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December 2, 2019

Containing Cyber Threats with Autonomous Response

Autonomous response technology can stop cyber threats in their tracks. Discover how these solutions enable rapid threat containment.
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
Max Heinemeyer
Global Field CISO
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02
Dec 2019
“The next phase in our journey toward autonomous security is Autonomous Response decision-making.”

Lawrence Pingree, Research Vice President, Gartner

We’ve talked extensively on this blog about Autonomous Response: the AI-powered technology that, according to Gartner, represents a paradigm shift in cyber defense. As the first such Autonomous Response tool, Darktrace Antigena has already thwarted countless cyber-attacks, from a spear phishing campaign against a major city to an IoT smart locker attack targeting a popular amusement park. Antigena’s surgical intervention afforded their security teams the time they needed to investigate — stopping the clock in seconds by containing just the malicious behavior.

For all its benefits, however, Autonomous Response does have one drawback: it can make for slightly anticlimactic blog posts. In place of captivating, step-by-step descriptions of malware spreading throughout the enterprise and inflicting irrevocable damage, Antigena case studies end a mere moment after they start, with the “patient zero” employee completely unaware of the compromise that could have been.

In this particular case, however, Antigena was deployed in Human Confirmation Mode — a starter mode wherein the AI’s actions must first be approved by the security team. Absent such approval, the result was both an in-depth look at a sophisticated ransomware attack, as well as a remarkable illustration of how Antigena reacted in real time to every stage of that attack’s lifecycle:

Initial download

Patient zero here was a device that Darktrace detected downloading an executable file from a server with which no other devices on the network had ever communicated. Downloads like this one regularly bypass conventional endpoint tools, since they cannot be programmed in advance to catch the full range of unpredictable future threats. By contrast, because Darktrace AI learned the typical behavior of the company’s unique users and devices while ‘on the job’, it easily determined the download to be anomalous.

Figure 1: Darktrace alerts on the 100% rare connection and subsequent download — as it occurs.

Had Antigena been in Active Mode at the time, this would have marked the end of the blog post. By blocking all connections to the associated IP and port, Antigena would have instantly stopped the download — without otherwise impacting the device at all.

Figure 2: Antigena, in Human Confirmation Mode, recommends that it block the suspicious activity.

Command and control

Following the download, Darktrace observed the device making an HTTP GET request to the same rare endpoint. The continuation of this suspicious activity precipitated an escalation in Antigena’s recommended response, which would now have blocked all outgoing traffic from the breached device to prevent any infection from spreading.

Darktrace then detected the device making yet more unusual external connections to endpoints that, in many cases, had self-signed SSL certificates. Such self-signed certificates do not require verification by a trusted authority and are therefore frequently utilized by cyber-criminals. As a consequence, the outgoing connections from our infected device are likely the installed malware communicating with its command and control infrastructure, as Darktrace flagged below:

Figure 3: Darktrace alerts on the suspicious SSL certificates.

Figure 4: Antigena recommends taking action to block the connections in question.

Internal reconnaissance

Beyond the unusual external activity observed from the breached device, it also began to deviate significantly from its typical pattern of internal behavior. Indeed, Darktrace detected the device making over 160,000 failed internal connections on two key ports: Remote Desktop Protocol port 3389 and SMB port 445. This activity — known as network scanning — provides crucial reconnaissance, giving the attacker insight into the network structure, the services available on each device, and any potential vulnerabilities. Ports 3389 and 445 are especially common targets.

Figure 5: Darktrace tracks this ransomware attack at every step, though the security team does not mount a response in time.

The unusual external connections to self-signed SSL certificates, combined with the highly anomalous internal connectivity from the device, would have caused Antigena to escalate further. Alas, the attack proceeds.

Darktrace detected no further anomalous activity from patient zero for the next four days — perhaps a mechanism to remain under the radar. Yet this period of dormancy concluded when, once again, the device connected to a rare domain with a self-signed SSL certificate, likely reaching out to its command and control infrastructure for additional instructions.

Lateral movement

A day later — in a sign that suggests the prior scanning was somewhat fruitful — the infected device performed a large amount of unusual SMB activity consistent with the malware attempting to move laterally across the network. Darktrace picked up on the breached device sending unusual outgoing SMB writes to the remote administration tool PsExec to a total of 38 destination devices, 28 of which it compromised with a malicious file.

Darktrace recognized this activity as highly anomalous for the particular device, as it doesn’t usually communicate with these destination devices in this manner. Antigena would therefore would have surgically blocked the remote administration behavior by first containing the patient zero device to its normal ‘pattern of life’, and then by escalating to blocking all outgoing connections from the device if lateral movement had continued. Antigena’s escalation can be seen below: the first action is taken at 08:03, the second, more severe action at 08:43.

Figure 6: Darktrace repeatedly alerts on the unusual SMB traffic with high confidence — thanks to its evolving understanding of the device’s typical ‘pattern of life’.
Figure 7: Antigena again recommends immediate intervention, this time to impede lateral movement.

Encryption

Darktrace observed the first sign of the ransomware’s ultimate objective — encrypting files — on a different device, which also performed a large volume of unusual SMB activity. After accessing a multitude of SMB shares that it hadn’t accessed previously, it systematically appended those files with the .locked extension. When all was said and done, this encryption activity was seen from no less than 40 internal devices.

In Active Mode, Antigena Ransomware Block would have fully quarantined the devices — a culmination of increasingly severe Antigena actions from the initial infection of patient zero, to the command and control communication, to the internal reconnaissance, to the lateral movement, and finally to the file encryption.

Figure 8: Antigena Ransomware Block was fully armed and prepared to fight back against the infection.

The case for boring blog posts

No other approach to cyber security is able to track ransomware so comprehensively throughout its lifecycle, as programming legacy tools to flag all remote administration behavior, for instance, would inundate security teams with thousands of false positive alerts. Thus, only Darktrace’s understanding ‘self’ for each infected device can shed light on such activities — in the rare cases when they are anomalous.

Figure 9: An overview of Darktrace’s myriad warnings throughout the five-day attack with each colored dot representing a high-confidence alert.

However, intriguing though it may be to track this lifecycle to conclusion, the technology to write far less intriguing blog posts already exists and is already proven. Autonomous Response will render this kind of threat story a relic of the past, and for organizations with sensitive data and critical intellectual property to safeguard, the days of boring security blogs cannot come soon enough.

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
Max Heinemeyer
Global Field CISO

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December 23, 2025

How to Secure AI in the Enterprise: A Practical Framework for Models, Data, and Agents

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Introduction: Why securing AI is now a security priority

AI adoption is at the forefront of the digital movement in businesses, outpacing the rate at which IT and security professionals can set up governance models and security parameters. Adopting Generative AI chatbots, autonomous agents, and AI-enabled SaaS tools promises efficiency and speed but also introduces new forms of risk that traditional security controls were never designed to manage. For many organizations, the first challenge is not whether AI should be secured, but what “securing AI” actually means in practice. Is it about protecting models? Governing data? Monitoring outputs? Or controlling how AI agents behave once deployed?  

While demand for adoption increases, securing AI use in the enterprise is still an abstract concept to many and operationalizing its use goes far beyond just having visibility. Practitioners need to also consider how AI is sourced, built, deployed, used, and governed across the enterprise.

The goal for security teams: Implement a clear, lifecycle-based AI security framework. This blog will demonstrate the variety of AI use cases that should be considered when developing this framework and how to frame this conversation to non-technical audiences.  

What does “securing AI” actually mean?

Securing AI is often framed as an extension of existing security disciplines. In practice, this assumption can cause confusion.

Traditional security functions are built around relatively stable boundaries. Application security focuses on code and logic. Cloud security governs infrastructure and identity. Data security protects sensitive information at rest and in motion. Identity security controls who can access systems and services. Each function has clear ownership, established tooling, and well-understood failure modes.

AI does not fit neatly into any of these categories. An AI system is simultaneously:

  • An application that executes logic
  • A data processor that ingests and generates sensitive information
  • A decision-making layer that influences or automates actions
  • A dynamic system that changes behavior over time

As a result, the security risks introduced by AI cuts across multiple domains at once. A single AI interaction can involve identity misuse, data exposure, application logic abuse, and supply chain risk all within the same workflow. This is where the traditional lines between security functions begin to blur.

For example, a malicious prompt submitted by an authorized user is not a classic identity breach, yet it can trigger data leakage or unauthorized actions. An AI agent calling an external service may appear as legitimate application behavior, even as it violates data sovereignty or compliance requirements. AI-generated code may pass standard development checks while introducing subtle vulnerabilities or compromised dependencies.

In each case, no single security team “owns” the risk outright.

This is why securing AI cannot be reduced to model safety, governance policies, or perimeter controls alone. It requires a shared security lens that spans development, operations, data handling, and user interaction. Securing AI means understanding not just whether systems are accessed securely, but whether they are being used, trained, and allowed to act in ways that align with business intent and risk tolerance.

At its core, securing AI is about restoring clarity in environments where accountability can quickly blur. It is about knowing where AI exists, how it behaves, what it is allowed to do, and how its decisions affect the wider enterprise. Without this clarity, AI becomes a force multiplier for both productivity and risk.

The five categories of AI risk in the enterprise

A practical way to approach AI security is to organize risk around how AI is used and where it operates. The framework below defines five categories of AI risk, each aligned to a distinct layer of the enterprise AI ecosystem  

How to Secure AI in the Enterprise:

  • Defending against misuse and emergent behaviors
  • Monitoring and controlling AI in operation
  • Protecting AI development and infrastructure
  • Securing the AI supply chain
  • Strengthening readiness and oversight

Together, these categories provide a structured lens for understanding how AI risk manifests and where security teams should focus their efforts.

1. Defending against misuse and emergent AI behaviors

Generative AI systems and agents can be manipulated in ways that bypass traditional controls. Even when access is authorized, AI can be misused, repurposed, or influenced through carefully crafted prompts and interactions.

Key risks include:

  • Malicious prompt injection designed to coerce unwanted actions
  • Unauthorized or unintended use cases that bypass guardrails
  • Exposure of sensitive data through prompt histories
  • Hallucinated or malicious outputs that influence human behavior

Unlike traditional applications, AI systems can produce harmful outcomes without being explicitly compromised. Securing this layer requires monitoring intent, not just access. Security teams need visibility into how AI systems are being prompted, how outputs are consumed, and whether usage aligns with approved business purposes

2. Monitoring and controlling AI in operation

Once deployed, AI agents operate at machine speed and scale. They can initiate actions, exchange data, and interact with other systems with little human oversight. This makes runtime visibility critical.

Operational AI risks include:

  • Agents using permissions in unintended ways
  • Uncontrolled outbound connections to external services or agents
  • Loss of forensic visibility into ephemeral AI components
  • Non-compliant data transmission across jurisdictions

Securing AI in operation requires real-time monitoring of agent behavior, centralized control points such as AI gateways, and the ability to capture agent state for investigation. Without these capabilities, security teams may be blind to how AI systems behave once live, particularly in cloud-native or regulated environments.

3. Protecting AI development and infrastructure

Many AI risks are introduced long before deployment. Development pipelines, infrastructure configurations, and architectural decisions all influence the security posture of AI systems.

Common risks include:

  • Misconfigured permissions and guardrails
  • Insecure or overly complex agent architectures
  • Infrastructure-as-Code introducing silent misconfigurations
  • Vulnerabilities in AI-generated code and dependencies

AI-generated code adds a new dimension of risk, as hallucinated packages or insecure logic may be harder to detect and debug than human-written code. Securing AI development means applying security controls early, including static analysis, architectural review, and continuous configuration monitoring throughout the build process.

4. Securing the AI supply chain

AI supply chains are often opaque. Models, datasets, dependencies, and services may come from third parties with varying levels of transparency and assurance.

Key supply chain risks include:

  • Shadow AI tools used outside approved controls
  • External AI agents granted internal access
  • Suppliers applying AI to enterprise data without disclosure
  • Compromised models, training data, or dependencies

Securing the AI supply chain requires discovering where AI is used, validating the provenance and licensing of models and data, and assessing how suppliers process and protect enterprise information. Without this visibility, organizations risk data leakage, regulatory exposure, and downstream compromise through trusted integrations.

5. Strengthening readiness and oversight

Even with strong technical controls, AI security fails without governance, testing, and trained teams. AI introduces new incident scenarios that many security teams are not yet prepared to handle.

Oversight risks include:

  • Lack of meaningful AI risk reporting
  • Untested AI systems in production
  • Security teams untrained in AI-specific threats

Organizations need AI-aware reporting, red and purple team exercises that include AI systems, and ongoing training to build operational readiness. These capabilities ensure AI risks are understood, tested, and continuously improved, rather than discovered during a live incident.

Reframing AI security for the boardroom

AI security is not just a technical issue. It is a trust, accountability, and resilience issue. Boards want assurance that AI-driven decisions are reliable, explainable, and protected from tampering.

Effective communication with leadership focuses on:

  • Trust: confidence in data integrity, model behavior, and outputs
  • Accountability: clear ownership across teams and suppliers
  • Resilience: the ability to operate, audit, and adapt under attack or regulation

Mapping AI security efforts to recognized frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework helps demonstrate maturity and aligns AI security with broader governance objectives.

Conclusion: Securing AI is a lifecycle challenge

The same characteristics that make AI transformative also make it difficult to secure. AI systems blur traditional boundaries between software, users, and decision-making, expanding the attack surface in subtle but significant ways.

Securing AI requires restoring clarity. Knowing where AI exists, how it behaves, who controls it, and how it is governed. A framework-based approach allows organizations to innovate with AI while maintaining trust, accountability, and control.

The journey to secure AI is ongoing, but it begins with understanding the risks across the full AI lifecycle and building security practices that evolve alongside the technology.

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About the author
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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