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March 7, 2025

Darktrace's Early Detection of the Latest Ivanti Exploits

In January 2025, Ivanti disclosed two critical vulnerabilities affecting their products. Darktrace detected exploitation of these vulnerabilities as early as December 2024.
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
Hugh Turnbull
Cyber Analyst
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07
Mar 2025

As reported in Darktrace’s 2024 Annual Threat Report, the exploitation of Common Vulnerabilities and Exposures (CVEs) in edge infrastructure has consistently been a significant concern across the threat landscape, with internet-facing assets remaining highly attractive to various threat actors.

Back in January 2024, the Darktrace Threat Research team investigated a surge of malicious activity from zero-day vulnerabilities such as those at the time on Ivanti Connect Secure (CS) and Ivanti Policy Secure (PS) appliances. These vulnerabilities were disclosed by Ivanti in January 2024 as CVE-2023-46805 (Authentication bypass vulnerability) and CVE-2024-21887 (Command injection vulnerability), where these two together allowed for unauthenticated, remote code execution (RCE) on vulnerable Ivanti systems.

What are the latest vulnerabilities in Ivanti products?

In early January 2025, two new vulnerabilities were disclosed in Ivanti CS and PS, as well as their Zero Trust Access (ZTA) gateway products.

  • CVE-2025-0282: A stack-based buffer overflow vulnerability. Successful exploitation could lead to unauthenticated remote code execution, allowing attackers to execute arbitrary code on the affected system [1]
  • CVE-2025-0283: When combined with CVE-2025-0282, this vulnerability could allow a local authenticated attacker to escalate privileges, gaining higher-level access on the affected system [1]

Ivanti also released a statement noting they are currently not aware of any exploitation of CVE-2025-0283 at the time of disclosure [1].

Darktrace coverage of Ivanti

The Darktrace Threat Research team investigated the new Ivanti vulnerabilities across their customer base and discovered suspicious activity on two customer networks. Indicators of Compromise (IoCs) potentially indicative of successful exploitation of CVE-2025-0282 were identified as early as December 2024, 11 days before they had been publicly disclosed by Ivanti.

Case 1: December 2024

Authentication with a Privileged Credential

Darktrace initially detected suspicious activity connected with the exploitation of CVE-2025-0282 on December 29, 2024, when a customer device was observed logging into the network via SMB using the credential “svc_negbackups”, before authenticating with the credential “svc_negba” via RDP.

This likely represented a threat actor attempting to identify vulnerabilities within the system or application and escalate their privileges from a basic user account to a more privileged one. Darktrace / NETWORK recognized that the credential “svc_negbackups” was new for this device and therefore deemed it suspicious.

Darktrace / NETWORK’s detection of the unusual use of a new credential.
Figure 1: Darktrace / NETWORK’s detection of the unusual use of a new credential.

Likely Malicious File Download

Shortly after authentication with the privileged credential, Darktrace observed the device performing an SMB write to the C$ share, where a likely malicious executable file, ‘DeElevate64.exe’ was detected. While this is a legitimate Windows file, it can be abused by malicious actors for Dynamic-Link Library (DLL) sideloading, where malicious files are transferred onto other devices before executing malware. There have been external reports indicating that threat actors have utilized this technique when exploiting the Ivanti vulnerabilities [2].

Darktrace’s detection the SMB write of the likely malicious file ‘DeElevate64.exe’ on December 29, 2024.
Figure 2: Darktrace’s detection the SMB write of the likely malicious file ‘DeElevate64.exe’ on December 29, 2024.

Shortly after, a high volume of SMB login failures using the credential “svc_counteract-ext” was observed, suggesting potential brute forcing activity. The suspicious nature of this activity triggered an Enhanced Monitoring model alert that was escalated to Darktrace’s Security Operations Center (SOC) for further investigation and prompt notification, as the customer was subscribed to the Security Operations Support service.  Enhanced Monitoring are high-fidelity models detect activities that are more likely to be indicative of compromise

Suspicious Scanning and Internal Reconnaissance

Darktrace then went on to observe the device carrying out network scanning activity as well as anomalous ITaskScheduler activity. Threat actors can exploit the task scheduler to facilitate the initial or recurring execution of malicious code by a trusted system process, often with elevated permissions. The same device was also seen carrying out uncommon WMI activity.

Darktrace’s detection of a suspicious network scan from the compromised device.
Figure 3: Darktrace’s detection of a suspicious network scan from the compromised device.

Further information on the suspicious scanning activity retrieved by Cyber AI Analyst, including total number of connections and ports scanned.
Figure 4: Further information on the suspicious scanning activity retrieved by Cyber AI Analyst, including total number of connections and ports scanned.
Darktrace’s detection of a significant spike in WMI activity represented by DCE_RPC protocol request increases at the time, with little to no activity observed one week either side.
Figure 5: Darktrace’s detection of a significant spike in WMI activity represented by DCE_RPC protocol request increases at the time, with little to no activity observed one week either side.

Case 2: January 2025

Suspicious File Downloads

On January 13, 2025, Darktrace began to observe activity related to the exploitation of CVE-2025-0282  on the network of another customer, with one in particular device attempting to download likely malicious files.

Firstly, Darktrace observed the device making a GET request for the file “DeElevator64.dll” hosted on the IP 104.238.130[.]185. The device proceeded to download another file, this time “‘DeElevate64.exe”. from the same IP. This was followed by the download of “DeElevator64.dll”, similar to the case observed in December 2024. External reporting indicates that this DLL has been used by actors exploiting CVE-2025-0282 to sideload backdoor into infected systems [2]

Darktrace’s detection of the download of the suspicious file “DeElevator64.dll” on January 13, 2025.
Figure 6: Darktrace’s detection of the download of the suspicious file “DeElevator64.dll” on January 13, 2025.

Suspicious Internal Activity

Just like the previous case, on January 15, the same device was observed making numerous internal connections consistent with network scanning activity, as well as DCE-RPC requests.

Just a few minutes later, Darktrace again detected the use of a new administrative credential, observing the following details:

  • domain=REDACTED hostname=DESKTOP-1JIMIV3 auth_successful=T result=success ntlm_version=2 .

The hostname observed by Darktrace, “DESKTOP-1JIMIV3,” has also been identified by other external vendors and was associated with a remote computer name seen accessing compromised accounts [2].

Darktrace also observed the device performing an SMB write of an additional file, “to.bat,” which may have represented another malicious file loaded from the DLL files that the device had downloaded earlier. It is possible this represented the threat actor attempting to deploy a remote scheduled task.

Darktrace’s detection of SMB Write of the suspicious file “to.bat”.
Figure 7: Darktrace’s detection of SMB Write of the suspicious file “to.bat”.

Further investigation revealed that the device was likely a Veeam server, with its MAC address indicating it was a VMware device. It also appeared that the Veeam server was capturing activities referenced from the hostname DESKTOP-1JIMIV3. This may be analogous to the remote computer name reported by external researchers as accessing accounts [2]. However, this activity might also suggest that while the same threat actor and tools could be involved, they may be targeting a different vulnerability in this instance.

Autonomous Response

In this case, the customer had Darktrace’s Autonomous Response capability enabled on their network. As a result, Darktrace was able to contain the compromise and shut down any ongoing suspicious connectivity by blocking internal connections and enforcing a “pattern of life” on the affected device. This action allows a device to make its usual connections while blocking any that deviate from expected behavior. These mitigative actions by Darktrace ensured that the compromise was promptly halted, preventing any further damage to the customer’s environment.

Darktrace's Autonomous Response capability actively mitigating the suspicious internal connectivity.
Figure 8: Darktrace's Autonomous Response capability actively mitigating the suspicious internal connectivity.

Conclusion

If the previous blog in January 2024 was a stark reminder of the threat posed by malicious actors exploiting Internet-facing assets, the recent activities surrounding CVE-2025-0282 and CVE-2025-0283 emphasize this even further.

Based on the telemetry available to Darktrace, a wide range of malicious activities were identified, including the malicious use of administrative credentials, the download of suspicious files, and network scanning in the cases investigated .

These activities included the download of suspicious files such as “DeElevate64.exe” and “DeElevator64.dll” potentially used by attackers to sideload backdoors into infected systems. The suspicious hostname DESKTOP-1JIMIV3 was also observed and appears to be associated with a remote computer name seen accessing compromised accounts. These activities are far from exhaustive, and many more will undoubtedly be uncovered as threat actors evolve.

Fortunately, Darktrace was able to swiftly detect and respond to suspicious network activity linked to the latest Ivanti vulnerabilities, sometimes even before these vulnerabilities were publicly disclosed.

Credit to: Nahisha Nobregas, Senior Cyber Analyst, Emma Foulger, Principle Cyber Analyst, Ryan Trail, Analyst Content Lead and the Darktrace Threat Research Team

Appendices

Darktrace Model Detections

Case 1

·      Anomalous Connection / Unusual Admin SMB Session

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Internal / Unusual SMB Script Write

·      Anomalous File / Multiple EXE from Rare External Locations

·      Anomalous File / Script from Rare External Location

·      Compliance / SMB Drive Write

·      Device / Multiple Lateral Movement Model Alerts

·      Device / Network Range Scan

·      Device / Network Scan

·      Device / New or Uncommon WMI Activity

·      Device / RDP Scan

·      Device / Suspicious Network Scan Activity

·      Device / Suspicious SMB Scanning Activity

·      User / New Admin Credentials on Client

·      User / New Admin Credentials on Server 

Case 2

·      Anomalous Connection / Unusual Admin SMB Session

·      Anomalous Connection / Unusual Admin RDP Session

·      Compliance / SMB Drive Write

·      Device / Multiple Lateral Movement Model Alerts

·      Device / SMB Lateral Movement

·      Device / Possible SMB/NTLM Brute Force

·      Device / Suspicious SMB Scanning Activity

·      Device / Network Scan

·      Device / RDP Scan

·      Device / Large Number of Model Alerts

·      Device / Anomalous ITaskScheduler Activity

·      Device / Suspicious Network Scan Activity

·      Device / New or Uncommon WMI Activity

List of IoCs Possible IoCs:

·      DeElevator64.dll

·      deelevator64.dll

·      DeElevate64.exe

·      deelevator64.dll

·      deelevate64.exe

·      to.bat

Mid-high confidence IoCs:

-       104.238.130[.]185

-       http://104.238.130[.]185/DeElevate64.exe

-       http://104.238.130[.]185/DeElevator64.dll

-       DESKTOP-1JIMIV3

References:

1.     https://www.ivanti.com/blog/security-update-ivanti-connect-secure-policy-secure-and-neurons-for-zta-gateways

2.     https://unit42.paloaltonetworks.com/threat-brief-ivanti-cve-2025-0282-cve-2025-0283/

3.     https://www.proofpoint.com/uk/blog/identity-threat-defense/privilege-escalation-attack#:~:text=In%20this%20approach%2C%20attackers%20exploit,handing%20over%20their%20login%20credentials

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
Hugh Turnbull
Cyber Analyst

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September 22, 2026

Darktrace / SECURE AI: Extending Behavioral Security for the Age of Agentic AI

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AI is moving faster than the controls built to secure it

Over 80% of Darktrace’s customers now use generative AI services, and the average organization interacted with five different AI provides in August 20261. That level of adoption reflects how quickly AI has become embedded in day-to-day business operations. But as adoption accelerates, so do the opportunities for new forms of exposure that often operate outside traditional security controls. Prompts can expose sensitive information, an over-permissioned agent can turn a routine task into a serious security concern, and an employee using an unsanctioned AI tool to get work done can quietly introduce risk long before the security team is aware of its existence.

Securing AI isn’t a nice-to-have. It's an urgent, board-level requirement for any business that wants AI adoption to be an advantage rather than a liability.

Why traditional security controls fall short

The challenge isn’t a lack of AI security tools, it’s that most security solutions weren’t built for the way AI behaves.  

AI systems are adaptive and, by nature, unpredictable. A prompt can be entirely benign in one context and a serious risk in another. An agent's permissions can look reasonable in isolation and dangerous the moment they're combined with what that agent is actually doing. Rule-based controls, built to catch known signatures and static policy violations, simply aren't designed to catch this kind of subtlety. They might be able to tell you what happened, but they can’t tell you if it mattered.  

Effective AI security requires something different: a deep understanding of what normal looks like across every human, system, and agent in an environment, so the earliest signs of drift, misuse, or compromise stand out as they emerge.

Behavioral understanding is our foundation

Darktrace isn’t reinventing itself to secure AI. We’re extending what we already do. For over a decade, Darktrace has been built on a single premise – that every organization has its own unique, evolving way of operating, shaped by how its systems, users, and devices behave. Understanding that is the only reliable way to catch what rules and signatures miss. Our Adaptive AI™ continuously learns the distinct behaviors, relationships, and operational patterns of every enterprise it protects, building a Unique Behavioral Profile that no other vendor can replicate. It's how we've spent years interpreting ambiguity, uncovering subtle intent, and spotting drift before it becomes dangerous across networks, cloud, identities, email, OT, and endpoints. AI is a new domain, but behavioral security isn't a new discipline for us.

Introducing Darktrace / SECURE AI

Darktrace / SECURE AI helps organizations embrace AI innovation without losing control of how it is used. Delivered through the Darktrace Behavioral Defense Platform™, it provides visibility into AI activity across employees, agents, and AI systems, helping security teams understand how AI is being used throughout their business.

Darktrace then applies behavioral understanding to that activity, adding the context needed to distinguish routine usage from genuine risk. By understanding the relationships between users, agents, systems, and data, / SECURE AI helps organizations move beyond simply observing AI usage to understanding risk, governing behavior against policy, and enabling AI adoption with confidence. The result is greater oversight, stronger governance, and the ability to confidently accelerate AI innovation without creating unmanaged risk.

Securing your AI ecosystem

AI risk doesn't originate from a single source, and effective AI security can't focus on just one layer of the problem. Organizations need visibility, understanding, and governance across the entire AI ecosystem, from the prompts users submit and the agents they create, to the development environments where AI is built and the unsanctioned tools operating outside approved channels. Here's how Darktrace / SECURE AI helps organizations secure each of these areas.

Shadow AI management

Darktrace / SECURE AI helps security teams discover unsanctioned AI services and track usage trends over time using Darktrace telemetry and supported SASE integrations such as Microsoft Entra Global Secure Access. It also extends visibility to Model Context Protocol (MCP) usage, helping teams understand where AI tools and agents are connecting to external services. Through Darktrace platform integrations, organizations can block unsanctioned AI usage or quarantine affected devices when needed, helping them reduce exposure and guide users toward approved options.

AI prompt analysis

Prompts reveal what users are asking AI to do, what information they are sharing, and the outcomes they are trying to produce. Darktrace / SECURE AI provides visibility into prompts, sessions, and responses across supported platforms, including Microsoft Copilot, Copilot Studio, ChatGPT Enterprise, Claude, Salesforce, and AWS Bedrock2. Behavioral analysis detects activity such as attempted jailbreaks, sensitive data exposure, and potential indirect prompt injection, while risk scoring helps analysts prioritize the sessions that need attention. Policy Manager maps organizational AI policies against prompt activity and surfaces potential violations, helping teams govern AI use with direct evidence instead of relying on fragmented logs or inferred intent.

AI agent identities and actions

AI agents have their own permissions, roles, relationships, and access to systems and data. Darktrace / SECURE AI brings agent identities together with the users interacting with them, giving security teams visibility into access, sessions, and connections across supported environments. A real-time audit trail helps teams continuously evaluate whether an agent’s activity remains aligned with its intended purpose, so they can identify excessive access or behavioral drift before it becomes a security issue.

AI agent development risk management

Many AI risks are introduced during development, when agents are created, permissions are assigned, and connections to data sources are established. Darktrace / SECURE AI provides visibility across both low-code and high-code environments. In platforms such as AWS Bedrock, teams can examine AI architecture and the agent identities involved in development and deployment. In low-code environments, such as Copilot Studio, they can observe agents as they are created, monitor whether behavior stays aligned with their intended purpose, and connect prompt activity during development with behavior in production. This helps organizations address misconfigurations and excessive permissions before they reach production.

The promise of AI, secured

The question isn't whether to adopt AI, it's whether security teams can enable secure adoption that allows every business to benefit from AI’s potential.

Darktrace / SECURE AI is built to solve this problem – extending a decade of behavioral understanding to the newest, fastest-moving part of the enterprise, using the same principles which already protect people and hybrid infrastructure.

Darktrace / SECURE AI is generally available now. Discover the product, or get a demo today.

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[1] Based on aggregated Darktrace product telemetry across a fleet of ~8,200 observed deployments, measuring generative AI service usage across accounts monitored in August 2026. The 80%+ figure reflects the share of monitored accounts with any generative AI service usage during the same period.

[2] Microsoft, Copilot, ChatGPT, Claude, Salesforce, and Amazon Bedrock are trademarks of their respective owners.

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Brittany Woodsmall
Senior Product Marketing Manager, AI

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September 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

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Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

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Jason Lusted
AI Governance Advisor
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