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March 18, 2020

5 Security Risks Companies Face Transitioning to Remote Work

Discover 5 security risks companies face with remote work employees. Protect against email scams, weakened security controls, errors, and insider threats.
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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.
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18
Mar 2020

As we all adjust to working remotely, security teams across the world are grappling with a very serious challenge. Almost overnight our companies have changed. Well established procedures are being rewritten, best practices quickly rethought, and policies stretched to breaking point.

Business transformation is always a security risk. New technology and working practices need new security measures; but normally this risk is managed carefully, and over time. COVID-19 has not afforded us that luxury. For some businesses the scale and speed of this change will be unprecedented. It is also very public; attackers are aware of the situation and already exploiting it. Below are some of the most serious threats that security teams will face over the coming weeks.

1. Email scams

Change brings novelty, and novelty brings opportunity for scammers. In the last 48 hours, internal security teams will have been racing to roll out essential remote working tools. Links to download new software, changes to how we authenticate services. When you do not know what to expect, employee training on spotting social engineering goes out the window. Both employees and IT departments should be wary of unexpected calls and requests:

“Hi, I’m calling from IT, can you please read out your 2FA code to me to confirm that you have been transitioned to the new Duo system?”

“Hi, I’ve forgotten my O365 password, can you please email a reset code to my personal Gmail?”

Such requests may be legitimate and may need to be resolved outside normal channels. The onus will be on individuals to be cautious, apply common sense and validate as appropriate.

There will also be ample opportunity for spear phishers to impersonate third-parties and clients:

“Hi John, I need to reschedule our meeting next week to be remote. Please see the link below for an invite to the Zoom call.”

These risks will be exacerbated by the simultaneous relaxing of security controls in order to facilitate the use of non-standard web conferencing software and the sharing of files by email. Attackers will have both the opportunity and the means.

2. Weakened security controls

The weakening of security controls goes far beyond relaxing firewall rules and email policy. Many existing layers of security will not apply to remote workers. Employees suddenly taking their work computer home with them will find themselves stripped of protection as they trade the office network for their home Wi-Fi. Without internet proxy, NAC, IDS and NGFW, client devices will now be sitting exposed on potentially unsecured networks amongst potentially compromised devices. Endpoint security will have to bear the full brunt of protection.

Internal network security may be compromised as well; employees might need access to resources previously only accessible on a wired network in one location. To make it reachable over VPN, internal segmentation might need to be flattened. This will open the door to malware spread and lateral movement. Client certificate authentication protecting web services might need to be turned off to enable BYOD working for employees that don’t have a company laptop.

These changes must be scrupulously logged, and dependencies understood. The extra weight will have to be carried elsewhere: perhaps host AV policies can be tightened to compensate for lack of network protection, perhaps employee devices can be reconfigured to use a secure external DNS provider instead of the on-prem DNS server.

3. Attacks on remote-working infrastructure

Beyond the weakening of existing controls, spinning up new infrastructure will bring fresh risks. In January we saw a spate of attacks on web-facing Citrix infrastructure. Companies will be rapidly deploying VPN gateways, transitioning to Sharepoint and expanding their internet-facing perimeter. This rapidly increased attack surface will need monitoring and protecting. Security teams should be on heightened alert for brute force and server-side attacks. DDoS protection will also become more important than ever; for many companies this will be the first time that a DDoS attack could cripple their business by preventing remote workers from accessing services over the internet. We should expect to see a sharp rise in both of these forms of attack immediately.

4. Errors and creative solutions

“Put it in an S3 bucket.”

“Let’s use join.me instead.”

“I’ll send it to you over WeTransfer.”

Both IT, and individual employees, will face blockers. There won’t be an authorized solution for their needs, and those needs may well be extremely urgent. At a time when businesses are extremely worried about their financial position and ability to operate, there will be pressure to throw caution to the wind and protect ‘business as usual’. This pressure may even come from the top. Security leadership must do the best they can to both push back against rash decisions and provide creative solutions.

Well-meaning employees will get creative, and responsibility will be delegated to team leaders to “do what it takes”. It may be impossible for security to police this centrally but monitoring vigilance will be required to spot risky behavior and non-compliance. This is easier said than done; the SOC will be asked to monitor for incidents in a sea of change. Existing use-cases and rules will not apply, and companies will need a more proactive and dynamic approach to detection and response.

5. Malicious insiders and malicious housemates

Unfortunately, there will be some within our companies that want to kick us while we are down. Sudden remote working is a godsend to malicious insiders. Data can now be easily taken from a company device over USB within the privacy of their own home. Security monitoring may be crippled or disabled entirely. This risk is harder to address. It may not be eliminable, but it can be balanced against the need for productivity and access to data.

We should also be wary of those around us. We all hope we can trust the people we live with. But from a company perspective, employee homes are zero-trust environments. Confidential conversations will now be conducted within range of eavesdroppers. Intellectual property will be visible on screens and monitors in living rooms around the world. This risk is greater for younger demographics likely to be house-sharing, but it remains for all workers; delivery personnel, visitors to the house – they could all potentially steal a company laptop from the kitchen room table. Education of employees in particular risk groups will be key.

Finding direction in a sea of digital change

All of the above changes and risks create a monitoring nightmare for SOCs. We are entering into a period of digital unknown, where change will be the new normal. Data flows and topology will change. New technology and services will be deployed. Logging formats will be different. The SIEM use-cases that took 12 months to develop will need to be scrapped overnight. For the next few weeks, business practice will shift rapidly.

Static defenses and rules will not be able to keep up, no matter how diligently and rapidly we rewrite them. How will you spot a malicious login attempt to O365 in your audit logs now that connections are coming from thousands of different locations around the world? Companies need to leverage technology that can allow them to continue to operate amidst uncertainty without choking productivity at this critical time. More critical still, containing those threats is of paramount importance – it won’t be feasible to entirely quarantine an infected machine if it cannot be re-imaged or replaced for days.

AI systems that can continuously evolve and adapt to change will provide the best chance of detecting misconfigurations, attacks, and risky behavior – when you don’t know what to look for, you need technology that is able to identify patterns and quantify risks for you. Autonomous Response technology can also surgically intervene to halt malicious activity when teams can’t be there to stop it, protecting devices and systems whilst allowing essential operations to continue unaffected.

Evolutions: Meeting the challenge head-on

Confronting these threats will not be easy. It will require a mixture of hard work, creativity, and new technology, alongside an openness to new ways of working and a willingness to embrace dynamic, proactive defense, instead of traditional rigid policies. However, placing trust in defensive systems to autonomously protect employees will be the single most effective way of maintaining resilience and security when our static defenses have failed us.

At Darktrace we are working hard to help our customers get even more value from their Cyber AI platform throughout this difficult time, and ease workloads of busy security teams. We know that with the right tools and technologies – from Autonomous Response and Cyber AI Analyst, through to the Darktrace Mobile App – these teams will be able to navigate these stormy waters. In this unprecedented period of uncertainty, the need for security that evolves in step with your changing digital business has never been greater.

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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.
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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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About the author
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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