Importance Of Security Measures

Your Smarthome Is Talking—But Who’s Listening? Smart home devices offer incredible convenience, allowing us to control lights, locks, appliances, and cameras remotely. However, each of these Internet of Things (IoT) devices also represents a potential vulnerability in your home’s digital perimeter. Many users install these gadgets without changing default settings, leaving them wide open to cyber intrusions. Threat actors have exploited poorly secured devices to spy on households, manipulate smart locks, or gain access to broader home networks. To avoid these risks, we must treat IoT devices with the same caution as computers or smartphones. That means using strong, unique passwords, enabling two-factor authentication where possible, and consistently updating firmware. Network segmentation is another smart move—placing IoT devices on a separate Wi-Fi network to prevent them from interacting with sensitive systems like work laptops or home servers. Finally, it’s important to evaluate the necessity of each new connected device. Ask yourself if the benefits truly outweigh the privacy risks. Not every gadget needs to be online, and sometimes convenience can come at the cost of security. In an age where even your thermostat or baby monitor can be exploited, a little common sense goes a long way in protecting your privacy and peace of mind. #cybersecurity #IoT #smarthomes #securitycameras #babymonitors #webcams #smartappliances

When AI Meets Security: The Blind Spot We Can't Afford Working in this field has revealed a troubling reality: our security practices aren't evolving as fast as our AI capabilities. Many organizations still treat AI security as an extension of traditional cybersecurity—it's not. AI security must protect dynamic, evolving systems that continuously learn and make decisions. This fundamental difference changes everything about our approach. What's particularly concerning is how vulnerable the model development pipeline remains. A single compromised credential can lead to subtle manipulations in training data that produce models which appear functional but contain hidden weaknesses or backdoors. The most effective security strategies I've seen share these characteristics: • They treat model architecture and training pipelines as critical infrastructure deserving specialized protection • They implement adversarial testing regimes that actively try to manipulate model outputs • They maintain comprehensive monitoring of both inputs and inference patterns to detect anomalies The uncomfortable reality is that securing AI systems requires expertise that bridges two traditionally separate domains. Few professionals truly understand both the intricacies of modern machine learning architectures and advanced cybersecurity principles. This security gap represents perhaps the greatest unaddressed risk in enterprise AI deployment today. Has anyone found effective ways to bridge this knowledge gap in their organizations? What training or collaborative approaches have worked?

If you interview at Google for a Security Engineer role, you might talk about Chronicle or GCP SecOps. If you interview at Meta for a Security Engineer role, you might mention Secret Manager or open-source Bug Bounty platforms. If you interview at Microsoft for a Security Engineer role, you might discuss Azure Security Center or Defender. But after 5 years in the field, I can tell you with certainty, in every security interview, you will absolutely use “threat modeling.” Threat modeling is the backbone of security engineering, no matter the company, tech stack, or problem. If you know how to identify risks, map out attack surfaces, prioritise vulnerabilities, and design mitigations, you already understand 70% of what matters in security. Don’t get distracted by every new shiny tool; vuln scanners, SIEMs, and frameworks keep evolving. But threat modeling is forever. The methods might change, STRIDE, PASTA, DREAD, or your own framework, but the fundamentals stay the same: – Asset identification and value – Entry points and trust boundaries – Likelihood and impact of threats – Mitigations, controls, and detection Master how threats move through your system, and you’ll see how every other security tool connects. Learn threat modeling deeply. You’ll thank yourself later. Follow saed ‎for more & subscribe to the newsletter: https://lnkd.in/eD7hgbnk I am now on Instagram: instagram.com/saedctl say hello 👋, DMs are open

Identity has become the new control plane for cybersecurity, and it’s evolving fast. Our team believes the future Identity platform will control all of the following pillars. At Software Analyst Cyber Research, we see the next decade of identity security organized around two parallel worlds: Human Identities and Non-Human Identities (NHIs) connected, but solved distinctly. The easy trap is to believe in one platform that truly solves all pillars, but the more you dive into the details, the more you realize (at least for the next 2-3 years), each of these domains needs specialized solutions. However, whatever solutions are created, every human identity must be linked to a machine/NHI, and vice versa. The industry’s next transformation will converge these domains across five foundational pillars: 1️⃣ IAM (Identity and Access Management): the connective tissue of authentication, SSO, IDP and federation. We've mostly this issue for humans, but NHIs require their own specialized for verifying an agent. 2️⃣ Visibility - ISPM (Identity Security Posture Mgmt). We need a connective tissue that sees ALL identities and gives you enough context. This thrives around visibility, drift detection, and continuous posture assurance for identity configurations. Everyone needs a graph-based architecture to tie in identity across SaaS, cloud and AD. 3️⃣ IGA (Identity Governance and Administration): the brain for lifecycle orchestration and compliance, increasingly AI-assisted for access reviews and automating many of the compliance-heavy parts of this area. Increasingly, we need to cover the lifecycle of agents, including access controls. Non-Human Identity IAM & Governance, built to manage service accounts, machine users, API keys, tokens, and agentic workloads that now outnumber humans in the enterprise. 4️⃣ PAM (Privileged Access Management): The guardrail for elevated entitlements and just-in-time access will be more important. Most of this issue has been solved on-prem, but we need more for cloud workloads and increasingly for agents. 5️⃣ ITDR (Identity Threat Detection & Response): Runtime is the next battlefield. No amount of posture will save you. Detection and Response against lateral movement or identity-based attacks is the nervous system that detects and responds to credential misuse and identity-based attacks. And beneath it all: ➡️ The future identity stack will be hybrid, ie, bridging human and non-human, governance and runtime across cloud and application layers. This is all powered by an Identity Infrastructure spanning platforms. Identity is no longer a feature. It’s the fabric that ties together every modern security decision, most especially Data & AI security in the modern stack.

Most third-party risk programs are optimized for the front door. We assess vendors when they come in. We review their security controls. We make sure procurement and legal get what they need. Then we move on. But vendors don’t stay static. Their product changes. Their architecture evolves. Internal usage expands. What we thought we were approving at onboarding often looks very different a few months in. We talk about lifecycle risk, but most of our visibility is still front-loaded. I care less about a clean intake form and more about whether the vendor still meets expectations once they're fully embedded in our environment. That’s where the risk lives, and that’s where it’s easiest to miss. If we’re not set up to track risk as it evolves, we’re not really managing it. We’re just betting that our initial assumptions will hold. What are you building to shift that? Genuinely curious how others are tackling vendor risk after onboarding, especially in SaaS-heavy, fast-moving orgs.

97% of orgs faced AI breaches in 2025 had zero access controls in place. Not weak; Not outdated controls. Zero [Source: IBM] Meanwhile, 35% of real-world AI security incidents came from simple prompts some causing $100K+ in losses without a single line of code [Source: Adversa] The gap between AI deployment speed and security implementation is only widening. Hence I am sharing 10 security checkpoints every AI agent needs before touching production systems: ✅ Output Validation → Middleware that verifies decisions against rules before execution. Traffic lights for AI actions. ✅ Access Control → Least privilege enforcement. Role-based permissions that limit what agents can touch. ✅ Credential Safety → Secrets management that keeps API keys away from prompts and logs. Store them like vault keys, not sticky notes. The other 7 checks are in the carousel including rate limiting that prevents runaway loops and human approval for high-stakes decisions 👇 Most teams rush deployment. Security becomes an afterthought until something breaks. Tell me your story: what security measure has prevented a disaster in your AI system? Follow me, Bhavishya Pandit, for practical AI production insights from the trenches 🔥 #ai #security #agents

The Navy just gave away its crown jewels. Not by accident…by memo. The Department of the Navy recently published its updated Priority Technology Areas (PTAs) — the blueprint for where they’re putting their investment, their focus, and their bets for the future. The list includes: - AI / Autonomy - Quantum - Transport / Connectivity - C5ISR / Naval Space - Cyber Operations / Zero Trust In other words: the exact areas hostile nation-states are trying to steal from. These aren’t just buzzwords they’re bullseyes. And if you work in these sectors (or touch them through vendors, R&D, or joint ventures), you don’t just need to secure your network. You need to secure your people. Because threat actors don’t just hack networks. They charm interns. They blackmail contractors. They recruit your engineer on LinkedIn. Human risk isn’t hypothetical. It’s the quiet insider leaking schematics. It’s production delays caused by sabotage. It’s the employee who doesn’t even know they’re being used. If you touch any part of these tech priorities, your people are targets — not just employees. This is why Human Risk Management can’t be an afterthought. Especially when IP theft, insider recruitment, and sabotage-by-trust are the playbook. So ask yourself: ✔️ Have your people been trained to recognize social engineering, elicitation, or suspicious contact? ✔️ Does your leadership know what a human risk assessment actually looks for? ✔️ Do you have a protocol for early signs of insider targeting? Because the next leak won’t come from a firewall... It’ll come from a badge swipe. And the Navy’s memo just handed adversaries the map. #HumanRisk #InsiderThreat #NationalSecurity #DON #PriorityTechnologyAreas #Cybersecurity #IPTheft #InsiderSabotage #AI #Quantum #C5ISR #ZeroTrust

Why did NVIDIA, the darling of the AI market, drop 2.5% today? The Biden administration dropped the mic (and some weighty export controls) on AI chips and models—arguably the most aggressive attempt yet to regulate the flow of transformational tech. Let’s break it down: 🧩 A Three-Tier System of Access ⏩ 🥇 Top Tier: AI flows freely for 19 nations (G7 + allies like Japan, South Korea, and Taiwan). 🥈 Middle Tier: Most of the world faces caps but can negotiate for more chips by aligning with US policy interests 🥉 Bottom Tier: China and Russia? Completely locked out—no chips, no dice, no exceptions. 🔐 Locks on AI’s Crown Jewels ⏩ Firms must keep 75% of their AI computing power in the U.S. or allied nations, with no more than 7% in any other country. Data center operators like Microsoft and Google will need accreditation to trade AI tech freely, tightly aligning with U.S. security goals. 🤖 New AI Model Parameters ⏩ For the first time, restrictions extend to the very DNA of AI: model weights. Overseas data centers must implement strict safeguards to protect this intellectual property. Officially, it’s about national security: keeping AI away from adversaries like China and Russia. But unofficially? It’s about locking in dominance. It’s a strategic move to control the future of AI innovation and adoption. Pushback is already fierce. Nvidia has called the rules “misguided,” warning that global buyers will pivot to non-U.S. suppliers. Restricting friendly nations like Israel, Mexico, and Switzerland could also strain diplomatic ties. And let’s not forget the unintended consequence: Balkanization of the AI ecosystem. Countries and companies excluded from the U.S.-led framework may double down on domestic R&D or turn to less-restricted alternatives (hello, China). That could erode America’s soft power over time. This is the tech Cold War. Chips are the new oil. Code is the new currency. If these controls stick, the big question is whether they will cement U.S. dominance—or just fuel the competition.

All risk is enterprise risk. Cybersecurity Risk Management (CSRM) must be part of Enterprise Risk Management (ERM). Many companies think managing cyber risks is: ╳ Just an IT problem. ╳ Isolated from other risks. ╳ A low-priority task. But in reality, it is: ☑ A key part of the entire risk strategy. Here are the key steps to integrate cybersecurity risk into enterprise risk management: 1. Unified Risk Management ↳ Integrating CSRM into ERM helps handle all enterprise risks effectively. 2. Top-Level Involvement ↳ Top management must be involved in managing cyber risks along with other risks. 3. Contextual Consideration ↳ Cyber risks should be considered in the context of the enterprise's mission, financial, reputational, and technical risks. 4. Aligned Risk Appetite ↳ Align risk appetite and tolerance between enterprise management levels and cybersecurity systems. 5. Holistic Approach ↳ Adopt a holistic approach to identify, prioritize, and treat risks across the organization. 6. Common Risk Language ↳ Establish a common language around risk that permeates all levels of the organization. 7. Continuous Improvement ↳ Monitor, evaluate, and adjust risk management strategies continuously. 8. Clear Governance ↳ Ensure clear governance structures to support proactive risk management. 9. Digital Dependency ↳ Understand how cybersecurity risks affect business continuity, customer trust, and regulatory compliance. 10. Strategic Enabler ↳ Prioritize risk management as both a strategic business enabler and a protective measure. 11. Risk Register ↳ Use a unified risk register to consolidate and communicate risks effectively. 12. Organizational Culture ↳ Foster a culture that values risk management as important for achieving strategic goals. Integrating cybersecurity risk into enterprise risk management isn't just a technical task. It's a strategic necessity. 💬 Leave a comment — how does your company handle cyber risk? ➕ Follow Andrey Gubarev for more posts like this

Indian healthcare sees 8,614 attacks per week, making it one of the most attacked sectors. I’ve been in cybersecurity for over 30+ years. But I’ve never seen hospitals being targeted at this scale. Healthcare was once considered a “low priority” target for threat actors. That’s changed. Today, hospitals run on data. Patient records, insurance logs, prescription systems, lab reports, everything is on the computer system right now. It’s no longer just paper files and stethoscopes. It’s full-stack digital infrastructure. And attackers know that better than most CISOs. In late 2024, 7.2 TB of patient data was stolen from the leading healthcare insurance company, Star Health, impacting over 31 million people. It had policy documents, medical histories, tax IDs, lab reports and every single detail or a patient. All public, via Telegram chatbots and leaked web portals. When Reuters tested, they downloaded over 1,500 sample files across claims and medical documents. The reason healthcare is now the softest target? Because the cost of downtime is too high. And the cost of compliance is too low. You can’t afford to shut down hospital systems during an attack. And the penalties for poor security practices? Still far too lenient. That’s the dangerous equation attackers exploit. At Seqrite, we’ve seen a 3x jump in targeted attempts on healthcare setups over the past 18 months alone. And most of them weren’t even zero-days or complex APTs. From basic phishing emails to compromised vendor credentials and public-facing misconfigurations. The same attack playbooks, just aimed where it hurts most. This isn’t a product problem. It’s a mindset problem. If healthcare institutions treat cybersecurity like an IT purchase instead of critical infrastructure protection, these numbers will keep rising. India doesn’t just need better protection tools. We need frameworks, visibility, and accountability, especially for sectors that protect human lives. Have you seen the inside of a healthcare setup's security posture? Was it better or worse than you expected? Seqrite #CyberSecurity #HealthcareSecurity #DataProtection #Ransomware #Infosec #DigitalIndia #DataPrivacy #CyberAwareness #HealthcareIndustry