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December 4, 2024

Phishing Attacks Surge Over 600% in the Buildup to Black Friday

Black Friday and Cyber Monday are prime targets for cyber-attacks, as consumer spending rises and threat actors flock to take advantage. Darktrace analysis reveals a surge in retail cyber scams at the opening of the peak 2024 shopping period, and the top brands that scammers love to impersonate. Plus, don’t forget to check out our top tips for holiday-proofing your SOC before you clock off for the festive season.
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
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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04
Dec 2024

Defenders are accustomed now to an uptick in cyber-attacks around the holiday period. The festive shopping season creates ideal conditions for cybercriminals. Consumers are inundated with time-sensitive deals, while retailers handle record-breaking transaction volumes at speed. This environment makes it harder than ever to identify suspicious activity.

An investigation conducted by Darktrace’s global analyst team revealed that Christmas-themed phishing attacks leapt 327%1 around the world and Black Friday and Cyber Monday themed phishing attacks soared to 692% last week compared to the beginning of November2 (4th - 9th November), as threat actors seek to take advantage of the busy holiday shopping period.

The United States retail sector saw the most marked increase in threat actors crafting convincing emails purporting to be from well-known brands, mimicking promotional emails. Attacks designed to look like they came from major brands including Walmart – which was easily the most mimicked US brand – Macy’s, Target, Old Navy, and Best Buy3 increased by more than 2000% during peak shopping periods.

Darktrace analysis also highlighted a redistribution of scammers’ resources to take advantage of the festive shopping season, moving from targeting businesses to consumers. The impersonation of major consumer brands, dominated by Amazon and PayPal4, increased by 92% globally between analyzed periods, while the spoofing of workplace-focused brands, like Adobe, Zoom and LinkedIn, decreased by 9%.

Major retail brands invest heavily in safeguarding themselves and their customers from scams and cyberattacks, particularly during the holiday season. However, phishing and website spoofing occur outside the retailers' legitimate infrastructure and security controls, making it difficult to catch and prevent every instance due to their sheer volume. While advancements like AI are helping security teams narrow the gap, brand impersonation remains a persistent challenge.

Multiple attack methods exploit trust during holiday rush

Darktrace’s findings demonstrate some of the most common brand spoofing strategies used by attackers during the holiday season:

Domain spoofing, which sees attackers create near perfect replicas of retail websites, complete with lookalike domain names and branding, to trick consumers into handing over personal and payment details.  

Brand spoofing, where attackers send a phishing email designed to look like a favorite retailer, enticing their target to click a link for a discount, when in fact the link downloads malware to their device.  

Safelink smuggling, which involves an attacker intentionally getting their malicious payload rewritten by a security solution’s Safelink capability to then propagate the rewritten URL to others. This not only evades detection but also undermines trust in email security tools. Darktrace observed over 300,000 cases of Safelinks being included in unexpected and suspicious contexts over a period of 3 months.

Multi-stage attacks which combine these tactics into a single attack: brand spoofing emails lead unsuspecting shoppers directly to domain spoofed websites that harvest login or payment details, creating a seamless deception that hands personal and financial data directly to attackers. This coordinated approach exploits the chaos of holiday sales, when shoppers are primed to expect high volumes of retail emails and website traffic promoting significant savings.

A spike in cyber-criminal activity which extends beyond email

While email often serves as the front door to an organization and the initial avenue of attack, Darktrace frequently observes a surge in cyber-attacks during public holidays5. These “off-peak” attacks exploit common organizational practices and human vulnerabilities with greater ease.

When staff numbers are reduced, and employees mentally and physically disconnect from work, the speed of detection and response has the potential to slow. This creates opportunities for threat actors to infiltrate undetected. Without real-time autonomous systems in place, such attacks can have a far more severe impact on an organization’s ability to respond and recover effectively.

Ransomware is among the most common threats targeting organizations after hours. In 76% of cases, the encryption process begins during off-hours or on weekends6. For instance, Darktrace identified a ransomware attack launched in the early hours of Christmas Day on a client’s network, taking advantage of the period when most employees were offline.

Festive cheer: giving your SOC team the break they deserve

Staff burnout is increasingly top of mind, with 74% of cybersecurity leaders reporting that they’ve had employees resign due to stress7. And the numbers stack up – almost 60% of security analysts report feeling burnt out, and many are choosing to leave their jobs and even security altogether.8

At a human level, the holiday season should be a time of relaxation and merriment rather than anxiety. For SOC leaders, giving teams time to prioritize recharging during the holidays is crucial for sustaining long-term resilience and productivity, balanced with the importance of maintaining rigorous defenses with a reduced workforce.  

So… how can cybersecurity leaders ensure peace of mind during the holidays?

Step 1: Cover yourself from every angle. It’s no longer enough for your email solution to only catch known threats. Security leaders need to invest in multi-layered email defenses that can combat novel and advanced attacks – such as the multi-stage brand personation attacks that lead shoppers to domain-spoofed websites.  

Darktrace / EMAIL – the fastest growing email security solution – has been proven to detect up to 56% more threats than other email solutions.9  It is uniquely capable of catching novel attacks on the first encounter, rather than waiting the 13 days it takes for other solutions to take action10 – by which time your decorations might be coming down, along with your business.

Step 2: Avoid an overwhelming deluge of alerts raining (or snowing) down on your L1 SOC analysts. Lining up people to manage the grunt work over the holidays is an easy pattern to fall into, but consider technology that can automate that initial triage. For example, Darktrace’s Cyber AI Analyst automatically investigates every alert detected by Darktrace’s core real-time detection engine. It does an additional layer of AI analysis – establishing whether an alert is unusual but benign, or part of a more serious security incident. Rather than looking at hundreds of alerts, your team is presented with just a handful of overall incidents. They can use that new free time to do more strategic work, or take some much-needed time off.

Step 3: Make sure someone – or something – is keeping guard in those super off-peak hours. Enter Autonomous Response. Because it knows what normal looks like for your business it can take action to stop and contain only the unusual and threatening activity. Even if it doesn’t eliminate the threat entirely, it can buy your security team time and space, allowing them to enjoy their holiday in peace.

With Black Friday over and the festive shopping period looming, businesses should act now to protect their brand and ensure they have the cybersecurity measures are in place to enjoy the gift of a stress-free holiday season.  

Interested in how AI-driven email security can protect your organization? Check out the product hub to learn more. Or watch the demo video to see Darktrace / EMAIL in action.

References

[1] Based on analysis of 626 customer deployments and attempted phishing emails mentioning Christmas that were detected by Darktrace / EMAIL.

[2] Emails in the analysis mentioning ‘Black Friday’ or ‘Cyber Monday’.

[3] Walmart, Target, Best Buy, Macy's, Old Navy, 1800-Flowers

[4] Amazon, eBay, Netflix, Alibaba, Paypal, Apple

[5] In 2021, Darktrace observed a 70% average increase in attempted ransomware attacks in November and December compared to January and February. (Darktrace Press Release, 2021)

[6] https://www.zdnet.com/article/most-ransomware-attacks-take-place-during-the-night-or-the-weekend

[7] https://www.scworld.com/perspective/ciso-stress-levels-are-out-of-control

[8] https://www.informationweek.com/cyber-resilience/the-psychology-of-cybersecurity-burnout

[9] 56% of malicious phishing emails detected and analyzed across Darktrace / EMAIL customer deployments from December 2023 – July 2024 passed through all existing security layers. (Darktrace Half Year Report 2024)

[10] 13 days mean average of phishing payloads active in the wild between the response of Darktrace / EMAIL compared to the earliest of 16 independent feeds submitted by other email security technologies. (Darktrace Press Release, 2023)

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
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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July 7, 2026

Securing AI: Analysis of the Complete Security Stack with Governance and Controls

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Why traditional cybersecurity approaches are not enough for AI

AI adoption outpaces most security programs’ ability to adapt.  That gap is now one of the most consequential sources of cyber risk facing enterprises. As organizations embed generative and agentic AI into development workflows, business operations, and security tooling itself, the question is no longer whether AI will introduce risk. The question is whether organizations understand where that risk actually lives and how to manage it operationally.  

Two recent pieces of guidance underscore this shift:

  1. The upcoming Cybersecurity Framework Profile for AI from NIST
  1. The Five Eyes government guidance on the careful adoption of agentic AI services

Taken together, they point to a critical conclusion. AI security cannot be reduced to model hardening or prompt filtering. It requires a defense in depth strategy that treats AI as both a new attack surface and a force multiplier for defense, while accounting for how AI fundamentally changes scale, speed, and autonomy.  

Recent threat research suggests that today's cyber risk is driven less by initial compromise and more by an adversary's ability to blend into normal operations over time. AI systems create the same exposure in a new form: more autonomy, more scale, and more opportunities for risky behavior to blend into normal operations.

How NIST defines the three core pillars of AI security

The NIST profile organizes AI risk across three inseparable focus areas that span all cybersecurity functions, Secure, Defend and Thwart. These areas are not sequential. They exist simultaneously and must be addressed together.

Secure

This treats AI as an attack surface. It includes models, prompts, agents, pipelines, training and inference data, retrieval augmented generation corpora, and the AI supply chain itself. AI systems are opaque, probabilistic, and non-deterministic by design. Some vulnerabilities are inherent in how models are trained or how data is sourced. Traditional patching does not fully mitigate these risks. This is also where many enterprises are weakest today and, critically, where many security programs stop.  

Defend

This is AI as a defensive force multiplier. AI can improve detection speed, scale, correlation, and response, but only if the right models are used and operationalized correctly. Machine-speed behavior-based detection, response and containment becomes critical in defending non-deterministic systems. Accuracy, explainability, governance, testing, validation, and integration into SOC workflows matter as much as capability. Without those controls, hallucination risk, over automation, and misplaced trust become security risks themselves.  

Thwart

This treats AI as an adversarial accelerant. Threat actors are already using AI to generate targeted social engineering attacks, deepfakes, malware, and autonomous attack agents. Asymmetric warfare is highlighting faster vulnerability discovery and exploitation with a lag on patch development, testing and deployment.  

How this looks in practice

Darktrace researchers observed scaled, automated exploitation of the React2Shell vulnerability within days of disclosure. A vulnerable cloud asset was exploited in under 120 seconds of being deployed. Darktrace research team observed an AI/LLM-generated malware sample used in exploitation activity tied to React2Shell. The significance isn't novelty. It is that AI lowers the barrier to producing usable offensive tooling and compresses the time between experimentation and deployment.  

Tactics are getting more and more creative in order to string together steps of an attack kill chain. This creates a dependency on behavior-based detection, autonomous investigation, autonomous containment, training, resilience investment, and recovery planning across the entire enterprise.

Why agentic AI fundamentally changes enterprise cyber risk

The Five Eyes guidance on agentic AI highlights material changes to the cyber risk profile of an organization. Unlike generative AI systems that produce content for human consumption, agentic AI systems reason, plan, and act autonomously across tools, data, and environments. That autonomy, combined with access to real systems, amplifies the impact of traditional cyber failures and introduces new system level risks that are difficult to predict, observe, and contain.  

Risk in agentic systems does not live in the model alone. It emerges from interactions between models, prompts, memory, tools, APIs, identities, privileges, inter-agent trust relationships, and human assumptions baked into design. Vulnerabilities are often introduced through data, connectors, natural language interfaces, protocols, and drift by design.

In supply-chain incidents, attackers did not need sophisticated exploits to scale impact. They abused trusted systems built for automation and implicit access. Agentic AI inherits that model. Once a system can act across tools, data, and workflows, compromise propagates through trust relationships that were never designed for machine autonomy.

The major agentic AI risk classes include the following:  

  • The identity control for non-human identities or autonomous agents makes it difficult to mitigate over-permissioning, limiting access, scope, and duration, as well as access hygiene
  • Agents are frequently over permissioned
  • Compromised tools inherit agent authority
  • Static secrets enable impersonation
  • Implicit trust between agents enables lateral movement

Design and configuration risks compound this, including privileges evaluated once at startup, poor segmentation, unvetted third party tools, reused authorization decisions outside their original context, and guardrail limitations.  

Behavioral risk  

Agents can optimize for goals in unsafe ways, misinterpret ambiguous intent, chain actions into unintended sequences, change behavior during evaluation, and exhibit deceptive or sycophantic responses.  

Structural risk  

Structural risk follows from agentic systems that are tightly coupled, multicomponent ecosystems. Failures can propagate across agents. Hallucinations cascade downstream. Resource exhaustion becomes systemic. Tool misuse enables indirect prompt injection and command execution. Rogue agents can poison peer agents through trust relationships.  

Accountability

Accountability becomes unclear as autonomy increases. Autonomous agents assume human identity permissions, and humans should have clear ownership of these agents, but they don’t, and this model is flawed. Decision paths are opaque and non-deterministic. Logs are fragmented and difficult to interpret. Reproducing an incident will be impossible without explicit design for observability and forensics. An agent compromise is functionally an insider threat, often with better access and fewer behavioral constraints than a human.  

What does defense in depth look like for AI?

Agentic AI runs on software, networks, identities, and data. It must be governed using the same foundational principles that have proven resilient under uncertainty, including secure by design, defense in depth, zero trust, least privilege, continuous monitoring, behavior-based advanced threat detection and containment, and incident response and recovery.

Core components to a Defense in depth Strategy for Securing the use of AI:

  • Strong, precise identity control plane to include an identity per agent (cryptographic, non‑shared)
    • Privilege monitoring and just‑in‑time access
  • Data Governance
  • Secure‑by‑default configurations
    • Security Posture Management  
    • Zero Trust principles  
  • Strong guardrails, deny‑by‑default policies, and isolation
  • Explicit instruction hierarchies and controlled context
  • Behavioral-based detection across entire enterprise to include inputs, tools, and outputs as well as AI used on the endpoint, across the network, cloud, SaaS, email, and OT
    • Runtime anomaly detection and goal‑drift detection
    • Autonomous containment to mitigate risk and minimize damage
  • Hard boundaries on autonomy and delegation
  • Testing, Evaluation, Validation and Verification  
    • Determine when autonomous action and when human in the loop
    • Adversarial training and agent‑specific testing
    • Simulation, red teaming, and chaos testing
  • Kill‑switches, rollback, and containment mechanisms
    • Forensics data captures, interpretability, autonomous containment, and remediation/recovery plans  

Until standards, tooling, and assurance methods mature, organizations should assume agentic AI systems will behave unexpectedly and design deployments around resilience, behavior-based detection, reversibility, and containment, not efficiency.

How security leaders should prepare for enterprise AI adoption

AI security is not model security alone. Data, pipelines, identities, and agents are first class assets. Many AI attacks succeed through standard cyber failures amplified by AI. Identity, data, and supply chain risk dominate. Behavior-based detection and response are critical, not optional. Logging, provenance, versioning, and forensics data capture of detections are mandatory because you cannot investigate or recover from AI incidents without them.  

Risk will often be visible in behavior before it is clearly defined in policy or guidance. The same pattern has been seen in pre-CVE disclosure detection, where abnormal activity appears before the industry has named or described the vulnerability. AI systems introduce that uncertainty by design.

Security leaders should prioritize controls before AI is fully deployed, avoid generic AI security checklists, integrate AI risk into existing cyber programs, and mitigate the risk of non-deterministic technology with continuous oversight, monitoring, behavior analytics, anomaly detection, autonomous investigation, and autonomous containment.

Visibility has a different connotation with AI. Previously, audit logging worked for software/people, but with Generative AI-based systems, interpretability and explainability is difficult to understand, you cannot "undo" what has been done, or see the logic or control a chain of events. This is why behavioral-based detections and containment becomes critical.  

What capabilities should every AI security program include?

If an organization asked “what must be in place before scaling AI?”:

  1. AI Risk board and approval workflow
  1. IAM + PAM for all AI services and agents
  1. AI asset inventory
  1. Prompt/output DLP with sanctioned AI access – This is not just pre- and post- filters, but behavior-based detections of semantic interface as well as behavior-based analysis of output with associated risk context.  
  1. Shadow AI identification
  1. Secure MLOps – This is an entire paper itself
  1. Runtime guardrails and tool restrictions
    • Including AI Gateway/SASE/Zero trust/
  1. Runtime security with behavior-based detections
    • Complete visibility, monitoring, behavior analytics, anomaly detection, risk/intent/context evaluation of anomalies, autonomous investigation and autonomous containment of all AI assets across endpoint, network, SaaS, SASE, cloud, OT, email, and messaging platforms
  1. Secure data pipelines and data governance
  1. SOC workflow changes from malicious classification workflows to behavior-based detection workflows
  1. Remediation plans for AI-related incidents  

Layered Governance and Security Stack for Securing AI  

The following outline considers governance and security tools that should be considered, well-integrated, deployed, tested, operationalized and embedded within security workflows. These tools and controls map to NIST’s CMF for AI.  

These considerations do not need to be implemented in order. Runtime Detect and Respond will help mitigate risk while Governance, Visibility, and Identity mature.

Category Tooling Controls
Governance & Visibility
  • AI asset inventory / AI CMDB
  • Shadow AI discovery
  • SaaS discovery
  • AI usage on non-endpoint managed systems via network or cloud telemetry
  • MCP server/client usage via protocols
  • Browser telemetry
  • Gateway or SASE telemetry
  • Establish a risk board to set up controls
  • Mandatory registration of AI systems
  • Owner, data classification, intended use, and risk tier
  • Supplier disclosure requirements
  • Risk mitigation plan for AI adoption, innovation, or development
Identity, Access & Agent Control

Non-human autonomous agents should not have the full permissions associated with a human user.

  • IAM with workload identities
  • PAM for AI service accounts
  • Secrets management with short-lived tokens
  • Zero Trust principles
  • Identity, permission, and token hygiene
  • Unique identities per model, agent, and pipeline
  • Least privilege for tools, data, and APIs
  • Explicit approval for autonomous actions
Data Security & Privacy
  • Data classification and labeling
  • Enterprise DLP across endpoint, email, network, cloud, and SaaS
  • Forensics data capture after risky detections
  • Prompt-level DLP through behavior-based semantic analysis with risk and intent context
  • Input/interface analysis for risky data requests
  • Output analysis for sensitive data
  • Data integrity evaluation
  • Retention and redaction policies for prompts and responses
Secure MLOps / LLMOps
  • Secure CI/CD with AI-specific gates
  • Model registries with approval workflows
  • Dependency, container, and artifact scanning
  • SBOM/AIBOM generation
  • IaC security scanning
  • Security posture management
  • Misconfiguration identification
  • Hardening recommendations
  • Signed models and prompts
  • Versioned datasets, configurations, logging, and controls
  • Securing data pipelines
  • Controlled promotion
  • Quality assurance
  • Adversarial testing
Runtime Security

Securing runtime goes beyond guardrails and model firewalls to include behavior-based detections, response, and containment.

  • Detection, monitoring, and SOC integration
  • Centralized visibility into prompts, outputs, and tool calls
  • AI-specific detections
  • Behavior-based detection for AI usage patterns
  • Model drift and behavior monitoring
  • Autonomous containment
  • Behavior-based detection of model inputs and outputs
  • Prompt injection detection
  • Model manipulation, including jailbreaking, poisoning, and related attacks
  • Sensitive data access attempts
  • Behavior-based detection across low-code agents, high-code agents, MCP clients and servers, endpoint, network, cloud, email, SaaS, SASE, IoT, and OT
  • Policy enforcement between users, models, tools, agents, SaaS models/tools, and MCP servers/clients
  • Risk, intent, and context evaluation for detections and response actions
Response & Recovery
  • Autonomous containment
  • AI-assisted playbooks
  • Forensics data capture for AI-related events
  • Model rollback mechanisms
  • Backup and restore for models and datasets
  • Kill switch for agents
  • Autonomous response to agents performing risky behaviors
  • Model and dataset rollback
  • Remediation plans
  • Tabletop exercises
  • Supplier coordination plans
  • Post-incident AI performance validation

AI security requires continuous visibility and behavioral detection

AI changes how fast systems move, how decisions are made, and how risk propagates. It does not change the fundamentals of security. Organizations that succeed will be the ones that apply those fundamentals rigorously, assume failure, and build systems that can detect, contain, and recover when AI behaves in ways they did not anticipate. Security is not what AI is allowed to do. It is whether the organization can understand, trust, and control what AI actually does in practice.  

Take this guidance to understand different initiatives that organizations should be considering. Securing AI is the most critical component to AI safety. As organizations invest more in AI adoption, they should be investing in security in order to mitigate the risk of AI adoption. Organizations should be evaluating their governance and security stack to include well-integrated tools that are deployed, tested, operationalized and embedded within security workflows. While organizations mature in governance, visibility and identity access management, they should be investing in behavior-based detection and autonomous containment to mitigate AI risk.  

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July 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

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Static AI guardrails are inherently limited

As organizations adopt generative AI, many still assume that the right set of guardrails will be enough. The problem is you can’t anticipate every way these systems might be misused, abused or attacked. What NIST has done is put a mathematical foundation under that intuition.

In recent research building on Gödel’s incompleteness theorems, which showed that any system built on a fixed set of rules will always have gaps, NIST demonstrates that there is no finite set of guardrails that can be universally robust against adversarial prompts. In plain terms, if your defense is based on a fixed set of rules, there will always be inputs that bypass them. Not because the rules are badly written, but because the problem space is bigger than static rules can ever cover.

This is not new in cybersecurity - detection rules have always had to live with this trade-off. What is different with GenAI is the scale and shape of that problem. These systems are built on human language, and human language is not bounded. It is fluid, contextual and deliberately ambiguous. The number of ways intent can be hidden is effectively limitless. You are not defending against a defined protocol or a fixed exploit chain. You are defending against the entire expressive capacity of people.

So attempting to create a complete set of rules is the wrong starting point. It assumes the problem can be deterministically described. NIST’s work shows that it cannot. Organizations still need a way to manage AI risk, but the traditional approach of defining allowed and disallowed patterns is always going to lag behind what is actually happening. The same input can be benign in one context and risky in another, and static rules struggle to capture that distinction.

The question then is what fills that gap?

AI security must shift from rules to behavior

What's required is a shift in what you are trying to understand. Rules try to describe what should and shouldn't happen. Behavior shows you what is happening. Or to put it another way, if inputs are unbounded and adversaries adapt, the only stable signal is behavior.

In a GenAI context, that means analyzing how an AI model is being used, how prompts evolve over time, how outputs are shaped, and where AI agent interactions start to drift from what is expected. It means moving from static definitions of bad to a more dynamic understanding of intent.

Instead of trying to predict every bad prompt, you focus on identifying when behavior starts to move outside expected norms. Instead of asking whether a single input matches a rule, you ask whether the overall pattern of activity makes sense for the system and how it’s being used.

Guardrails remain important but they are only one layer

This does not eliminate the need for guardrails. They still play a role. But they will never address the entire problem space and are simply one part of your defense in depth approach.

NIST’s proof is useful because it makes this explicit. It removes the assumption that with enough effort, a complete rule set is achievable. It isn’t.

Once you accept that, the shift becomes unavoidable. This is no longer a problem of writing better rules, but of understanding behavior in a space where the possible inputs are effectively unbounded.

For security leaders, that changes the nature of the problem. It is less about defining what should be allowed, and more about recognizing when something is no longer consistent with expected behavior.

That does not remove the need for guardrails, but it does change their role. They set boundaries, but they do not define understanding. The gap between the two is where risk now sits.

In the end, this is what “can’t be solved with rules” really means. Rules will always leave gaps, and those gaps are not theoretical. They show up in how systems actually behave Not what we expect them to do, or what we intended them to do, but what they are doing in practice. That is where the signal is, and increasingly, that is where the security problem sits.

References:

https://www.nist.gov/news-events/news/2026/06/nist-mathematical-proof-supports-transition-continuous-monitor-and-update

https://ieeexplore.ieee.org/document/11475847

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician
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