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November 3, 2024

AI and Cybersecurity: Predictions for 2025

Discover the role of AI in shaping cybersecurity predictions for 2025 and how organizations can prepare for emerging threats.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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The Darktrace Community
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03
Nov 2024

Introduction: AI cybersecurity predictions for 2025

Each year, Darktrace's AI and cybersecurity experts reflect on the events of the past 12 months and predict the trends we expect to shape the cybersecurity landscape in the year ahead. In 2024, we predicted that the global elections, fast-moving AI innovations, and increasingly cloud-based IT environments would be key factors shaping the cyber threat landscape.

Looking ahead to 2025, we expect the total addressable market of cybercrime to expand as attackers add more tactics to their toolkits. Threat actors will continue to take advantage of the volatile geopolitical environment and cybersecurity challenges will increasingly move to new frontiers like space. When it comes to AI, we anticipate the innovation in AI agents in 2024 to pave the way for the rise of multi-agent systems in 2025, creating new challenges and opportunities for cybersecurity professionals and attackers alike.

Here are ten trends to watch for in 2025:

1. The overall Total Addressable Market (TAM) of cybercrime gets bigger

Cybercrime is a global business, and an increasingly lucrative one, scaling through the adoption of AI and cybercrime-as-a-service. Annual revenue from cybercrime is already estimated to be over $8 trillion, which we’ve found is almost 5x greater than the revenue of the Magnificent Seven stocks. There are a few key factors driving this growth.

The ongoing growth of devices and systems means that existing malware families will continue to be successful. As of October 2024, it’s estimated that more than 5.52 billion people (~67%) have access to the internet and sources estimate 18.8 billion connected devices will be online by the end of 2024. The increasing adoption of AI is poised to drive even more interconnected systems as well as new data centers and infrastructure globally.

At the same time, more sophisticated capabilities are available for low-level attackers – we’ve already seen the trickle-down economic benefits of living off the land, edge infrastructure exploitation, and identity-focused exploitation. The availability of Ransomware-as-a-Service (RaaS) and Malware-as-a-Service (MaaS) make more advanced tactics the norm. The subscription income that these groups can generate enables more adversarial innovation, so attacks are getting faster and more effective with even bigger financial ramifications.

While there has also been an increasing trend in the last year of improved cross-border law enforcement, the efficacy of these efforts remains to be seen as cybercriminal gangs are also getting more resilient and professionalized. They are building better back-up systems and infrastructure as well as more multi-national networks and supply chains.

2. Security teams need to prepare for the rise of AI agents and multi-agent systems

Throughout 2024, we’ve seen major announcements about advancements in AI agents from the likes of OpenAI, Microsoft, Salesforce, and more. In 2025, we’ll see increasing innovation in and adoption of AI agents as well as the emergence of multi-agent systems (or “agent swarms”), where groups of autonomous agents work together to tackle complex tasks.

The rise of AI agents and multi-agent systems will introduce new challenges in cybersecurity, including new attack vectors and vulnerabilities. Security teams need to think about how to protect these systems to prevent data poisoning, prompt injection, or social engineering attacks.

One benefit of multi-agent systems is that agents can autonomously communicate, collaborate, and interact. However without clear and distinct boundaries and explicit permissions, this can also pose a major data privacy risk and avenue for manipulation. These issues cannot be addressed by traditional application testing alone. We must ensure these systems are secure by design, where robust protective mechanisms and data guardrails are built into the foundations.

3. Threat actors will be the earliest adopters of AI agents and multi-agent systems

We’ve already seen how quickly threat actors have been able to adopt generative AI for tasks like email phishing and reconnaissance. The next frontier for threat actors will be AI agents and multi-agent systems that are specialized in autonomous tasks like surveillance, initial access brokering, privilege escalation, vulnerability exploitation, data summarization for smart exfiltration, and more. Because they have no concern for safe, secure, accurate, and responsible use, adversaries will adopt these systems faster than cyber defenders.

We could also start to see use cases emerge for multi-agent systems in cyber defense – with potential for early use cases in incident response, application testing, and vulnerability discovery. On the whole, security teams will be slower to adopt these systems than adversaries because of the need to put in place proper security guardrails and build trust over time.

4. There is heightened supply chain risk for Large Language Models (LLMs)

Training LLMs requires a lot of data, and many experts have warned that world is running out of quality data for that training. As a result, there will be an increasing reliance on synthetic data, which can introduce new issues of accuracy and efficacy. Moreover, data supply chain risks will be an Achilles heel for organizations, with the potential interjection of vulnerabilities through the data and machine learning providers that they rely on. Poisoning one data set could have huge trickle-down impacts across many different systems. Data security will be paramount in 2025.

5. The race to identify software vulnerabilities intensifies

The time it takes for threat actors to exploit newly published CVEs is getting shorter, giving defenders an even smaller window to apply patches and remediations. A 2024 report from Cloudflare found that threat actors quickly weaponized proof of concept exploits in attacks as quickly as 22 minutes after the exploits were made public.

At the same time, 2024 also saw the first reports from researchers across academia and the tech industry using AI for vulnerability discovery in real-world code. With threat actors getting faster at exploiting vulnerabilities, defenders will need to use AI to identify vulnerabilities in their software stack and to help identify and prioritize remediations and patches.

6. Insider threat risks will force organizations to evolve zero trust strategies

In 2025, an increasingly volatile geopolitical situation and the intensity of the AI race will make insider threats an even bigger risk for businesses, forcing organizations to expand zero-trust strategies. The traditional zero-trust model provides protection from external threats to an organization’s network by requiring continuous verification of the devices and users attempting to access critical business systems, services, and information from multiple sources. However, as we have seen in the more recent Jack Teixeira case, malicious insiders can still do significant damage to an organization within their approved and authenticated boundary.

To circumvent the remaining security gaps in a zero-trust architecture and mitigate increasing risk of insider threats, organizations will need to integrate a behavioral understanding dimension to their zero-trust approaches. The zero-trust best practice of “never trust, always verify” needs to evolve to become “never trust, always verify, and continuously monitor.”

7. Identity remains an expensive problem for businesses

2024 saw some of the biggest and costliest attacks – all because the attacker had access to compromised credentials. Essentially, they had the key to the front door. Businesses still struggle with identity and access management (IAM), and it’s getting more complex now that we’re in the middle of a massive Software-as-a-Service (SaaS) migration driven by increasing rates of AI and cloud use across businesses.

This challenge is going to be exacerbated in 2025 by a few global and business factors. First, there is an increasing push for digital identities, such as the rollout of the EU Digital Identity Framework that is underway, which could introduce additional attack vectors. As they scale, businesses are turning more and more to centralized identity and access solutions with decentralized infrastructure and relying on SaaS and application-native security.

8. Increasing vulnerabilities at the edge

During the COVID-19 pandemic, many organizations had to stand-up remote access solutions quickly – in a matter of days or weeks – without the high level of due diligence that they require to be fully secured. In 2025, we expect to see continued fall-out as these quickly spun-up solutions start to present genuine vulnerability to businesses. We’ve already seen this start to play out in 2024 with the mass-exploitation of internet-edge devices like firewalls and VPN gateway products.

By July 2024, Darktrace’s threat research team observed that the most widely exploited edge infrastructure devices were those related to Ivanti Connect Secure, JetBrains TeamCity, FortiClient Enterprise Management Server, and Palo Alto Networks PAN-OS. Across the industry, we’ve already seen many zero days and vulnerabilities exploiting these internet-connected devices, which provide inroads into the network and store/cache credentials and passwords of other users that are highly valuable for threat actors.

9. Hacking Operational Technology (OT) gets easier

Hacking OT is notoriously complex – causing damage requires an intimate knowledge of the specific systems being targeted and historically was the reserve of nation states. But as OT has become more reliant and integrated with IT systems, attackers have stumbled on ways to cause disruption without having to rely on the sophisticated attack-craft normally associated with nation-state groups. That’s why some of the most disruptive attacks of the last year have come from hacktivist and financially-motivated criminal gangs – such as the hijacking of internet-exposed Programmable Logic Controllers (PLCs) by anti-Israel hacking groups and ransomware attacks resulting in the cancellation of hospital operations.  

In 2025, we expect to see an increase in cyber-physical disruption caused by threat groups motivated by political ideology or financial gain, bringing the OT threat landscape closer in complexity and scale to that of the IT landscape. The sectors most at risk are those with a strong reliance on IoT sensors, including healthcare, transportation, and manufacturing sectors.

10. Securing space infrastructure and systems becomes a critical imperative

The global space industry is growing at an incredibly fast pace, and 2025 is on track to be another record-breaking year for spaceflight with major missions and test flights planned by NASA, ESA, CNSA as well as the expected launch of the first commercial space station from Vast and programs from Blue Origin, Amazon and more. Research from Analysis Mason suggests that 38,000 additional satellites will be built and launched by 2033 and the global space industry revenue will reach $1.7 trillion by 2032. Space has also been identified as a focus area for the incoming US administration.

In 2025, we expect to see new levels of tension emerge as private and public infrastructure increasingly intersect in space, shining a light on the lack of agreed upon cyber norms and the increasing challenge of protecting complex and remote space systems against modern cyber threats.  Historically focused on securing earth-bound networks and environments, the space industry will face challenges as post-orbit threats rise, with satellites moving up the target list.

The EU’s NIS2 Directive now recognizes the space sector as an essential entity that is subject to its most strict cybersecurity requirements. Will other jurisdictions follow suit? We expect global debates about cyber vulnerabilities in space to come to the forefront as we become more reliant on space-based technology.

Conclusion: Preparing for the future

Whatever 2025 brings, Darktrace is committed to providing robust cybersecurity leadership and solutions to enterprises around the world. Our team of subject matter experts will continue to monitor emerging threat trends, advising both our customers and our product development teams.

And for day-to-day security, our multi-layered AI cybersecurity platform can protect against all types of threats, whether they are known, unknown, entirely novel, or powered by AI. It accomplishes this by learning what is normal for your unique organization, therefore identifying unusual and suspicious behavior at machine speed, regardless of existing rules and signatures. In this way, organizations with Darktrace can be ready for any developments in the cybersecurity threat landscape that the new year may bring.

Discover more about Darktrace's predictions on the AI and cybersecurity landscape for 2025 by watching the full recorded webinar here.

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
The Darktrace Community

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May 21, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

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Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

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Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

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Mikey Anderson
Product Marketing Manager, Network Detection & Response

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May 21, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

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Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
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