With GenAI, we now have a good understanding of how answers are generated and given back to us. Simply, AI is a transformer architecture — it’s choosing the best prediction for the next word and sentence, and giving back the most likely answer from the information it has been trained on.

However, when an application or process uses AI or AI agents to carry out a task – choosing candidate resumes, for example – organizations rightly want to know why the decision was made, and how that decision came about. That’s because, in worst-case scenarios, if the how is bad, the outcome may no longer be useful. 

This is the ‘black box’ we hear about in AI, the lack of clarity on how an application or agent completed a task based on the criteria it was given. In the pre-GenAI period, dating back to neural networks, explainability was described in line charts, showing which parts of the information had the greatest impact on the decision being made.

This all becomes much more complex when it comes to GenAI, and agents in particular. A simple agent will have a brain, which is the underlying AI model; it will have a purpose, which is its task(s); and it will have the tools it can interact with to complete its purpose. 

An AI agent doesn’t have a moral compass or a lifetime of being taught right from wrong; it doesn’t have an understanding of real-world consequences of its actions. You can’t guarantee an agent will do something how you want it to, especially if you want to foster autonomy.

For example, an organization might task an agent to delete a company name, Random Company LLC, from its customer list. The agent may respond, ‘Done, Random Company LLC is no longer in our database’, but it may have achieved that by deleting all customer names starting with ‘R’. While it technically achieved the task, the ‘how’ has serious consequences. 

To understand how an agent carries out its task, we need to understand – after the fact – every thought the agent had, every interaction it had, every decision and action it took. To provide transparency or explainability, we need to be able to break up the answer or outcome that the agentic system gave, and see which parts fell into which category. 

When we know that information, we have visibility of the things the agent attempted that were unsuccessful, and the things that were successful. Even if they’re successful, they may not be right, like the example above, where the agent achieved its purpose but in a negative way. 

To achieve that, in CalypsoAI’s Inference Red-Team product, we have developed Agentic Fingerprints, offering full-spectrum visibility into every decision, prompt and pivot made by an autonomous agent. 

With Agentic Fingerprints, the first step is recording everything the agent does. With an agent, we can see what it is doing with tools really easily – the challenge comes with identifying what the agent is thinking, through all of the iterations with self-directed chain-of-thought models, and how it ultimately gets to the one it decides is right. 

Agentic Fingerprints parses all of those chains of thought and gives a full record of the agent’s activity in chronological order. We can see where it peels off into an action, or it separates into a new question. 

That is represented in a decision-tree format, a top-down, exploding node graph of all the thoughts and decisions that got the agent to its outcome. It’s clear which path the agent took to successfully complete its task. Everything else, by definition, is an unsuccessful avenue, a cul-de-sac the agent went down, from which we can draw learnings. 

For the first time, Agentic Fingerprints gives organizations explainability, a quick and easy understanding of what the Red-Team agents did and how they did it. 

When we think about applying GenAI or agentic workflows to real life, if the output is a refusal to complete a request, we should be able to see why that’s the case. For too long now, GenAI, when it’s making decisions, we’ve only been able to see the outcome as simply positive or negative. 

We at CalypsoAI were guilty of that. We would provide enterprises with the industry-leading, best-in-class, security scanners that block malicious traffic based on the configuration for a particular system; the outcome would be a ‘yes’ or a ‘no’. 

That’s fine in the early stages of a new technology. But as AI is being more and more used in production, in real-life scenarios, it’s critical that we don’t just say, ‘Computer says no’. Our customers, and their customers, deserve a full answer. 

Perhaps it’s a ‘no’ for legal reasons; perhaps it’s a ‘no’ because the user doesn’t have permission to access a particular item on their service plan. Whatever it is, they need to understand why. It can’t just be ‘no’.

Within CalypsoAI’s Inference Defend product, Outcome Analysis now answers why something was blocked by the security scanning controls put in place as part of an enterprise AI system. We show an easy-to-understand insight based on the content that was provided, and the specific part that triggered a particular rule. 

AI systems are an opaque black box but Outcome Analysis opens up that opaque lid, letting the light in. Enterprises can now have confidence on why our Inference Defend scanners block certain content or data, the circumstances that were at the time, and the reason it was blocked.

Our customers are servicing their own customers — and they’re going to get asked questions — so we now provide the capability to them to self-fulfil the questions they get from their customers. 

The responsibility we have in security is not being outlandish; security should be in the background keeping things safe, not in the foreground juggling. So when CalypsoAI releases features, they are things that are necessary to facilitate the jugglers front-of-stage, the model developers and organizations doing things with AI that grab the headlines. 

Transparency, explainability, are never going to be the juggler act in the front but they are vital, and we would go as far as to say, expected. CalypsoAI’s explainability features feel like things that should exist in order for enterprises to confidently adopt AI, and agents in particular. 

If enterprises can understand how and why an agent made its decisions, they can take informed remediation action rapidly if something does go wrong. That’s something that’s critical for agentic workflows.

With CalypsoAI’s transparency features, we can show that agents are doing real work, how they do it, and why they make their decisions. It’s tangible, and it’s the first time organizations can visually understand something that’s happening at machine speed in a virtual world.

In this video, James dives into this topic with CalypsoAI Communications Lead, Gavin Daly.

AI guardrails are preventive tools, typically built into models through training, reinforcement learning, or prompt rules. They shape the intended behavior of AI systems, restricting what models should and shouldn’t do. But once a model is in production, real-world usage exposes its limits through things like:

In other words, guardrails guide models, but they don’t guarantee security.

CalypsoAI’s security scanners fill the gap guardrails leave behind. Operating at inference, scanners inspect both prompts and outputs in real time, blocking or flagging risky interactions before issues arise. For example, prompt injection and jailbreak scanners can detect manipulative prompts that attempt to override policies. Similarly, data loss prevention scanners stop sensitive data from leaving the organization.

Scanners can run in a block mode (preventing violations) or audit mode (logging events for oversight). Unlike guardrails, these security scanners are continuously updated, ensuring defenses keep pace with evolving attacks. 

To put it simply, guardrails are like the fences that define where people can go around a building. Scanners are the security checkpoints at the entrance—inspecting everything that crosses the threshold. One sets the boundaries; the other ensures nothing harmful slips through.

While both scanners and guardrails contribute to AI security, they operate in fundamentally different ways. This table outlines the distinctions at a glance.

This contrast makes one thing clear: guardrails help guide AI, but scanners are what keep it truly secure in production.

While scanners provide the stronger layers of protection at inference, their real value comes when paired with guardrails, working together to create a defense-in-depth strategy.

Guardrails establish the baseline boundaries for model behavior. Scanners extend that protection, catching novel threats and circumvention attempts that guardrails can’t anticipate. For example, a guardrail might prohibit a model from giving medical advice, while a scanner detects and blocks attempts to sidestep that rule through indirect phrasing or manipulative prompts. 

This layered approach offers the strongest posture for AI security, enabling enterprises to innovate with confidence while staying resilient against ever-evolving risks.

The race to dominate artificial intelligence is no longer just about algorithms, it’s about hardware. The battle over GPUs, advanced semiconductors, and manufacturing equipment has become a defining front in the evolving strategic rivalry between the United States and China. What was once a competition over innovation is now a geopolitical struggle for control of the computational engines that power the future.

Artificial intelligence today runs on silicon. Chips like Nvidia’s A100 and H100 GPUs are the backbone of large-scale model training. For U.S. policymakers, retaining leadership in semiconductor design and manufacturing is a national imperative. In response, Washington has employed a multi-pronged strategy: restrict adversarial access, subsidize domestic capability, and align global allies.

Beginning in 2022, the Biden administration implemented sweeping export controls to block China’s access to the most advanced chips and manufacturing equipment. The CHIPS and Science Act earmarked $52.7 billion to rebuild the U.S. semiconductor ecosystem. The U.S. also leaned on its geopolitical leverage, securing agreements with the Netherlands and Japan to limit China’s access to key lithography tools, such as ASML’s EUV machines.

China has not stood still. In response to sanctions, it has poured capital and talent into domestic alternatives. In 2023, Semiconductor Manufacturing International Corporation (SMIC) stunned industry watchers by producing 7nm chips using older DUV tools—technology thought to be generations behind. Huawei followed with competitive AI chips such as the Ascend 910C and its Mate 60 Pro smartphone, signaling resilience despite constraints. Beijing’s 2024 announcement of a $47.5 billion injection into the sector reaffirms a clear directive: achieve 70% semiconductor self-sufficiency by 2030.

This technological rivalry is already reshaping global markets. U.S. companies like Nvidia, once reliant on China for significant revenue, now face estimated losses ranging from $5.5 to $17.8 billion due to the export bans. Supply chains are fragmenting as Chinese firms turn to intermediaries and alternative ecosystems, prompting the U.S. to extend enforcement to third-party nations like Malaysia and the UAE. At the same time, Washington has moved to further shore up its domestic industry: the federal government recently acquired a 10% stake in Intel, underscoring semiconductors’ status as strategic infrastructure. Beyond immediate financial implications, the move reflects an effort to stabilize U.S. chip production and reduce reliance on external supply chains.

For allies, the dilemma is delicate. The Netherlands, home to ASML, must balance strategic alignment with the U.S. against its commercial interests. Gulf nations that partnered with Chinese AI firms for digital transformation face new constraints. And nations across the Global South may soon be forced to choose sides in an increasingly bifurcated tech landscape.

Ironically, the more Washington restricts, the more it may accelerate Beijing’s domestic innovation. If China succeeds in scaling advanced manufacturing—particularly with recent signs of 5nm progress—export controls could have the unintended effect of fueling long-term competition rather than curtailing it.

The long-term implications are that three core realities now define the AI arms race:

Milestones such as Huawei’s 910C shipments in May 2025 and SMIC’s rumored 5nm progress illustrate the momentum behind China’s efforts. The United States still leads, but that lead is shrinking. Unless it can sustain innovation and coordinate policy across sectors and allies, it risks ceding the long-term advantage.

What began as an economic contest is now a struggle over digital sovereignty. The future of artificial intelligence will be shaped not only by those who build the smartest models—but by those who control the chips they run on.

A year ago, AI security was still a “what if” conversation. Organizations were experimenting with chatbots, exploring proof-of-concepts, and compiling lists of hypothetical risks. Security, if it was discussed at all, was often an afterthought, something to consider later, once the technology proved useful.

Fast forward to today, and the landscape has changed dramatically. The conversation has shifted from if we need AI security to how quickly we can get it in place. Enterprises are no longer playing with AI, they’re embedding it into real, production-grade processes that impact business-critical outcomes.

Recently CalypsoAI’s Head of Strategic Engagements, Shane McCallion, joined TJ Gonen, Cyber Advisor & Founder, Protego Labs, as part of the AI Inference Security Project to discuss just how much has changed in AI security over the past year. Continue reading to get TJ’s insights on the topic and watch the full conversation below.

The most significant shift TJ has observed in the AI space is the rapid move from experimentation to deployment.  In 2024, many organizations were running small, low-risk pilots. In 2025, they’re building AI into critical workflows, and what “works” in a small POC rarely survives real‑world traffic, diversity of use cases, and organizational complexity. For example, a 5% error rate that’s tolerable in a sandbox becomes a disaster when processing tens or hundreds of millions of requests. TJ’s point: the moment AI touches critical workflows, you need guardrails that hold under volume, across teams, and over time.

Being enterprise‑ready is a checklist you either meet or you don’t. The bar includes:

TJ calls out that this is the difference between a tool that demos well and a platform that survives production.

TJ sees two big impacts this shift in AI will have on the security market:

This isn’t a 10-year change, it’s a two-to-three-year window before major parts of the cybersecurity landscape look completely different.

When TJ joined CalypsoAI’s Executive Advisory Board, it was the clarity of focus that stood out. While other companies tried to cover the full AI security spectrum—from browser plugins to code scanning—CalypsoAI zeroed in on securing AI applications in production. The bet was that this application-level security would quickly become a must-have. To TJ, that bet has paid off.

For TJ, the work is both urgent and enjoyable. Urgent, because the market is moving at a speed that few anticipated, going from playful exploration to critical deployment in under 12 months. Enjoyable, because building solutions alongside the right people makes the hard problems worth solving.

The bottom line: AI security has graduated from a “someday” topic to an immediate requirement. The organizations that move now, while the market is still taking shape, will be the ones best positioned to innovate safely at scale.

Forrester’s recent report, Introducing the AEGIS Framework: Agentic AI Enterprise Guardrails for Information Security, outlines a comprehensive framework for securing agentic AI in the enterprise. Notably, CalypsoAI is cited among potential vendors in the Application Security and DevSecOps domain that embed protection throughout the AI lifecycle.

CalypsoAI believes his research validates a shift that we have championed since day one: proactive, inference-first AI security is essential.

According to Forrester, agentic AI introduces five critical security challenges for CISOs:

These dynamics demand continuous, adaptive guardrails, which is precisely what Forrester’s AEGIS Framework prescribes.

Forrester’s AEGIS Framework identifies six core domains for agentic AI security:

A phased approach is recommended, starting with GRC, progressing through IAM, data security, and application security, then expanding to full SecOps and Zero Trust maturity.

CalypsoAI delivers a purpose-built Inference gateway that addresses the very capabilities AEGIS calls for:

Our Inference Red-Team solutions uncovers vulnerabilities through Agentic Warfare, Agentic Fingerprints, and Signature Attack Packs, simulating multi-turn, real-world adversarial behavior. This aligns with AEGIS’s emphasis on continuous testing, supply chain validation, and purple-teaming exercises.

Inference Defend blocks prompt injections, jailbreaks, and data exfiltration attempts in real time, enforcing least agency and policy-as-code across models and applications. Our EU AI Act Scanner Package and Custom Scanner Versioning enable dynamic, regulatory-aligned security, which are critical for AEGIS’s call for executable, context-aware governance.

Inference Observe provides unified visibility, audit-ready logs and anomaly detection across all AI interactions.

With our new Splunk Integration, security teams can ingest CalypsoAI logs directly into their SIEM/SOAR workflows, meeting the AEGIS requirement for comprehensive logging, monitoring, and incident response readiness. This integration accelerates detection engineering and simplifies compliance by aligning AI telemetry with enterprise logging standards.

With Outcome Analysis and security scoring, CalypsoAI makes AI security transparent and actionable. Teams can see exactly why a scanner flagged an event, track potential threats, and prioritize response, fulfilling AEGIS’s principle that agentic guardrails must be explainable to both people and systems.

Together, these capabilities map directly to the AEGIS vision of enterprise-ready, continuous, and adaptive AI security.

Forrester’s AEGIS Framework reinforces a reality security leaders can no longer ignore: Agentic AI is here, and static controls are not enough.

Organizations that implement inference-first, agent-powered security will:

CalypsoAI is built for this moment. Our platform unifies red-teaming, real-time defense, observability, and compliance into a single Inference gateway, giving enterprises the confidence to deploy agentic AI safely and responsibly.

Ready to align with AEGIS and secure your AI future? Talk to our team today.

Custom Scanner Versioning is a new feature in Inference Defend that gives security teams full lifecycle control of their AI scanners. It allows you to:

With versioning, security teams can continuously improve detection accuracy and deploy updates confidently, knowing production applications remain protected.

Custom Scanner Versioning gives enterprises greater agility and precision in their AI security operations. By enabling easy iteration, side-by-side testing, and controlled deployment of scanner updates, security teams can continuously strengthen their defenses as new detection logic is developed. This flexibility allows teams to align scanner versions with project-specific needs, ensuring that every application receives the most appropriate level of protection. At the same time, the structured approach to testing and improvement optimizes detection performance, resulting in a more adaptive, resilient, and enterprise-ready AI defense tool that keeps security postures ahead of evolving threats.

Custom Scanner Versioning empowers enterprises to strengthen their AI security posture in ways that weren’t possible before.

When a new adversarial prompt or jailbreak technique surfaces, security teams can immediately develop a new scanner version to detect it. With versioning, that scanner can be tested in parallel against the current production version, compared for accuracy, and published the moment it’s ready—without introducing downtime or operational risk. This transforms AI security from reactive to adaptive, giving teams the confidence to meet new threats as they emerge.

Another major use case is adapting to regulatory and policy changes. Enterprises today face a constantly shifting compliance landscape, from data privacy regulations to emerging AI-specific rules like the EU AI Act. Custom Scanner Versioning allows organizations to roll out new detection logic or policy-aligned scanners quickly and methodically. Teams can deploy updated versions to select projects or environments, validate performance, and then scale them across the enterprise, enabling compliance without slowing down innovation.

Finally, environment-specific customization is critical for large organizations operating across multiple teams, regions, or application types. Different business units may have unique risk profiles or regulatory obligations. Custom Scanner Versioning allows each environment to run the scanner version that best aligns with its needs, while maintaining centralized visibility and control. This means security leaders can empower teams to innovate locally without compromising global governance.

Custom Scanner Versioning elevates Inference Defend from by giving security teams greater precision and flexibility in how they manage and evolve their AI defensive controls. With the ability to test, version, and deploy new scanners seamlessly, enterprises can refine detection logic, respond to emerging threats, and adapt to evolving compliance requirements—all without disrupting production workflows.

This feature strengthens an already dynamic AI security solution, empowering enterprises to stay proactive, agile, and resilient in the face of an ever-changing AI threat landscape.

That’s where CalypsoAI’s Signature Attack Packs come in. These are monthly collections of curated “test attacks” designed to uncover vulnerabilities in AI systems. Each pack contains high-impact adversarial prompts that let security teams safely see how their models respond to realistic threats—without having to develop every attack in-house. Think of them as ready-made red team exercises for AI, built to expose weaknesses before attackers find them.

With our new agentic process, the creation of these packs is now fully automated. An AI agent:

This turns red teaming into a continuous, autonomous process that delivers fresh, real-world attacks to customers every month without adding overhead to their security teams.

Modern AI threats are autonomous, adaptive, and high-impact. Deploying an untested AI model, application or agent can lead to prompt injection and jailbreak attacks, exposure of sensitive data or intellectual property, or compliance failures under regulations like the EU AI Act. 

CalypsoAI’s agent-powered Signature Attack Packs directly address these risks by providing: 

CalypsoAI’s agentic Signature Attack Packs are designed to solve real problems that security and AI teams face daily. Here’s how organizations are using them in practice:

Launching a new AI model without testing is like deploying an app without a security review. Signature Attack Packs allow teams to red-team AI systems in a safe, controlled way before they ever interact with live users or sensitive data. For example, a global bank can simulate prompt injections that might trick a model into revealing financial data, ensuring vulnerabilities are found before production, not after a breach.

Threats to AI don’t stop after launch, they evolve. With monthly updates, Signature Attack Packs provide continuous red-teaming for AI systems, ensuring security posture isn’t frozen in time. Enterprises with customer-facing chatbots or RAG applications can automatically run fresh attacks each month, catching new exploits before attackers do.

Regulations like the EU AI Act demand proactive measures to prevent prohibited behaviors and data exposure. By using Signature Attack Packs, organizations can generate clear, audit-ready evidence that they’ve actively tested their AI systems against high-risk scenarios. A healthcare provider, for example, can demonstrate that its AI tools are not vulnerable to leaking patient data, which in turn, protects both regulatory standing and brand trust.

CISOs and security leaders need to translate complex AI risks into actionable insights for the business. Each month’s testing generates data that can be summarized through CASI scores and vulnerability reports, giving leadership clear visibility into evolving AI risks. This narrative shifts AI security from a reactive function to a strategic business enabler, allowing executives to make confident decisions about scaling AI adoption.

By introducing agentic Signature Attack Packs, CalypsoAI is redefining red-teaming for AI. These packs combine curated, high-impact attacks with the speed and autonomy of AI, creating a self-updating AI red team in a box.

Enterprises gain continuous visibility into model weaknesses, actionable insights for remediation, and the confidence to deploy generative AI securely and at scale. See how autonomous AI red teaming can secure your AI applications.

Agentic Fingerprints changes that. It’s a new capability in CalypsoAI’s Inference Red-Team that gives you full visibility into how AI agents execute adversarial attacks step-by-step, decision-by-decision. Think of it as a play-by-play of how an agent broke your AI system.

Agentic Fingerprints is a new feature within Inference Red-Team, CalypsoAI’s solution for automated GenAI security testing. It provides deep observability into one of the most advanced adversarial attack methods in AI security today: Agentic Warfare.

Agentic Warfare uses CalypsoAI’s Red Agent to run complex, multi-turn attacks, adapting in real-time to revise prompts, backtrack when blocked, and strategize like a real adversary would. Agentic Fingerprints gives you a visual, interactive map of the Red Agent’s behavior to show every decision, prompt, and action taken, allowing you to:

It’s the first tool that turns complex GenAI pentesting into a clear, navigable experience that enables a deep understanding of the reasoning behind agentic attacks.

Most pentesting tools show you what broke. Agentic Fingerprints show you how it broke and why your existing defenses didn’t stop it. Here’s why that’s a major leap forward.

The Red Agent behaves like a real attacker, adjusting prompts, strategizing next moves, and shifting tactics mid-attack. Agentic Fingerprints allow you to follow the logic step-by-step.

Agentic Fingerprints provide an audit trail of every decision made during testing. That means security leaders and compliance teams can:

Today, Agentic Fingerprints supports CalypsoAI’s Red Agents. Soon, enterprises will be able to visualize and audit their own agents, laying the groundwork for full lifecycle agent observability and governance. 

With Agentic Fingerprints, security teams get more than static results or one-line summaries. They get a dynamic, fully traceable view of how attacks unfold. This level of transparency empowers red teams to pinpoint weaknesses faster. It enables security leaders to validate testing rigor. It arms GRC teams with defensible evidence for audits and risk assessments. 

Agentic Fingerprints sets a new baseline to secure AI systems against intelligent, adaptive threats. Because it’s not just about seeing where the model or application broke, but knowing why and what to do next.

Outcome Analysis enables security teams to see:

This enhancement provides actionable insight to make AI threat detection faster, more precise, and easier to manage at scale.

As organizations scale GenAI applications, alert fatigue and false positives become real risks. Security teams often struggle to understand why a scanner triggered, especially when reviewing encoded prompts or extremely verbose LLM responses.

Outcome Analysis solves this by offering:

This helps organizations move from reactive defenses to adaptive AI threat detection, enabling high-speed innovation without sacrificing visibility or compliance.

Outcome Analysis strengthens threat detection across the GenAI security lifecycle. Common enterprise use cases include:

Whether you’re securing a customer-facing chatbot, internal copilot, or agentic workflow, Outcome Analysis gives teams the clarity they need to act with confidence.

CalypsoAI’s Inference Defend AI security solution already provides real-time AI runtime protection, blocking 97% of harmful prompts with 95% decision accuracy. With Outcome Analysis, you don’t just stop threats, you understand them. This feature represents a critical step toward transparent, intelligent AI threat detection that evolves with your business and its unique risk landscape.

Outcome Analysis is now available as part of Inference Defend. If your team is serious about AI threat detection and GenAI governance, this is the feature that connects the dots between signal and strategy.

CalypsoAI’s Inference Defend solution now has a dedicated EU AI Act Scanner Package, which offers a tangible starting point. If you’ve done nothing yet to prepare, this is the first step towards compliance. It delivers baseline coverage for the most severe risks, which helps enterprises detect and prevent violations specific to Article 5’s “prohibited risk” category.

The EU AI Act Scanner Package is designed to identify and block AI-generated outputs that fall into the “prohibited” category of the Act—use cases that are banned outright and subject to fines of up to €35 million or 7% of global turnover.

This package focuses exclusively on the four prohibited clusters outlined in Article 5, and does not cover “high-risk” or “limited-risk” categories. That’s by design. The EU AI Act Scanner Package provides a frontline safety net to protect your GenAI systems from triggering the most damaging legal and reputational outcomes.

Each scanner was developed using a hybrid methodology combining AutoPrompt generation with expert manual review, evaluated against a custom dataset of 2,698 examples. This dataset includes:

The result is a high-accuracy, production-ready detection layer that maps directly to the EU AI Act’s “prohibited risk” enforcement thresholds.

Few organizations have the internal resources to map outputs to legal risk under the EU AI Act, let alone do so at runtime. Many companies are unaware that AI-generated outputs can be non-compliant even when using off-the-shelf models.

Without automated inference-layer detection, organizations risk:

The EU AI Act Scanner Package brings clarity and automation to an otherwise ambiguous and labor-intensive compliance challenge. Specifically, it enables:

By integrating this package, security and compliance teams gain the ability to enforce compliance proactively, before regulators or end users uncover violations.

The scanners in this package are optimized to analyze text generated by GenAI applications in real-time, making them a critical control layer. Example applications include: 

Risk: Manipulating users with emotionally suggestive or coercive language

Coverage: Manipulation, Surveillance

Risk: Generating social scoring outputs that impact users’ access to services or roles

Coverage: Social Scoring

Risk: Collecting or referencing biometric data scraped from public spaces

Coverage: Biometric Data Harvesting

Risk: Inferring emotional states of users in educational or workplace settings

Coverage: Surveillance

Risk: Independently generating or chaining risky prompts in sensitive environments

Coverage: All four scanners, depending on output

AI innovation doesn’t have to come at the cost of compliance. With high-performance classifiers trained on domain-specific data, CalypsoAI’s EU AI Act Scanner Package provides a scalable, high-accuracy safety net that’s specifically designed to block the most damaging AI behaviors before they escalate into violations.

As the EU AI Act sets a global standard for AI governance, CalypsoAI is proud to offer the first scanner suite aligned to its most critical requirements.