Top Cybersecurity Trends in 2026: Rising Threats & Strategies

While each year brings new technologies that fuel innovation, it also introduces new complexities and security risks. In fact, TierPoint’s survey of 500 IT decision-makers found that security and privacy concerns will remain the leading IT modernization challenge through 2030.

In 2026, organizations should be on the lookout for new cybersecurity trends, including emerging threats and strategies that can help you become more proactive and operate with greater safety and confidence.

What Are the Most Important Cybersecurity Trends to Watch in 2026?

The top cybersecurity trends to watch in 2026 include the rise of new strategies, such as AI-powered detection and response and identity-centric security. New threats, including AI-driven attacks and supply chain vulnerabilities, will also advance.

1. Artificial Intelligence in Cybersecurity

Both cybercriminals and cybersecurity teams are keeping up with the latest technology advances by leveraging AI. Knowing how to use AI to effectively detect and respond to cybercrime can help organizations tackle sophisticated threats with ease.

AI-Powered Cyber Attacks

Cybercriminals are increasingly using artificial intelligence to enhance and scale existing attack techniques. In fact, 87% of security professionals report exposure to AI-enabled tactics, most commonly in phishing, fraud, and social engineering campaigns.

Today’s threat actors primarily use AI to automate reconnaissance, personalize phishing messages, rapidly generate malware variants, and produce deepfake content that increases the success rate of social engineering attacks. This allows them to operate at greater speed and scale than with traditional methods.

Researchers have demonstrated early examples of autonomous, AI-driven attack concepts, including projects such as Morris II (Cornell) and PromptLock (NYU). While these remain research prototypes rather than active threats, they offer a clear view into how automation and generative AI could be leveraged by attackers in the future. They also show why security teams will need automated detection and response mechanisms to counter these threats.

AI-Powered Detection and Response

When it comes to AI, you can fight fire with fire. Driven by staffing shortages and rising alert volumes, organizations are increasingly turning to AI and machine learning (ML) to strengthen detection and response. These technologies can analyze millions of signals per second, identify anomalous behavior earlier, and automate containment actions before damage occurs. According to the 2030 IT Blueprint report, 49% of IT decision-makers plan to adopt or invest in AI-powered cybersecurity technologies over the next five years.

The modern AI-powered security toolkit can include:

• Endpoint and extended detection and response (EDR/XDR) solutions
• Security Orchestration, Automation, and Response (SOAR) tools
• Security Information and Event Management (SIEM) platforms
• User and Entity Behavior Analytics (UEBA) software

For a more comprehensive approach, many organizations are turning to managed detection and response (MDR) services, which integrate AI-driven security tools with human expertise. With MDR, cybersecurity experts actively hunt for threats, providing round-the-clock protection with both automated and human-guided responses to security incidents.

Adversarial AI and the Weaponization of Machine Learning

While AI is helping defenders rapidly detect and respond to threats, they must also tackle new risks associated with organizational overreliance on these systems. Without proper guardrails, large language models (LLMs) can be prone to adversarial attacks. These include:

• Model poisoning: Attackers inject malicious data in the training phase, corrupting training data or creating backdoors.
• Evasion attacks: Attackers bypass security systems by modifying input data to trick ML models into misclassification.
• Model extraction: Attackers steal sensitive data, intellectual property, or the LLM model itself using API vulnerabilities.

On security teams, overreliance can also lead to the acceptance of AI hallucinations, which occur when LLMs produce confident but false statements. If analysts don’t perform due diligence, this can create a cascade of additional incorrect and unvalidated conclusions, reducing the efficacy of AI-powered security tools quickly.

Cybersecurity teams can take the lead in educating organization-wide employees about the risks of AI usage, encouraging it as an augmentation, not a replacement for human intervention. Increasingly, security leaders are also playing a role in the development of broader AI governance initiatives.

2. The Growing Cybersecurity Skills Shortage

While all organizations can benefit from additional cybersecurity expertise, this skill set can be hard to come by. There is a significant cybersecurity workforce shortage, and this gap is even wider when it comes to AI capabilities. 

Teams either need to build internal AI and cyber expertise or partner with outside experts to close the gap. ISC2 reports one-third of teams feel that they cannot adequately staff with their current resources, and 39% of organizations had hiring freezes in 2025. As a result, 39% organizations are outsourcing work or bringing in third-party IT security services to address internal skills shortages. According to McKinsey, more than 90% of AI capabilities are expected to come from third-party service providers in the coming years.

3. Zero Trust Architecture and Identity-Centric Security

Stolen credentials remain one of the most common threat vectors for attackers, making up 87% of data breaches. This has driven organizations to prioritize identity-centric security and Zero Trust principles.

While adoption varies by maturity, most organizations now treat multi-factor authentication (MFA) as a baseline control. Beyond MFA, 92% of security leaders are planning or implementing passwordless authentication initiatives.
Some organizations are also implementing biometrics and secretless logins for greater security. However, they must be aware of its unique risks. Unlike a password, biometric information cannot be reset, so if it is breached, it can pose a long-term liability. Advances in AI have also enabled the creation of synthetic fingerprints, facial replicas, and deepfakes. To mitigate these risks, biometric implementations can benefit from decentralized or on-device biometric storage, liveness detection, and multi-modal authentication.

4. The Complexity of Multicloud and Hybrid Cloud Security

As more organizations adopt multicloud and hybrid cloud architectures, implementing consistent security and governance policies becomes significantly more complex. Different cloud providers, native security tools, and operating models often lead to tool sprawl and fragmented visibility, making it difficult for security teams to maintain a unified view of risk.

Organizational silos can further compound the issue. Cloud, infrastructure, security, and application teams may each own different tools and controls, slowing response times and creating gaps that attackers can exploit. Centralized IT and security platforms help reduce these blind spots by correlating signals across environments and minimizing risks associated with misconfigurations, a common target for attackers.

With a multicloud or hybrid cloud environment, it’s also important to map out a shared responsibility model for each vendor. Doing so clarifies which layers of the stack are secured by the provider versus the customer, reducing missed vulnerabilities.

5. Supply Chain and Third-Party Security Risks

Your perimeter can also be wider than the network or identity, encompassing the software supply chain you work with. With agentic workflows and deep cloud integrations increasing, APIs have become more essential and must be protected. If one vendor, supplier, or service provider is targeted in your software supply chain, your business can also become exploited. 

To counter risks associated with external partners, organizations should create a digital bill of materials to list all third-party components and open-source libraries that are part of their environment. Understanding the attack surface can help security teams quickly respond to new exploits and vulnerabilities. 

AI solutions can also be implemented to continuously monitor third-party tools for potential security issues. If a vendor’s security policies start to degrade, the business can take action.

6. Targeted Attacks on Critical Infrastructure

With geopolitical tensions rising, nation-state actors and sophisticated advanced persistent threats (APTs) pose new risks for organizations, especially those that deal in critical infrastructure. This can include key industries like financial services, healthcare, manufacturing, energy, and government organizations – entities that provide core services, connect people, and house sensitive information. In these cases, the goal is often theft or operational disruption.

7. Data Protection, Privacy, and Regulatory Pressure

Organizations are facing increased pressure to demonstrate that reasonable security measures are in place before an incident occurs. Global lawmakers are advancing regulations focused on data sovereignty, accountability, and AI governance. Many industries are tightening requirements around where data can be stored and processed to ensure it remains within defined geographic boundaries.

In parallel, regulators such as the SEC are placing greater emphasis on timely, accurate incident disclosure and demonstrable governance. They’re pushing organizations to improve internal visibility, documentation, and coordination across legal, security, and executive teams.

US state privacy laws are also going into effect as 2026 begins. In Indiana, Kentucky, and Rhode Island, along with major amendments in Connecticut, states are including more data under the umbrella of what is sensitive data and should be protected. This includes precise geolocation information and neural data. The California Consumer Privacy Act also launched more regulations.AI governance is also in development, starting with the EU AI Act, and will likely continue to shape what is required of businesses for cybersecurity standards, as well as ensuring the models do not contain bias.

How Can Organizations Prepare for the Future of Cybersecurity?

Being reactionary won’t prepare organizations for the future of cybersecurity. Proactive security measures are becoming top priority. Building resilience will require unifying visibility, stress-testing for threats, and ensuring that IT is part of your overall business strategy.

Build Resilience and Cybersecurity by Design

It’s important for businesses to invest in the right tools, but truly protecting your environment requires a greater investment in security architecture. This should include the capability for otherwise siloed tools to talk to one another, unifying efforts instead of creating additional vulnerabilities. Organizations should also implement Zero Trust as a standard, mandating that applications, identity, and devices are continuously verified.

Prioritize Visibility Across Cloud and Identity

Noise can be a real issue for security teams that receive disconnected signals from SaaS apps, multiple clouds, and AI agents. It’s important to unify visibility to reduce blind spots with solutions like centralized MDR services. These services combine human expertise and threat intelligence feeds with AI-driven detection and response technologies, allowing teams to move faster and prioritize the most critical, urgent issues.

Test Incident Response for Modern Threats

Organizations should simulate AI-driven and cloud-based attacks in which autonomous agents exploit vulnerabilities at much faster speeds to determine whether current security measures will be able to respond in time.

Incident response plans also need to be updated to meet regulatory standards, such as the four-day SEC disclosure rule. These plans should include steps for how legal teams, communications teams, and security teams address breaches and share next steps with key stakeholders.

Align Cybersecurity with Business Strategy

Cybersecurity is a core component in digital trust that end users have with organizations. Security leaders should align measures with cloud and IT transformation priorities to ensure that future moves, such as hybrid cloud advancements, are enabled by security strategies instead of being blocked. 

Security teams should also work with business leaders to balance protections with risk tolerance and performance needs. Frameworks like Factor Analysis of Information Risk (FAIR) can help leaders transform cyber risks into dollar amounts, which can help boards understand how a $50,000 or $100,000 investment could prevent $5 million in potential losses for a company. 

The more organizations mitigate their risk, the more attractive they are to customers and clients. Security, privacy, and reliability can all be selling propositions and growth drivers for companies, offsetting some of the costs associated with implementation and management.

Prepare for Cybersecurity Trends in 2026 with a Comprehensive MDR Service

As cybersecurity threats continue to evolve in speed, scale, and sophistication, organizations can no longer rely on reactive or fragmented defenses. Preparing for 2026 requires a security approach that unifies visibility across cloud, identity, and endpoints, while combining automation with experienced human oversight.

The TierPoint Adapt Platform is designed to support this shift. By bringing together AI-powered detection, centralized monitoring, and expert-led response, Adapt Managed Detection and Response (MDR) helps security teams reduce noise, respond faster, and maintain resilience as environments and threat models grow more complex.Ready to learn more about the security, cloud, and IT trends impacting businesses over the next five years? Download TierPoint’s 2030 IT Blueprint report today.

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