming the new baseline for compliance and continuity. In 2026, resilience will be measured not by localized response plans but by an organization’s ability to unify, quantify, and govern risk across its global footprint.

GEORGE PRICHICI, VP OF PRODUCTS, OPSWAT:

1. Files Are Evolving—Security Isn’t
Security teams remain focused on productivity files such as Office documents and PDFs, in which embedded hyperlinks and encrypted content continue to pose real risks. But this focus can leave blind spots elsewhere. Today’s “files” increasingly include Python scripts and malicious npm packages – many of which slip past traditional content inspection tools. Attackers are aware of this gap and are actively exploiting it.

2. AI Is Expanding Attack Surfaces
The enterprise rush to deploy LLMs is outpacing governance. Chatbots and AI copilots now handle sensitive data with little oversight. The result? A widening attack surface that includes data leakage, brand impersonation, and adversarial probing – a mix of insider risks and external threat actor delivery that will require stronger policy and technical controls.

3. Trust Is Emerging as a Primary Vulnerability
Third-party vendors and “trusted” integrations remain soft targets. CISOs are realizing that focusing budgets solely on endpoints, identity, or edge security creates imbalance: a fortified front door, but an open side entrance. The path forward is not zero trust for everything, but smarter, consistent processes that elevate defenses across all channels, including partners, APIs, and supply chains.

Bottom Line:
Security leaders must redefine what constitutes a threat, scrutinize every data exchange, and enforce AI governance before innovation turns into exposure.