The conversation about AI in cybersecurity has recently centered on capabilities like vulnerability discovery, exploit generation, and automated proof-of-concept development. It’s easy to see why: These tasks produce binary outcomes; a vulnerability either exists or it doesn’t. That makes them useful for measuring model progress and demonstrating increasingly sophisticated cybersecurity capabilities.

Vulnerability discovery matters to defenders. According to the Verizon 2026 Data Breach Investigations Report, vulnerability exploitation is now the most common initial access vector, accounting for 31% of breaches in the reporting dataset. This is a meaningful and growing share of the problem and a strong reason to continue advancing AI capabilities in this area.

But it also means 69% of breaches begin through other paths. Credential abuse, phishing, social engineering, trusted relationships, and other forms of access remain central to the adversary playbook. A comprehensive evaluation framework should therefore measure not only whether AI can discover and exploit vulnerabilities, but also whether it can help defenders detect identity abuse, investigate suspicious activity, engineer effective detections, hunt for adversaries, and respond across the broader attack lifecycle.

We believe effective AI for defense must be evaluated against the operational reality of security teams: the range of techniques adversaries use to gain initial access, the work required across the kill chain, and defenders’ most time-consuming tasks. Here, we explore some of these use cases.

Detecting Adversary Behavior After Initial Access

Once an adversary is inside, the defender’s work becomes more complex. Security teams must detect and triage suspicious activity across massive alert volumes, balancing signal and noise. Speed here determines whether the adversary is contained in minutes or operates on a network for a longer period of time.

When suspicious activity is found, the focus shifts to investigation, which requires significant effort and expertise. Analysts must reconstruct events across endpoints, identities, cloud environments, and other systems to determine what happened and how far the adversary has progressed. This is where attackers gain time: The longer an investigation takes, the greater the opportunity to move laterally, escalate privileges, and achieve their objectives.

Detection engineering powers these investigations by translating adversary behavior into durable detections, yet it remains a specialized and often under-resourced discipline. Effective defense also requires proactive threat hunting to find adversaries operating below existing detection thresholds. This depends on skilled analysts and accurate models of real-world behavior.

None of these disciplines produce clean binary outcomes or are easily benchmarked. However, they all depend on grounded, accurate models of real adversary behavior that public benchmarks lack.

Evaluating AI Against Adversary Tradecraft

Proactive defense requires knowing exactly how adversaries operate: the tools they use, the sequence of actions they take, and the ways their activity appears in telemetry. Adversary emulation translates that knowledge into realistic activity, making it foundational to detection engineering, threat hunting, and security validation.

The critical factor here is being grounded in reality. AI red teaming based on hypothetical scenarios or synthetic data has limited value if it does not reflect the techniques, artifacts, and operational patterns observed in real intrusions.

A key question for AI is whether it can reproduce adversary behavior with enough fidelity to test detection coverage, expose engineering gaps, and improve defensive readiness. For most defenders, that is a harder and more consequential test of AI capability than vulnerability discovery alone.

The post Expanding AI Benchmarks in Cybersecurity Beyond Vulnerability Discovery appeared first on MASSIVE News.

The Saudi, Pakistan, Turkiye defence pact faces mixed reactions, with some Iranian officials doubting its security guarantees. Iran and Oman agree on a Hormuz framework, while fighting in Yemen escalates between government forces and Houthis.
Al Jazeera’s @Mohammadfff_ reports.

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The post War on Iran: Phase II: Day 28 appeared first on MASSIVE News.

Resident doctors across Maharashtra have launched an indefinite agitation, suspending routine OPD services at government and civic hospitals while continuing emergency care. The protest is against the Maharashtra government’s move to grant Maharashtra Medical Council (MMC) registration to BHMS practitioners who have completed the Certificate Course in Modern Pharmacology (CCMP).

Speaking to Free Press Journal (FPJ), resident doctors said their protest is against mixopathy and crosspathy, arguing that patient safety could be compromised in the long run if different systems of medicine are allowed to overlap without a robust scientific and regulatory framework. They stressed that homeopathy and modern medicine are distinct disciplines requiring different training and maintained that practitioners should be registered under their respective councils.

Doctors also expressed concern that the policy could dilute modern medical education and professional standards. At the same time, they clarified that their agitation is not against homeopathy as a system of medicine, but against what they describe as cross-practice without adequate safeguards.

In this FPJ ground report, resident doctors explain why they have gone on strike, what mixopathy and crosspathy mean, which hospital services are affected, and how the protest could impact patients across Maharashtra.

#Maharashtra #DoctorsStrike #ResidentDoctors #BHMS #MMC #Mixopathy #Crosspathy #MedicalNews #Healthcare #HospitalStrike #OPD

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The post Maharashtra Doctors Speak Out Against Crosspathy | OPDs Suspended Across Hospitals appeared first on MASSIVE News.