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April 17, 2024

Sliver C2: How Darktrace Provided a Sliver of Hope

Learn how Darktrace is tackling the challenges posed by the Sliver C2 framework and what it means for modern cybersecurity defenses.
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
Natalia Sánchez Rocafort
Cyber Security Analyst
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Apr 2024

Offensive Security Tools

As organizations globally seek to for ways to bolster their digital defenses and safeguard their networks against ever-changing cyber threats, security teams are increasingly adopting offensive security tools to simulate cyber-attacks and assess the security posture of their networks. These legitimate tools, however, can sometimes be exploited by real threat actors and used as genuine actor vectors.

What is Sliver C2?

Sliver C2 is a legitimate open-source command-and-control (C2) framework that was released in 2020 by the security organization Bishop Fox. Silver C2 was originally intended for security teams and penetration testers to perform security tests on their digital environments [1] [2] [5]. In recent years, however, the Sliver C2 framework has become a popular alternative to Cobalt Strike and Metasploit for many attackers and Advanced Persistence Threat (APT) groups who adopt this C2 framework for unsolicited and ill-intentioned activities.

The use of Sliver C2 has been observed in conjunction with various strains of Rust-based malware, such as KrustyLoader, to provide backdoors enabling lines of communication between attackers and their malicious C2 severs [6]. It is unsurprising, then, that it has also been leveraged to exploit zero-day vulnerabilities, including critical vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

In early 2024, Darktrace observed the malicious use of Sliver C2 during an investigation into post-exploitation activity on customer networks affected by the Ivanti vulnerabilities. Fortunately for affected customers, Darktrace DETECT™ was able to recognize the suspicious network-based connectivity that emerged alongside Sliver C2 usage and promptly brought it to the attention of customer security teams for remediation.

How does Silver C2 work?

Given its open-source nature, the Sliver C2 framework is extremely easy to access and download and is designed to support multiple operating systems (OS), including MacOS, Windows, and Linux [4].

Sliver C2 generates implants (aptly referred to as ‘slivers’) that operate on a client-server architecture [1]. An implant contains malicious code used to remotely control a targeted device [5]. Once a ‘sliver’ is deployed on a compromised device, a line of communication is established between the target device and the central C2 server. These connections can then be managed over Mutual TLS (mTLS), WireGuard, HTTP(S), or DNS [1] [4]. Sliver C2 has a wide-range of features, which include dynamic code generation, compile-time obfuscation, multiplayer-mode, staged and stageless payloads, procedurally generated C2 over HTTP(S) and DNS canary blue team detection [4].

Why Do Attackers Use Sliver C2?

Amidst the multitude of reasons why malicious actors opt for Sliver C2 over its counterparts, one stands out: its relative obscurity. This lack of widespread recognition means that security teams may overlook the threat, failing to actively search for it within their networks [3] [5].

Although the presence of Sliver C2 activity could be representative of authorized and expected penetration testing behavior, it could also be indicative of a threat actor attempting to communicate with its malicious infrastructure, so it is crucial for organizations and their security teams to identify such activity at the earliest possible stage.

Darktrace’s Coverage of Sliver C2 Activity

Darktrace’s anomaly-based approach to threat detection means that it does not explicitly attempt to attribute or distinguish between specific C2 infrastructures. Despite this, Darktrace was able to connect Sliver C2 usage to phases of an ongoing attack chain related to the exploitation of zero-day vulnerabilities in Ivanti Connect Secure VPN appliances in January 2024.

Around the time that the zero-day Ivanti vulnerabilities were disclosed, Darktrace detected an internal server on one customer network deviating from its expected pattern of activity. The device was observed making regular connections to endpoints associated with Pulse Secure Cloud Licensing, indicating it was an Ivanti server. It was observed connecting to a string of anomalous hostnames, including ‘cmjk3d071amc01fu9e10ae5rt9jaatj6b.oast[.]live’ and ‘cmjft14b13vpn5vf9i90xdu6akt5k3pnx.oast[.]pro’, via HTTP using the user agent ‘curl/7.19.7 (i686-redhat-linux-gnu) libcurl/7.63.0 OpenSSL/1.0.2n zlib/1.2.7’.

Darktrace further identified that the URI requested during these connections was ‘/’ and the top-level domains (TLDs) of the endpoints in question were known Out-of-band Application Security Testing (OAST) server provider domains, namely ‘oast[.]live’ and ‘oast[.]pro’. OAST is a testing method that is used to verify the security posture of an application by testing it for vulnerabilities from outside of the network [7]. This activity triggered the DETECT model ‘Compromise / Possible Tunnelling to Bin Services’, which breaches when a device is observed sending DNS requests for, or connecting to, ‘request bin’ services. Malicious actors often abuse such services to tunnel data via DNS or HTTP requests. In this specific incident, only two connections were observed, and the total volume of data transferred was relatively low (2,302 bytes transferred externally). It is likely that the connections to OAST servers represented malicious actors testing whether target devices were vulnerable to the Ivanti exploits.

The device proceeded to make several SSL connections to the IP address 103.13.28[.]40, using the destination port 53, which is typically reserved for DNS requests. Darktrace recognized that this activity was unusual as the offending device had never previously been observed using port 53 for SSL connections.

Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.
Figure 1: Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.

Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.
Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.

Further investigation into the suspicious IP address revealed that it had been flagged as malicious by multiple open-source intelligence (OSINT) vendors [8]. In addition, OSINT sources also identified that the JARM fingerprint of the service running on this IP and port (00000000000000000043d43d00043de2a97eabb398317329f027c66e4c1b01) was linked to the Sliver C2 framework and the mTLS protocol it is known to use [4] [5].

An Additional Example of Darktrace’s Detection of Sliver C2

However, it was not just during the January 2024 exploitation of Ivanti services that Darktrace observed cases of Sliver C2 usages across its customer base.  In March 2023, for example, Darktrace detected devices on multiple customer accounts making beaconing connections to malicious endpoints linked to Sliver C2 infrastructure, including 18.234.7[.]23 [10] [11] [12] [13].

Darktrace identified that the observed connections to this endpoint contained the unusual URI ‘/NIS-[REDACTED]’ which contained 125 characters, including numbers, lower and upper case letters, and special characters like “_”, “/”, and “-“, as well as various other URIs which suggested attempted data exfiltration:

‘/upload/api.html?c=[REDACTED] &fp=[REDACTED]’

  • ‘/samples.html?mx=[REDACTED] &s=[REDACTED]’
  • ‘/actions/samples.html?l=[REDACTED] &tc=[REDACTED]’
  • ‘/api.html?gf=[REDACTED] &x=[REDACTED]’
  • ‘/samples.html?c=[REDACTED] &zo=[REDACTED]’

This anomalous external connectivity was carried out through multiple destination ports, including the key ports 443 and 8888.

Darktrace additionally observed devices on affected customer networks performing TLS beaconing to the IP address 44.202.135[.]229 with the JA3 hash 19e29534fd49dd27d09234e639c4057e. According to OSINT sources, this JA3 hash is associated with the Golang TLS cipher suites in which the Sliver framework is developed [14].

Conclusion

Despite its relative novelty in the threat landscape and its lesser-known status compared to other C2 frameworks, Darktrace has demonstrated its ability effectively detect malicious use of Sliver C2 across numerous customer environments. This included instances where attackers exploited vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

While human security teams may lack awareness of this framework, and traditional rules and signatured-based security tools might not be fully equipped and updated to detect Sliver C2 activity, Darktrace’s Self Learning AI understands its customer networks, users, and devices. As such, Darktrace is adept at identifying subtle deviations in device behavior that could indicate network compromise, including connections to new or unusual external locations, regardless of whether attackers use established or novel C2 frameworks, providing organizations with a sliver of hope in an ever-evolving threat landscape.

Credit to Natalia Sánchez Rocafort, Cyber Security Analyst, Paul Jennings, Principal Analyst Consultant

Appendices

DETECT Model Coverage

  • Compromise / Repeating Connections Over 4 Days
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Server Activity / Server Activity on New Non-Standard Port
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compromise / Quick and Regular Windows HTTP Beaconing
  • Compromise / High Volume of Connections with Beacon Score
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Large Number of Suspicious Failed Connections
  • Compromise / SSL or HTTP Beacon
  • Compromise / Possible Malware HTTP Comms
  • Compromise / Possible Tunnelling to Bin Services
  • Anomalous Connection / Low and Slow Exfiltration to IP
  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Numeric File Download
  • Anomalous Connection / Powershell to Rare External
  • Anomalous Server Activity / New Internet Facing System

List of Indicators of Compromise (IoCs)

18.234.7[.]23 - Destination IP - Likely C2 Server

103.13.28[.]40 - Destination IP - Likely C2 Server

44.202.135[.]229 - Destination IP - Likely C2 Server

References

[1] https://bishopfox.com/tools/sliver

[2] https://vk9-sec.com/how-to-set-up-use-c2-sliver/

[3] https://www.scmagazine.com/brief/sliver-c2-framework-gaining-traction-among-threat-actors

[4] https://github[.]com/BishopFox/sliver

[5] https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors

[6] https://securityaffairs.com/158393/malware/ivanti-connect-secure-vpn-deliver-krustyloader.html

[7] https://www.xenonstack.com/insights/out-of-band-application-security-testing

[8] https://www.virustotal.com/gui/ip-address/103.13.28.40/detection

[9] https://threatfox.abuse.ch/browse.php?search=ioc%3A107.174.78.227

[10] https://threatfox.abuse.ch/ioc/1074576/

[11] https://threatfox.abuse.ch/ioc/1093887/

[12] https://threatfox.abuse.ch/ioc/846889/

[13] https://threatfox.abuse.ch/ioc/1093889/

[14] https://github.com/projectdiscovery/nuclei/issues/3330

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
Natalia Sánchez Rocafort
Cyber 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
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