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• Cybersecurity Glossary

What is Cybersecurity in Artificial Intelligence?

Cybersecurity in Artificial Intelligence is the use of AI techniques, principally Machine Learning and Deep Learning, to detect, investigate and respond to cyber threats by learning what normal behavior looks like and flagging deviations from it, rather than relying only on known signatures. By modeling how users, devices and network traffic usually behave, AI driven security spots novel and stealthy attacks, reduces false positives and accelerates response at a scale human analysts cannot match. As attackers themselves adopt AI to move faster, behavioral detection has shifted from a nice to have to the practical core of modern defense. It is the engine behind behavioral detection in Teldat be.Safe XDR, applied on the same routers and gateways that already carry the traffic.

1AI in cybersecurity definition2How AI detects threats?3Signature based vs behavioral detection4What AI brings to defense?5When attackers use AI too6The limits and risks7What to look for in a platform?8AI driven security with Teldat9Frequently asked questions – FAQ’s

AI in cybersecurity definition

Cybersecurity in artificial intelligence is the application of AI, above all Machine Learning and Deep Learning, to the work of defending networks: detecting threats, investigating alerts and triggering response. Its defining move is to shift from asking “does this match a known attack?” to asking “is this normal for this environment?”. The system learns a baseline of how users, devices and traffic ordinarily behave, then continuously measures live activity against that baseline and surfaces what does not fit.

This matters because the threat landscape has outgrown the signature. Attacks now mutate constantly, hide inside legitimate traffic and appear in forms no analyst has catalogued. A defense that can only recognize what it has already seen is always one step behind. By reasoning about behavior instead of fixed patterns, AI driven security can flag a “Zero day”, a stolen credential being misused or data quietly leaving the network, none of which carries a known signature.

The second driver is scale. A modern network generates far more events than any team can read, and genuine attacks hide in that flood. Machine Learning processes the whole stream, scores every event for how unusual it is, and correlates weak signals into a coherent picture, so the handful of events that truly matter rise to the top. AI in cybersecurity is, in essence, how defense keeps pace with both the novelty and the volume of modern attacks, and it is the foundation of behavioral detection in Teldat be.Safe XDR.

How AI detects threats?

AI driven detection is not a single trick but a pipeline that turns raw network and endpoint data into a short list of events worth a human’s attention. The stages below describe how that pipeline works, and what Teldat builds into be.Safe XDR.

1
Learning a baseline of normal behavior
The model is trained on large volumes of traffic and endpoint activity to learn how each user and device usually behaves: when they connect, what they talk to, how much data they move. This baseline is personal to the environment, so what counts as normal in one network is learned from that network rather than assumed.
2
Scoring anomalies in real time
Once a baseline exists, live activity is continuously compared against it and each event is scored for how unusual it is. A login at an odd hour from a new location, a device that suddenly scans the network, or an unexpected data flow all score high, even when no known signature matches them.
3
Correlating signals across the Estate
Individual anomalies are often harmless; attacks reveal themselves as patterns. AI correlates weak signals from many users, devices and sites into a single narrative, so a sequence that looks innocuous event by event is recognized as an intrusion when seen together across the whole estate.
4
Deep Learning for complex patterns
Deep Learning models capture relationships too subtle or too high dimensional for simpler methods, learning the shape of sophisticated attacks directly from data. This is what lets AI recognize threats that disguise themselves as ordinary traffic, where the giveaway is a complex pattern rather than any single suspicious action.
5
Prioritizing and triggering response
The final stage ranks confirmed threats by severity and, where policy allows, triggers an automated response such as isolating a device or blocking a flow. This turns detection into action fast enough to contain an attack before it spreads, with analysts focused on the incidents that genuinely need a human decision.

Signature based vs behavioral detection

Understanding what AI adds means contrasting it with the signature based approach that defined security for decades. The two are complementary rather than rivals, but they work in fundamentally different ways. The table below sets them side by side.

Dimension Signature based detection AI behavioral detection
Core question Does this match a known attack? Is this normal for this environment?
Known threats Fast and precise Detected as deviations, with context
Novel and zero day threats Missed until a signature exists Caught as anomalous behavior
Maintenance Constant signature updates required Model learns and adapts continuously
Encrypted or disguised traffic Hard to inspect by signature Detected via behavioral patterns
False positives Low for known, blind to unknown Reduced through context and correlation
Scale Limited by rule maintenance Processes vast data automatically

They work best together: signatures cheaply handle the large volume of known, catalogued threats, while behavioral AI covers the novel, the disguised and the slow. A platform that relies on signatures alone is blind to anything new; one that uses AI alone wastes effort re deriving the obvious. Modern detection, including Teldat be.Safe XDR, layers behavioral AI on top of established techniques so each does what it does best.

What AI brings to defense?

Beyond catching what signatures miss, AI changes the economics of running a security operation. These are the concrete gains that make AI driven detection worth adopting, and the outcomes Teldat targets with be.Safe XDR.

1
Because it reasons about behavior rather than known patterns, AI catches attacks that have never been seen before, the zero days and bespoke intrusions that signature tools cannot recognize until it is too late. This closes the window between a new attack appearing and a signature being written for it.
2
Fewer False positives
By understanding context and correlating many signals before raising an alert, AI separates genuinely suspicious activity from harmless quirks. That cuts the flood of low value alerts that exhausts security teams, so attention goes to real incidents instead of noise, the single biggest day to day pain in most operations.
3
Speed at Machine scale
AI reviews every event across the network in real time, something no human team can do, and surfaces threats in seconds rather than days. Faster detection means faster containment, which directly reduces the damage an attacker can do between breaking in and being stopped.
4
Detection inside Encrypted traffic
As more traffic is encrypted, inspecting payloads by signature becomes impractical. Behavioral AI sidesteps this by analyzing patterns, timing, volume and destinations, so it can flag malicious activity hidden in encrypted flows without needing to read the content itself.
5
Continuous Adaptation
A model that keeps learning adapts as the network changes and as attackers shift tactics, without waiting for a vendor to publish an update. Defense stays current with the environment it protects, rather than drifting out of date the moment the threat landscape moves on.
6
Relief for stretched Teams
By automating triage and surfacing only what matters, AI lets a small team cover a large estate. With specialist security staff scarce and expensive, multiplying the reach of the people you already have is often the difference between a workable operation and an overwhelmed one.

When attackers use AI too?

AI is not only a defensive tool; attackers are adopting it just as quickly, and that arms race is the strongest argument for AI on the defending side. These are the ways adversaries now use AI, and why behavioral defense is the practical answer.

1
More convincing Phishing
Generative AI produces fluent, personalized phishing and social engineering at scale, stripping away the spelling and tone clues that once gave such messages away. The volume and quality both rise, which means more people are fooled and more credentials are stolen.
2
Polymorphic Malware
AI helps generate endless variants of malware that change their signature with every iteration, defeating tools that rely on matching known samples. When the signature is different every time, only detection that looks at what the malware does, not what it looks like, can keep up.
3
Faster reconnaissance
Automated, AI assisted scanning probes defenses and finds weak points far faster than manual methods, compressing the time between a vulnerability appearing and being exploited. Defenders no longer have days to react; the window has shrunk to hours or less.
4
Why defensive AI is now essential
Human teams using manual, signature driven methods cannot match AI accelerated attacks. Defensive AI that detects by behavior is the practical counter to offensive AI that constantly changes its signatures, which is why behavioral detection has moved from optional to foundational in modern security.

The asymmetry is the point: offensive AI wins by changing its appearance faster than signatures can be written. Defensive AI wins by ignoring appearance and watching behavior, which does not change as easily, an attacker still has to log in, move, scan or exfiltrate. Teldat be.Safe XDR is built on exactly this principle, detecting the behavior of an attack rather than chasing its ever changing surface.

The limits and risks

AI is powerful but not magic, and treating it as a black box that solves security on its own is a mistake. A clear eyed view of its limits is part of using it well. These are the constraints any serious deployment has to manage.

1
Dependence on Data quality
A model is only as good as the data it learns from. Incomplete or biased training data produces blind spots and skewed baselines, so the quality of detection rests on having broad, representative visibility of the network it is meant to protect.
2
Adversarial Manipulation
Attackers can try to poison training data or slowly shift behavior so that malicious activity is gradually accepted as normal. Defenses have to account for the possibility that the model itself is a target, with safeguards against being quietly retrained by an adversary.
3
The need for Human oversight
AI should augment analysts, not replace their judgment. The most consequential decisions still need a human in the loop, and a system that acts autonomously without oversight risks both missed context and unintended disruption to legitimate activity.
4
Explainability and Trust
An alert is only actionable if a team can understand why it fired. Detection that cannot explain its reasoning is hard to trust and hard to defend in an audit, so transparency about what triggered a decision matters as much as the decision itself.
5
Data privacy and Sovereignty
AI driven security analyzes large amounts of potentially sensitive data, so where that data is processed and under whose jurisdiction matters. For European organizations, keeping the analysis and the data under European control is both a privacy obligation and a sovereignty consideration.

What to look for in a platform?

Many products now claim to use AI, and the term alone says little. These are the qualities that separate genuinely useful AI driven security from a marketing label, and the ones worth examining before trusting a platform with detection.

1
Behavioral detection, not just Signatures
The platform should genuinely model normal behavior and detect deviations, not simply rebrand a signature engine as AI. Ask how it handles a threat with no known signature; the answer reveals whether the AI is doing real work or decorating an older approach.
2
Measurable False positive reduction
Good AI lowers alert fatigue. Look for evidence that the platform correlates signals and uses context to suppress noise, because a detector that floods the team with low value alerts is worse than useless no matter how clever its underlying model.
3
Detection where the data Lives
Running AI detection on the same routers and gateways that carry the traffic means analysis happens where the data already is, without backhauling everything to a distant service. This keeps detection fast, scalable across many sites, and closer to the source of any anomaly.
4
Correlation across the whole Estate
Threats reveal themselves across sites, not within one. A platform that correlates signals from the entire network into a single view catches the distributed, multi stage attacks that any per site tool, looking only at its own slice, would miss entirely.
5
Human oversight and Explainability
The platform should explain why it raised an alert and keep a human in the loop for consequential actions. Transparency and oversight are what make AI detection trustworthy in practice and defensible in an audit, rather than an opaque verdict the team has to take on faith.
6
European jurisdiction over the Data
Since AI security inspects sensitive data, a European vendor that keeps analysis and data under European jurisdiction removes a class of privacy and sovereignty risk, and aligns naturally with regulation such as NIS2 and the data protection rules that public and private bodies must meet.

AI driven security with Teldat

Teldat applies Machine Learning and Deep Learning in be.Safe XDR to detect threats by behavior across the network, on the same routers and gateways that already provide connectivity. The platform learns what is normal, flags what is not, correlates signals across the estate and reduces false positives, all operated under European jurisdiction and aligned with European regulation. AI is not a bolt on here; it is the detection engine.

1
Behavioral detection in be.Safe XDR
be.Safe XDR uses Machine Learning and Deep Learning to model the normal behavior of users, devices and traffic, then flags deviations as potential threats. This catches novel and zero day attacks that carry no known signature, the threats that signature only tools cannot see.
2
False positive reduction by Context
By correlating many signals and weighing context before raising an alert, be.Safe XDR cuts the flood of low value alerts that overwhelms security teams. Analysts spend their time on genuine incidents rather than chasing noise, which is where AI delivers its most immediate operational value.
3
Detection at the Edge, on the router
Because detection runs on the Teldat routers and gateways that already carry the traffic, analysis happens where the data is, without backhauling everything to a distant service. This keeps AI detection fast and scalable across thousands of sites, close to the source of any anomaly.
4
Correlation across the whole Network
be.Safe XDR collects telemetry from across the estate and correlates it, so an anomaly at one node is seen in the context of the whole network rather than in isolation. This is what catches the distributed, multi stage attacks that a per site view would miss.
5
One platform with Connectivity and security
AI detection, firewall, segmentation and connectivity run on the same Teldat platform, so there is no separate appliance to deploy or integrate. For organizations managing many sites, consolidating intelligence and connectivity in one place lowers cost and operational burden alike.
6
As a European vendor, Teldat keeps AI analysis and the data it inspects under European jurisdiction, removing a class of privacy and sovereignty risk and aligning naturally with NIS2. For European organizations, defensive AI and regulatory compliance come from the same platform.

Why AI on the router is the right place: detection is only as good as its visibility, and the most complete view of behavior is at the network layer the traffic already crosses. Because Teldat runs Machine Learning and Deep Learning directly on the routers and gateways of be.Safe XDR, it sees the behavior of every user, device and flow, correlates it across the estate and responds at the edge, while keeping all of that analysis under European jurisdiction.

FAQ’s about AI in cybersecurity

❯ What is artificial intelligence in cybersecurity in simple terms?

It is the use of AI, mainly Machine Learning, to help defend networks by learning what normal activity looks like and then spotting anything that does not fit. Instead of matching threats against a fixed list of known attack signatures, the system builds a picture of how users, devices and traffic usually behave and raises an alert when something deviates. This lets it catch new and disguised attacks that signature based tools would miss, and do so across far more data than a human team could review.

❯ How does Machine Learning detect cyber threats?

Machine Learning models are trained on large volumes of network and endpoint data to learn the normal behavior of each user and device. Once a baseline exists, the model continuously compares live activity against it and scores how unusual each event is. A login at an odd hour from a new location, a device suddenly scanning the network, or data moving in an unexpected pattern all stand out as anomalies, even if no known signature matches, which is how Machine Learning detects threats that have never been seen before.

❯ What is the difference between signature based and behavioral detection?

Signature based detection matches activity against a database of known attack patterns; it is precise for known threats but blind to anything new. Behavioral detection, powered by AI, instead learns what is normal and flags deviations, so it can catch novel, zero day and slow moving attacks that have no signature yet. The two are complementary: signatures handle the known cheaply, while behavioral AI covers the unknown, which is why modern platforms combine both.

❯ Does AI reduce false positives in security?

Yes, well designed AI reduces false positives by understanding context rather than triggering on isolated rules. By learning each environment’s normal behavior and correlating many signals before raising an alert, AI driven detection separates genuinely suspicious activity from harmless anomalies. This cuts the flood of low value alerts that overwhelms security teams, so analysts spend time on real incidents instead of chasing noise, while keeping detection of true threats high.

❯ Can attackers use AI too?

Yes, attackers increasingly use AI to craft more convincing phishing, generate malware variants, and probe defenses faster, which raises the volume and sophistication of attacks. This is precisely why AI on the defensive side is no longer optional: human teams cannot keep pace with AI accelerated attacks using manual, signature driven methods alone. Defensive AI that detects by behavior is the practical counter to offensive AI that constantly changes its signatures.

❯ How does Teldat use AI in cybersecurity?

Teldat applies Machine Learning and Deep Learning in be.Safe XDR to detect threats by behavior across the network. The platform learns the normal behavior of users, devices and traffic, flags deviations as potential threats, correlates signals from across the estate and reduces false positives so analysts focus on real incidents. Because detection runs on the same Teldat routers and gateways that provide connectivity and is operated under European jurisdiction, AI driven protection is delivered at the edge and aligned with European regulation such as NIS2.

Detect threats by behavior with Teldat

be.Safe XDR applies Machine Learning and Deep Learning to spot novel attacks, reduce false positives and respond at the edge, on the same Teldat routers that carry your traffic and under European jurisdiction.