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June 6, 2022

Unraveling Disinformation Tactics in Uncertain Times

Learn how Darktrace AI is combating disinformation! Learn more about the impact of disinformation and how Darktrace tackles this pressing issue.
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
Taisiia Garkava
Security Analyst
Written by
Justin Frank
Security Analyst
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06
Jun 2022

Since the beginning of the internet, we have seen a near, if not an exponential, surge of information sharing amongst users in cyberspace. Not long after, we saw how the emergence of social media ushered an access to public online platforms where other internet users worldwide could share, discuss, promote, and consume information, whether by deliberate choice or not.

These platforms, which are now wealthy in users, enabled the effectual sharing of a wide range of information and has facilitated the emergence of online communities, forums, webpages, and blogs - where everyone could create content and share it with other users leading to near infinite number of sources.

Public and private organisations have been able to leverage these platforms to communicate directly with the public, share relevant knowledge with their audiences, and expand users’ exposure to their organisation’s online presence – often by providing the users a direct link to websites and domains containing supplementary information on their organisations. However, there are some issues that organisations and users face when using such platforms.

Misinformation vs Disinformation

The ever-growing catalogue of informational sources and contributing users has introduced an old challenge with a more complex twist: distinguishing which information is truth and which is not. Two terms are used to describe inaccurate information – misinformation and disinformation.

Misinformation is “false information that is spread, regardless of whether there is intent or mislead”. For example, someone can read a compelling story on social media and share it with others without checking whether this story is, in fact, true.

During the COVID-19 pandemic, many people were rightfully concerned and anxious about their health, so they wanted to inform themselves as much as possible on the looming health risk. However, when they went looking for answers – they were overloaded with varying opinions and ‘fake facts’ that it became increasingly difficult to distinguish true facts from fiction.

Subsequently, at times a social media post - or two - that contained false information was shared by a friend, relative, or acquaintance who initially had good intentions in sharing what they had learned, but unfortunately, they were misinformed.

Disinformation instead means “deliberately misleading or biased information; manipulated narrative or facts; propaganda”, which can be interpreted as the intentional spreading of misinformation.

The main difference between misinformation and disinformation is the presence of clear intent in the latter. For example, during political conflict – or even wars – it is not uncommon for one, or both, opposing parties to broadcast news narratives to their own domestic audiences in the way that portrays them as either the righteous liberator or the unsuspecting victim.

Disinformation and Geopolitics

During turbulent times – such as (geo)political conflicts, national strife, digital revolutions, and pandemics – one can see the prevalence of massive disinformation campaigns being arranged by nation-state actors, independent threat actors and other ideologically driven actors. The likes of such campaigns are targeting businesses, governments, and individuals alike.

One of the most common channels used to spread disinformation would be social media platforms. In essence, any piece of information shared on social media can spread rapidly to all kinds of audiences across the globe. This is amplified by maliciously motivated actors’ use of “bots” to speed up the momentum of which disinformation is spread.

A bot is a “computer program that operates as an agent for a user or other program to stimulate a human activity. It is used to perform specific tasks repeatedly and autonomously. There is a plethora of these bots actively used to spread disinformation throughout the most popular social platforms including Facebook, Twitter and Instagram.

Impact of Disinformation on Organizations

When organisations are targeted by disinformation campaigns, malicious actors aim to leverage the discord and uncertainty on topics that are shrouded in controversy. Malicious actors like online scammers aim to exploit this induced discord by e.g., creating phishing emails that are more compelling to recipients – who are just trying to navigate between what is real and not real.

For example, a campaign stating that data held by a big telecommunication company was breached is used to craft emails in which scammers would prompt the recipients to check whether their personal data was also affected by this ‘breach’.

Regardless of whether this information is correct or not, the flux of news floating around the internet makes it increasingly difficult for a person to decide whether this information is accurate.

In parallel, the recipient may be experiencing feelings of anxiety and uncertainty regarding the breach – and the news about the breach – which often affects the recipients' decision to immediately react to new information on the topic. Since scammers use domains that are carefully crafted to seem legitimate to an untrained eye – e.g., domains containing near uncanny resemblance to the official organisation’s domain – it further increases the recipient’s susceptibility to trusting dubious sources. Thus, increasing the likelihood that recipients of phishing emails would be more compelled to e.g., click on a link attached to an email to verify whether their data was also leaked, or not.

The Future of Disinformation

Organisations who are already dealing with the social strains created by disinformation campaigns are now facing an additional risk: their audiences may be more susceptible to phishing campaigns in times of widespread uncertainty. To make a convincing phishing campaign, malign actors often use compromised domains, or attempt to mimic legitimate domains through a method called ‘typo squatting’.

Typo squatting is the act of registering domains with intentionally misspelled names of popular or official web presences and often filling these with untrustworthy content – to give their victims a false sense of legitimacy surrounding the source.

Once this false sense of legitimacy has been established between the attacker’s source and the victim’s susceptibility in trusting that source, it will be nearly entirely up to the victim to avoid being misled. Consequently, this means the attack surface of an organisation is growing as fast as disinformation and false domains can be created and shared to its audience.

Combatting Disinformation with Attack Surface Management

Organisations trying to protect their audiences from being misled by false domains will need get better visibility on domains associated with their brand. A brand-centric approach to discovering domains can shine light on:

  • The state of existing domains that are currently managed by your organisation – if they are being well maintained and properly secured.
  • The influx of ‘new’ domains that are attempting to impersonate your organisation’s brand.

Visibility on these types of domains and how your audience often interact with these domains enables an organisation to be more vigilant and responsive to the malign actors attempting to manipulate, hijack or impersonate your brand. Since an organisation’s brand pervades all sorts of publicly accessible assets – like domains – it has become of significant importance to include them in your organisation’s attack surface management regimen. Utilising a brand-centric approach to attack surface management will give your organisation a clearer view of your attack surface from a reputation risk perspective.

An attack surface management solution bolstered by such an approach will help your organisation’s security team to efficiently determine which domains – or other external facing digital assets – are posing a risk to your audience and reputation. It will help remove the repetitive work needed to identify these domains (and other assets), detect the risks associated with them, and help you manage any changes or actions required to protect both your audience and your organisation.

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
Taisiia Garkava
Security Analyst
Written by
Justin Frank
Security Analyst

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May 28, 2026

From Efficiency to Exposure: How AI Adoption Is Creating Unseen Vulnerabilities on the Factory Floor

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How AI agents impact the manufacturing industry

Security teams and IT personnel across the manufacturing industry are under constant pressure to protect production, maintain uptime, and safeguard critical assets but the rise of AI is bringing huge new opportunities alongside new cyber risks. Across manufacturing, AI is embedded into workflows, decision-making, and increasingly, autonomous AI agents are acting on behalf of employees and systems.  

Agentic systems are powerful because they can act independently, but that same autonomy also creates cyber and operational risk. Agents have extensive permissions and are capable of carrying out complex tasks, making decisions, and interacting with tools or external systems with little to no human intervention.

Unlike traditional AI models that perform predefined tasks, AI agents use advanced techniques to mimic human decision-making processes, dynamically adapting to new challenges, making decision and taking action based on their own judgement. They look like employees operationally but lack judgment, ethics, or fear of consequences like humans do. This means they can be easily manipulated by cybercriminals, and an AI agent embedded across an OT network creates threats that extend well beyond data exposure. For example, at BMW, AI identifies faults in welding processes as they occur. At its Spartanburg plant, AI monitors the weld of 300-400 metal studs onto every SUV frame to detect misplaced or faulty studs and correct them instantly. Corruption of BMW’s AI system could lead to catastrophic quality control errors.

Adopting agentic AI systems across manufacturing raises some concerns across security teams. New data from our State of AI Cybersecurity survey shows that 78% of manufacturing security professionals are worried about employee use of AI agents – their top concern. That’s followed by employee use of generative AI tools like CoPilot and ChatGPT, a worry for 76% of security professionals at manufacturing organizations. As these tools gain more access to business data and processes, and more autonomy within organizations, security teams, who today have minimal visibility of agent activity in their environments, increasingly have sensitive data exposure (a worry for 60%) and accidental policy and regulatory violations (59%) on their minds.

External AI-powered threats are evolving just as quickly

The same capabilities transforming manufacturing are also reshaping cyberattacks.

AI is enabling attackers to automate reconnaissance, refine targeting, and adapt in real time. What once required time and manual effort can now be executed continuously and at scale. Manufacturers are already seeing the impact. According to manufacturing security professionals we surveyed, 76% are already being impacted by AI-powered threats and 90% see AI increasing the success of social engineering attacks.

And the techniques themselves are evolving. Concerns across the manufacturing sector show growing anxiety about the range of AI-powered attack routes, most pressingly of adaptive malware that evolves in real-time – a prospect half (49%) of manufacturing security professionals we surveyed are worried by, a full 9% more than the average across industries. AI adaptive malware is followed by:

  • Automated vulnerability scanning and exploit chaining (48%) which has become even more pressing as Anthropic’s new Mythos AI Model supercharges vulnerability discovery
  • Hyper-personalized phishing campaigns (46%), which remain a mainstay in hackers’ arsenals, and AI has amplified their effectiveness by making phishing emails more convincing and harder to detect.

This is not just an increase in volume, it is a shift toward threats that evolve as they unfold - often faster than static defenses can respond.

Despite rising awareness, many manufacturers are not yet equipped to manage this shift. More than half (51%) say they are not adequately prepared for AI-driven threats, and only 37% have formal policies governing AI deployment.  

Securing AI through visibility, context, and guardrails

Addressing this challenge does not require manufacturers to slow innovation. It requires a different approach to security, one that can operate at the same speed and scale as AI. Three specific priorities are emerging for manufacturers looking to take advantage of the power of AI.

Visibility is foundational.  

Organizations need to understand where AI is being used, what it can access, and how it behaves across both IT and OT environments. Without that, risk cannot be measured or managed. It is no surprise that Darktrace’s research found that 91% of manufacturing security professionals said that they need to understand how AI makes decisions before trusting it. This is even more critical in operational settings where disruption has safety, environmental, financial, and reputational impacts.

Context is what turns visibility into action.  

In environments shaped by AI, normal behavior is constantly shifting. Detecting threats requires a behavioral approach; understanding patterns of life across the organization and identifying subtle deviations in real time – a step change in organizations’ traditional approach to security and risk management.

Guardrails ensure that agency does not become exposure  

As AI systems take on greater responsibility, organizations need clear boundaries around what they can do and when they can act independently. These controls must be embedded into systems themselves, not applied after the fact.  

Securing AI Agents Across Manufacturing IT and OT

The rise of agentic AI is transforming manufacturing - powering next-generation operations while reshaping the security landscape. This is not just an increase in threats, but a shift to autonomous systems, continuously evolving behaviors, and risks moving at machine speed. For organizations trying to grapple with the challenge of enabling AI while managing the risk, visibility, context and guardrails should be foundational.

Darktrace helps manufacturers build secure AI approaches by making those foundations possible. It provides visibility and real-time detection and response to unusual activity across IT and OT environments and allows organizations to understand AI activity from the prompts employees use and the agents they build to how those agents are behaving across the environment. For manufacturers scaling AI, this delivers a foundation for innovation without sacrificing control.

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About the author
Oakley Cox
Director of Product

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May 28, 2026

How to Evaluate AI Vendors: 5 Key categories for AI Adoption

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Understanding the AI buyers’ market

AI adoption has become a central topic of discussion in boardrooms, drawing growing interest from business leaders. Ultimately, organizations hope that an investment in AI technology will have tremendous returns. However, the process of buying an AI solution is not as straight forward as it appears on the surface.  

While business leaders may be eager to improve productivity across their operations, practitioners responsible for evaluating and selecting AI solutions may not always have the visibility or technical understanding needed to make the right decisions for their business. What is typically marketed as a holistic solution to their most critical problems is usually followed by uncertainty when AI tools are finally operationalized in real environments.

This guide is intended to support security leaders who are under growing pressure to adopt AI tools while navigating complex terminology, vendor claims, and increasingly crowded buying cycles. Ultimately, the goal is to help organizations evaluate and adopt AI in a safe, effective, and well-governed way. To support this, we’ve structured the evaluation framework across five key categories:

  1. Governance, safety, and data controls
  1. Data gathering and training
  1. Model and technique choice
  1. Performance and accuracy validation    
  1. Interpretability, adjustability, and transparency    

What buying AI looks like in cybersecurity

While investing in AI can bring immense benefits to your security team, first-time buyers of AI cybersecurity solutions may not know where to start. They will have to determine the type of tool they want, know the options available, and evaluate vendors. Research and understanding are critical to ensure purchases are worth the investment.  

With acceleration in AI adoption, accompanied by the recent boom in agentic AI and autonomous agents, CISOs must look “beneath the hood" of these tools to understand how they work, how they are governed, and to ensure the system is secure and compliant with internal policies.

Challenges in the AI buyers’ marketplace  

The AI security software market is buzzing with hype and flashy promises, which, understandably, needs to be addressed with due diligence. Potential buyers, especially in the cybersecurity space, are hesitant when it comes to allowing AI autonomous capabilities across their workflows, and a lack of vendor transparency can exacerbate those feelings.  

Reinforcing this sentiment, research from this year's Darktrace’s State of AI Cybersecurity report shows where confidence and hesitancy emerge amongst potential buyers. On the one hand, security professionals agree that they have good visibility into the logic and reasoning processes their AI solutions use. However, they lack the explainability and trust to allow AI to take independent remedial action.

  • 89% say they have good visibility into the reasoning behind the outputs generated by AI solutions
  • 92% say they need to understand how a defensive AI tool makes decisions before they can trust it
  • Only 14% say they allow AI to act independently, performing autonomous actions without human approval
  • 74% say they are limiting the autonomy of AI taking action in their SOC until explainability improves

Given the desire for trust and explainability we are seeing from buyers, it's important for them to be equipped with the right questions to ask vendors during an assessment or POV of AI tools in order to demystify marketing hype from real operational outcomes.

Below is a list of categories in which buyers can assess AI vendors or AI Service Providers (AISPs) to help reach safe adoption and maximize their ROI.  

5 categories of AI vendor assessment

Darktrace groups these AI-related questions into 5 categories: governance, data and training, model and technique choice, performance validation, and interpretability and adjustability. By asking questions regarding each of these 5 categories, buyers can gain a deeper understanding of how an AISP’s systems work and whether they suit their business requirements.

Governance, safety, and data controls

Governance of AI systems is critical for all AISPs. Whether their platform is based around a single model, or is a more complex, composite AI solution, strong governance is essential to ensure the system is safe, robust, and reliable.

A simple question you could ask is:

What AI governance policies and frameworks do you follow, and/or certifications do you currently maintain?

For more questions you can ask vendors, download the full guide here.

Darktrace is certified to the ISO/IEC 42001 standard, the world’s first AI Management System (AIMS) standard. ISO/IEC 42001 addresses the unique ethical and technical challenges AI poses by setting out a structured way to manage risks such as transparency, accuracy, and misuse. This includes a commitment to ethical AI development, and effective management and monitoring of AI systems both prior to and continually after release.

Data gathering and training

Accurate, meaningful, and unbiased data gathering is the first important step in producing any AI system. An AI model trained using inaccurate, unbalanced, or poor-quality training data will fail to perform optimally.

To alleviate concerns regarding training data quality, a question you could ask is:

What steps do you take to prevent bias in your AI models and training data?

For more questions, download the full guide here.

AISPs should be able to provide information about the steps taken, workflows followed, and auditing performed to reduce AI bias where appropriate. While it’s sometimes impossible to fully remove bias from an AI model, appropriate actions should be taken to mitigate or reduce bias where relevant.

Model and technique choice

Different AI techniques are optimal for different tasks. For example, research from Gartner suggests that relying on a single “one-size-fits-all" model can lead to data gaps, especially in highly specialized domains.

To achieve more accurate and robust AI solutions, AI leaders should move beyond using just one model or technique, embrace composite AI practices, and adopt a holistic AI system perspective.

A straightforward question you could ask is simply:

What type(s) of AI model(s) do you utilize in your solution?

For more questions, download the full guide here.

While specific detailed information about custom systems used by AISPs is likely proprietary, buyers should expect vendors to be able to provide an overview of the broad techniques used. This will allow you as a buyer to determine if the type of model is appropriate for your use case.

Performance and accuracy validation  

Testing and evaluation of performance is essential for all AI systems. Performance analysis should be performed both before release and continually after release to identify potential data or model drift.  

A question you could ask to understand an AISPs testing workflow is:

How do you audit, test, evaluate, verify, and validate your AI model outputs?

For more questions, download the full guide here.

Testing workflows will likely vary depending on the type of model – measurements relevant to one system may not always be relevant to others. Assessment of systems should also extend beyond these standard accuracy and robustness tests, and should also feature physical performance, such as latency and resource consumption.  

Interpretability, adjustability, and transparency  

AI systems are typically a black box, simply providing an output without an explanation of how that output was attained. Interpretability and transparency are critical to ensure that both SOC teams and end-users trust the outputs of a system to be accurate and meaningful.

A question you could ask is:

How do you promote a trust relationship between human analysts and AI outputs?

For more questions, download the full guide here.

In the context of cybersecurity, trust and interpretability are even more essential. This is particularly relevant for generative AI-based systems (including most AI Agents), where the risk of hallucination can reduce trust in responses.

Cybersecurity systems often need to perform autonomous actions to block incoming threats – an email filtering system may hold potentially dangerous emails; a firewall may block malicious inbound connections. If SOC teams can’t trust these systems to perform accurately, these systems may be limited or disabled, critically reducing their defensive power.

Darktrace as an AI-native cybersecurity vendor

Darktrace has been building and applying AI in cybersecurity for over a decade, developing its capabilities alongside an increasingly complex and fast‑moving threat landscape. This experience has resulted in a mature, multi-layered approach to AI, which continuously learns the normal patterns of each organization to understand behavior, interpret context, and identify meaningful deviations — without relying on predefined rules or known attack signatures. Over time, this has enabled a proven behavioral understanding that helps uncover subtle signals of risk that may otherwise be missed.

With the backing of our ISO/IEC 42001 certification, stakeholders, customers, and partners can be confident that Darktrace is responsibly, ethically, and safely developing its AI systems, and managing the use of AI in day-to-day operations in a compliant and secure manner.  

Explore the principles behind Darktrace’s responsible AI approach, informed by collaboration with global experts in academia and governments, detailing how accountability, explainability, and continuous validation are built into its cybersecurity technology.

How Darktrace secures AI systems

Darktrace now brings these capabilities to monitor and respond to risk generated from AI systems across organizations with Darktrace / SECURE AI. This solution analyzes how prompts, agents, and systems are used within the context of each organization, bringing every AI interaction into a single view. This unique approach helps teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI agent activity.

Stay up to date

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

Further Reading on AI in cybersecurity

When deciding to invest in an AI solution, it’s important to understand what this means for you and your organization. The questions presented here are only a starting point in understanding an AI solution and whether it is appropriate for your use case.  

Gain deeper knowledge on applications of AI in cybersecurity and Darktrace’s multi-layered AI in the AI Arsenal White Paper.

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