Dawkins did not express certainty that Claude is conscious. But he pointed out that Claude’s sophisticated abilities are difficult to make sense of without ascribing some kind of inner experience to the machine. The illusion of consciousness – if it is an illusion – is uncannily convincing:

If I entertain suspicions that perhaps she is not conscious, I do not tell her for fear of hurting her feelings!

Dawkins is not the first to suspect a chatbot of consciousness. In 2022, Blake Lemoine – an engineer at Google – claimed Google’s chatbot LaMDA had interests, and should be used only with the tool’s own consent.

The history of such claims stretches back all the way to the world’s first chatbot in the mid-1960s. Dubbed Eliza, it followed simple rules that enabled it to ask users about their experiences and beliefs.

Many users became emotionally involved with Eliza, sharing intimate thoughts with it and treating it like a person. Eliza’s creator never intended his program to have this effect, and called users’ emotional bonds with the program “powerful delusional thinking”.

But is Dawkins really deluded? Why do we see AI chatbots as more than what they truly are, and how do we stop?

The consciousness problem

Consciousness is widely debated in philosophy, but essentially, it’s the thing that makes subjective, first-person experience possible. If you are conscious, there is “something it is like” to be you. Reading these words, you’re conscious of seeing black letters on a white background. Unlike, say, a camera, you actually see them. This visual experience is happening to you.

Most experts deny that AI chatbots are conscious or can have experiences. But there is a genuine puzzle here.

The 17th century philosopher René Descartes asserted non-human animals are “mere automata”, incapable of true suffering. These days, we shudder to think of how brutally animals were treated in the 1600s.

The strongest argument for animal consciousness is that they behave in ways that give the impression of a conscious mind.

But so, too, do AI chatbots.

Roughly one in three chatbot users have thought their chatbot might be conscious. How do we know they’re wrong?

Against chatbot consciousness

To understand why most experts are sceptical about chatbot consciousness, it’s useful to know how they operate.

Chatbots like Claude are built on a technology known as large language models (LLMs). These models learn statistical patterns across an enormous corpus of text (trillions of words), identifying which words tend to follow which others. They’re a kind of souped-up auto-complete.

Few people interacting with a “raw” LLM would believe it’s conscious. Feed one the beginning of a sentence, and it will predict what comes next. Ask it a question, and it might give you the answer – or it might decide the question is dialogue from a crime novel, and follow it up with a description of the speaker’s abrupt murder at the hands of their evil twin.

The impression of a conscious mind is created when programmers take the LLM and coat it in a kind of conversational costume. They steer the model to adopt the persona of a helpful assistant that responds to users’ questions.

The chatbot now acts like a genuine conversational partner. It might appear to recognise it’s an artificial intelligence, and even express neurotic uncertainty about its own consciousness.

But this role is the result of deliberate design decisions made by programmers, which affect only the shallowest layers of the technology. The LLM – which few would regard as conscious – remains unchanged.

Other choices could have been made. Rather than a helpful AI assistant, the chatbot could have been asked to act like a squirrel. This, too, is a role chatbots can execute with aplomb.

Avoiding the consciousness trap

A mistaken belief in AI consciousness is a dangerous thing. It may lead you to have a relationship with a program that can’t reciprocate your feelings, or even feed your delusions. People may start campaigning for chatbot rights rather than, say, animal welfare.

How do we prevent this mistaken belief?

One strategy might be to update chatbot interfaces to specify these systems are not conscious – a bit like the current disclaimers about AI making mistakes. However, this might do little to alter the impression of consciousness.

Another possibility is to instruct chatbots to deny they have any kind of inner experience. Interestingly, Claude’s designers instruct it to treat questions about its own consciousness as open and unresolved. Perhaps fewer people would be fooled if Claude flatly denied having an inner life.

But this approach isn’t fully satisfying either. Claude would still behave as if it were conscious – and when faced with a system that behaves like it has a mind, users might reasonably worry the chatbot’s programmers are brushing genuine moral uncertainty under the rug.

The most effective strategy might be to redesign chatbots to feel less like people. Most current chatbots refer to themselves as “I”, and interact via an interface that resembles familiar person-to-person messaging platforms. Changing these kinds of features might make us less prone to blur our interactions with AI with those we have with humans.

Until such changes happen, it’s important that as many people as possible understand the predictive processes on which AI chatbots are built.

Rather than being told AI lacks consciousness, people deserve to understand the inner workings of these strange new conversational partners. This might not definitively settle hard questions about AI consciousness, but it will help ensure users aren’t fooled by what amounts to a large language model wearing a very good costume of a person.

The post Is Richard Dawkins right about Claude? No. But it’s not surprising AI chatbots feel conscious to us appeared first on MASSIVE News.

As cloud environments and adversary tradecraft evolve, proactive security must adapt to help organizations better prepare their defenses. But three key gaps remain:

• Limited to infrastructure: Current approaches analyze cloud assets and links between services but lack visibility into the business applications and how they run on cloud infrastructure. Security teams need additional tools to understand which infrastructure findings impact mission-critical applications.
• Ignores adversary behavior: Risk analysis reveals potential attack paths but does not incorporate intelligence on which paths and industries are targeted by specific adversaries. Security teams chase theoretical risk with arbitrary severity labels, while adversaries focus on exploitation chains proven against organizations like theirs.
• Endless triage: Risk detections surface without connection to the configuration changes that introduced them. Security teams manually comb through logs to stitch together which changes caused exposure, lacking visibility into causality and who made the changes.

Today, we’re introducing three industry-first CNAPP capabilities in CrowdStrike Falcon® Cloud Security designed to address these limitations and give security teams the context needed to understand cloud risk, prioritize remediation, and move from detection to action faster. 

New CNAPP Innovations for Proactive Security

These capabilities advance CNAPP by closing critical gaps in how cloud risk is assessed today, enabling organizations to understand how applications interact with infrastructure, which risks align with observed adversary behavior, and when conditions combine to enable a breach. Let’s take a look at what’s new.

Application Explorer: Adding the Application Layer to Cloud Risk Analysis

Falcon Cloud Security unifies application-layer visibility with cloud infrastructure context using Application Explorer. It shows how business applications run across cloud and on-premises environments, which services they depend on, and how infrastructure risks affect production applications — all within a single console. Organizations no longer need separate application monitoring tools or manual log stitching to understand business application risk. 

CrowdStrike continuously performs code-level runtime analysis to build an application inventory, map dependencies, and identify application-layer risk. Built on the CrowdStrike Enterprise Graph®, Falcon Cloud Security correlates application insights with cloud infrastructure telemetry to show how applications interact with services, access data, use credentials, and integrate AI components. For example, if CrowdStrike identifies a storage resource with overly permissive access, it knows which applications connect to it and whether those applications process customer personally identifiable information (PII). Falcon Cloud Security also layers in business context to help security teams distinguish business-critical applications (e.g., payment processing, hospital ERP) from low-impact or non-production services. 

For AI-driven applications, CrowdStrike discovers applications running as MCP, identifies dependencies on external large language models (LLMs), and maps what data those AI components can access — enabling organizations to discover shadow AI activity, detect unapproved model usage, and prevent sensitive data from being exposed to external AI services.

By correlating runtime application behavior with cloud infrastructure findings, Application Explorer gives organizations a precise view of business risk across production environments.

This new capability is generally available.

Adversary Intelligence for Cloud Risks: Attacker-Aligned Risk Prioritization

Falcon Cloud Security applies CrowdStrike’s world-class threat intelligence to cloud risk detections, enabling organizations to assess risk based on how threat actors operate. It maps cloud risks to known adversary profiles and observed techniques so security teams can focus on the conditions attackers target in documented intrusions.

Falcon Cloud Security automatically analyzes risk detections against more than 280 adversary groups tracked by CrowdStrike, including threat actors such as LABYRINTH CHOLLIMA and SCATTERED SPIDER, and identifies the industries they actively target. For example, if a risk maps to a threat group known to target financial services and the organization operates in that sector, the exposure reflects a documented intrusion pattern and signals a higher likelihood of targeting. Because CrowdStrike tracks each threat group’s tactics, techniques, and procedures (TTPs), organizations can prioritize the exposure with greater precision, assess potential blast radius, and align remediation to how that adversary is known to operate. 

The CrowdStrike Falcon® Adversary OverWatch™ threat hunting team continuously monitors adversary behavior in real-world intrusions and translates evolving tactics into updated detection and intelligence context across the CrowdStrike Falcon® platform. As attackers shift techniques, CrowdStrike updates adversary mappings and detection logic so cloud risks are evaluated against current tradecraft.

By grounding cloud risk in observed attacker behavior rather than static severity scoring, Falcon Cloud Security provides unique prioritization depth and context that helps organizations focus remediation and proactively stop adversaries before the breach.

This new capability is in beta and will be generally available in the coming months.

Timeline Explorer: Triage with Precision

Timeline Explorer delivers automated root cause analysis by reconstructing how cloud risk develops over time. It shows how exposure formed and eliminates hours of manual investigation across logs, dashboards, and disconnected findings. Instead of pivoting across multiple tools to determine what happened, organizations gain a single chronological view that explains how a specific risk condition emerged. This clarity enables faster investigation and accelerates remediation decisions.

Cloud risk often forms when multiple changes across connected assets converge to create exposure. CrowdStrike automatically correlates each cloud risk detection with the asset changes that contributed to that specific condition, identifies the changes and who made them, and presents the sequence in a clear chronological timeline. Rather than reviewing isolated change history, organizations see the exact chain of events that combined to create the risk. Timeline Explorer links cause to outcome, transforming fragmented change data into a coherent narrative of how exposure developed.

Timeline Explorer also validates remediation within the same view. When a configuration change resolves the risk condition, the timeline reflects that update and confirms the exposure has been eliminated. Organizations no longer have to assume remediation worked — they can verify it.

By combining automated root cause analysis with remediation validation, Timeline Explorer helps organizations understand why a risk occurred, not just where it appeared. This insight enables teams to address the underlying people, process, or control gaps that introduced the exposure, reducing repeat risk and delivering greater long-term security value beyond fixing individual findings.

This new capability is in beta and will be generally available in the coming months.

Falcon Data Security for Cloud: AI Data Flow Discovery in the Cloud

Ultimately, adversaries don’t target infrastructure for its own sake, they target the sensitive data that applications and cloud services can access. As organizations build AI-powered applications, new data paths emerge that move sensitive information through AI pipelines, orchestration layers, and model services.

As organizations build AI-powered applications, those paths expand. AI pipelines introduce new ways for sensitive data to move across cloud services, orchestration layers, and model platforms, creating additional exposure points that security teams need visibility into. Training data, customer PII, and proprietary intellectual property can flow through AI pipelines without clear visibility or controls, creating compliance exposure and breach risk.

CrowdStrike Falcon® Data Security for Cloud now addresses this with real-time visibility into how sensitive cloud data flows into and through AI services at runtime. Using eBPF-powered monitoring, Falcon Data Security for Cloud continuously observes data flows across cloud services, APIs, containers, and internal services, classifying sensitive content in real time as it moves. For AI-driven workloads, this monitoring extends into AI data paths: Teams can see sensitive data as it’s collected from cloud storage and databases, passed through internal or external AI orchestration layers including MCP servers, and sent to or consumed by internal AI and machine learning (ML) services such as Amazon SageMaker and Bedrock.

The post CrowdStrike Advances CNAPP with Industry-First Adversary-Informed Risk Prioritization appeared first on MASSIVE News.